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• Mengenal Complex Problem Solving, Kompetensi yang Paling Dibutuhkan di Era Disrupsi
• 12.430 Views
• Selasa, 7 Agustus 2018

World Economic Forum pada akhir 2015 memberikan gambaran 10 keterampilan yang paling dibutuhkan sesuai perkembangan teknologi dan disrupsi di banyak bidang hingga tahun 2020. Prediksi ini bersifat global termasuk berlaku juga untuk Indonesia.

Kompetensi pertama yang paling dibutuhkan adalah Complex Problem Solving dan menyusul 9 kompetensi lainnya, seperti dalam grafik. Laduni sudah mencoba menjelaskan beberapa penjelasan tentang Problem Solving yang memiliki keterhubungan dengan Complex Problem Solving.

Kali ini, Laduni menyampaikan rangkuman wawancara Prof. Don Sadana tentang kompetensi Complex Problem Solving dengan Rumah MSDM beberapa waktu lalu.

Complex Problem Solving sangat erat berhubungan dengan situasi permasalahan yang tidak terstruktur atau biasa disebut juga sebagai Messy Situation.

Complex Problem Solving (CPS) adalah paradigma baru dalam menyelesaikan masalah atau permasalahan. Dalam hal ini masalah dimaksudkan sebagai problem, sedangkan permasalahan adalah problematics . Masalah biasanya dapat didefinisikan dengan jelas dan terukur, sedangkan permasalahan bersifat susah didefinisikan dan diukur. Jadi, kadang kita sering kurang jelas, menentukan penyebutan istilah, apakah istilah ‘masalah’ ataukah ‘permasalahan’ terkait sesuatu hal yang sedang kita alami.

Masalah umumnya bersifat kuantitatif yang dapat diukur dan

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Problem Solving: Arti, Manfaat, Proses, dan Contohnya di Dunia Kerja

Setiap orang pastinya tidak ingin mengalami masalah, termasuk di dunia kerja. Namun, masalah terkadang diperlukan untuk pengembangan diri. Karena itu, kamu harus tahu seluk-beluk bagaimana cara menyelesaikan masalah dengan tepat atau disebut dengan problem solving.

Bisa dibilang,  problem solving skill  adalah salah satu kemampuan   terpenting yang wajib dimiliki oleh setiap karyawan, apapun posisi dan bidang mereka. Temuan dari  National Association of College and Employers (NACE)  pun memperkuat kebutuhan tersebut. Sebab, lebih dari 60% perusahaan mengutamakan kandidat yang memiliki kemampuan  creative problem solving . 

Lantas, apa itu  problem solving dan apa saja manfaat spesifik yang bisa kamu dapatkan dengan menguasainya? Yuk ,  kita pelajari di sini, lengkap dengan cara meningkatkan kemampuan tersebut!

Apa Itu Problem Solving?

Tujuan dan manfaat problem solving, proses problem solving, metode-metode problem solving, tips mengasah kemampuan problem solving, contoh problem solving di dunia kerja.

• Pertanyaan Seputar Problem Solving ⁠

Pada dasarnya,  problem solving  artinya kemampuan untuk mencari solusi terbaik dari sebuah hambatan atau masalah yang sedang dihadapi.  Problem solving  adalah proses yang memerlukan sejumlah  soft   skill . 

Sesuai dengan pengertian  problem solving , kemampuan tersebut mencakup bagaimana kamu bisa mengidentifikasi permasalahan, menganalisis informasi yang terkait, berpikir kreatif, dan pengambilan keputusan dengan mempertimbangkan berbagai faktor.  ⁠

Mengingat tujuan  problem solving  adalah untuk mengatasi hambatan dalam hidup, termasuk dunia kerja, menguasai kemampuan ini tentunya akan sangat menguntungkan kamu. 

Manfaat  problem solving  yang pertama adalah kamu bisa mengasah kreativitas serta kepercayaan dirimu. Saat kamu berhasil menyelesaikan masalah yang membuatmu gelisah, kamu akan berpikir bahwa kamu punya kemampuan yang memadai untuk mengatasi rintangan berat lainnya.

Selain itu, ketika kamu harus mengatasi sebuah hambatan, tentunya kamu akan dituntut untuk memutar otak. Inilah yang membuatmu bisa berpikir secara out of the box dan menemukan solusi unik.

Tapi, manfaat dari  creative problem solving tidak hanya akan terasa untuk dirimu sendiri, lho! Justru, karena kamu harus mengambil keputusan yang mempertimbangkan kesejahteraan pihak terkait lainnya, kamu bisa mengasah kemampuan komunikasi dan empati. 

Dengan kemampuan  problem solving , kamu pun juga dapat menjaga hubungan harmonis dengan orang lain. Pada akhirnya, kamu akan merasa lebih puas saat bekerja dan bisa menekan risiko  burnout . Apalagi, mengingat  burnout  bisa menyebabkan banyak masalah kesehatan menurut  WebMD . ⁠

Lalu, bagaimana  problem solving  digunakan untuk membantu memecahkan masalah? Berikut langkah-langkah problem solving yang bisa kamu ikuti:

Memetakan masalah 

Logikanya, kamu tidak bisa memecahkan masalah kalau kamu sendiri tidak tahu apa yang menghambatmu. Maka dari itu, identifikasi masalah menjadi bagian paling penting sekaligus mendasar dalam langkah-langkah  problem solving . 

Di sini, kamu perlu mengetahui apa saja yang ingin kamu atasi secara detail. Misalnya, mungkin kamu ingin bisa datang ke kantor lebih pagi agar tidak terlambat. 

Mengidentifikasi akar penyebab masalah 

Kalau kamu sudah memahami masalah apa saja yang ingin diselesaikan, sekarang kamu perlu mengetahui penyebabnya. Tapi, terkadang sebuah masalah bisa saja terjadi karena lebih dari satu faktor.

Nah, supaya lebih efektif, kamu perlu mengetahui akar terdalam yang memengaruhi semua faktor tersebut.

Misalnya,contoh  problem solving  dalam kehidupan sehari-hari, kamu mengetahui bahwa penyebab utama kamu sering terlambat ke kantor adalah karena terlambat bangun dan terjebak kemacetan di jalan.    

Menetapkan urutan prioritas permasalahan 

Setelah mengetahui apa saja penyebab utama dari masalah yang kamu hadapi, sekarang saatnya kamu membuat daftar prioritas. Daftar prioritas mencakup apa saja aspek yang harus kamu utamakan terlebih dahulu, dan mana yang bisa kamu kesampingkan untuk nanti. 

Masih menggunakan contoh  problem solving  dalam kehidupan sehari-hari agar tiba di kantor tepat waktu, kamu bisa berfokus pada faktor internal dulu.  

Misalnya, karena kamu tidak bisa mengendalikan arus lalu lintas, setidaknya kamu bisa bangun dan berangkat lebih pagi dari biasanya. Nah, agar kamu tidak mengantuk saat bangun lebih pagi, kamu juga harus beristirahat lebih awal. 

Menyusun alternatif solusi 

Terkadang, bisa saja solusi yang kamu pikirkan di awal tidak dapat diterapkan secara efektif karena keadaan tertentu. Jadi, jangan lupa membuat rencana B atau alternatif dari proses pemecahan masalah tersebut. 

Dalam konteks praktik  problem solving  agar tidak terlambat ke kantor, kamu dapat mencari solusi lain seandainya kamu tetap kesulitan bangun jauh lebih pagi dari biasanya. Contohnya, berunding dengan atasan untuk membuat jadwal  kerja  remote   pada hari-hari tertentu atau beralih ke moda transportasi lain. 

Menjalankan solusi dan melakukan evaluasi 

Jika kamu sudah menemukan masalah yang ingin diatasi, penyebabnya, dan berbagai cara untuk mengatasinya, sekarang saatnya untuk menerapkan solusi tersebut. 

Supaya kamu tidak mudah kewalahan, cobalah menerapkan satu solusi pada suatu waktu terlebih dulu, alih-alih semuanya secara bersamaan. Dengan pendekatan problem solving secara bertahap, kamu akan lebih mudah mencari solusi terbaik yang paling efektif dan apa saja yang harus ditingkatkan dari sana. 

Kemudian, terapkan hasil pembelajaran yang kamu dapatkan dari proses evaluasi tersebut dalam keseharianmu agar tidak mengulangi kesalahan yang sama. ⁠

Dalam dunia profesional, ada banyak teknik problem solving yang bisa kamu gunakan untuk meningkatkan kualitas produk, mencapai target KPI, atau bahkan  meningkatkan hubungan dengan rekan kerja.

Berikut 8 model  problem solving  yang sering dijumpai, yaitu: 

World Cafe 

Cara kerja dari teknik  problem solving  ini sebenarnya tidak berbeda jauh dengan  focus group discussion . Sebab, kamu hanya perlu mengumpulkan setidaknya 12 orang untuk dibagi menjadi 4-5 kelompok kecil per meja. 

Setelah memilih pemimpin diskusi untuk setiap meja, semua orang akan mengetahui pertanyaan apa saja yang perlu dijawab dalam sesi diskusi. 

Kemudian, setiap kelompok akan punya waktu 20 menit untuk berdiskusi sebelum harus pindah ke meja lainnya dan memberi tahu apa yang sudah mereka dapat kepada pemimpin diskusi dari kelompok baru. 

Karena model  problem solving  World Cafe sangat menuntut partisipasi aktif dari setiap anggotanya, metode ini juga bisa menjadi ajang yang baik untuk memperdalam hubungan antar pegawai.

Problem Definition Process 

Selain World Cafe, kamu juga bisa menggunakan Problem Definition Process yang cukup sederhana. Sesuai namanya, metode  problem solving  ini melibatkan kemampuan mengidentifikasi masalah dan menjabarkan proses yang diperlukan untuk mengatasinya. 

Namun, ada satu tambahan yang membuat teknik Problem Definition Process cukup unik. Jadi, setelah memetakan kebutuhan yang ingin dipenuhi, kamu juga perlu menjustifikasi alasan dari keperluan tersebut 

Setelah memetakan urgensi dari masalah yang ditentukan, barulah kamu bisa memahami penyebabnya secara menyeluruh, membuat kalimat singkat dan sederhana yang merangkum esensi masalah tersebut, serta langkah-langkah mengatasinya. 

Six Thinking Hats 

Ingin pendekatan  problem solving  yang lebih menyeluruh dan bisa dilakukan sendirian maupun secara berkelompok? Kamu dapat mencoba teknik Six Thinking Hats yang diciptakan oleh Edward de Bono, seorang dokter, psikolog, dan filsuf dari Malta. 

Sejalan dengan arti namanya, metode  problem solving  ini mengharuskan kamu dan anggotamu menjalani enam peran yang diwakilkan oleh berbagai warna “topi”, yaitu:

• Topi biru/kondektur: menetapkan agenda dan merangkum diskusi agar tidak melenceng; 
• Topi hijau/kreatif: mengeksplorasi berbagai ide dari setiap sudut pandang;
• Topi merah/emosi: mengungkapkan perasaan terdalam tanpa perlu justifikasi logika;
• Topi kuning/optimis: melihat sisi terang dari masalah yang dibahas dan solusinya;
• Topi hitam/hakim: mengkritisi setiap solusi untuk meminimalkan risiko. 
• Topi putih/fakta: mempertimbangkan informasi apa saja yang sudah dimiliki dan apa lagi yang perlu kamu kumpulkan.

Discovery & Action Dialogue (DAD) 

Sama seperti World Cafe, metode DAD sangat menekankan partisipasi aktif anggota kelompok. Sebab, kamu harus menanyakan ketujuh pertanyaan berikut ke setiap partisipan:

• Bagaimana kamu bisa mengetahui ada masalah A?
• Bagaimana kamu bisa berkontribusi untuk memecahkan masalah A?
• Apa yang menghambatmu memecahkan masalah tersebut?
• Apa kamu kenal seseorang yang bisa memecahkannya dengan konsisten?
• Apa kamu punya ide?
• Apa yang harus kamu lakukan untuk mengatasinya? Ada sukarelawan?
• Ada lagi yang perlu dipertimbangkan? 

Kemudian, di akhir sesi diskusi bersama, orang yang bertindak sebagai notulen akan merangkum wawasan penting dan langkah-langkah konkret yang perlu diambil.

The 5 Whys 

Kalau kamu ingin proses pemecahan masalah yang lebih menitikberatkan pada proses identifikasi akar masalah, The 5 Whys merupakan pilihan yang tepat.  

Alasannya, dengan metode ini, kamu harus membuat lima pertanyaan yang dimulai dengan kata “mengapa” dan berkaitan dengan masalah yang dihadapi. 

Lalu, di setiap poin, kamu akan menggali jawaban dari pertanyaan-pertanyaan sebelumnya. Dengan begitu, kamu bisa menemukan penyebab paling utama dari masalah yang sedang kamu hadapi.

Design Sprint 2.0 

Butuh pendekatan proses penyelesaian masalah yang lebih teknis untuk membuat produk? Design Sprint 2.0 dapat membantumu menemukan solusi nyata dari hambatan yang sedang kamu hadapi. 

Prinsip  problem solving  ini sebenarnya hampir sama dengan Agile yang sering digunakan di perusahaan  startup . Pada dasarnya, kamu akan membuat beberapa kelompok kecil. 

Kemudian, kelompok-kelompok kecil ini akan memetakan masalah yang dihadapi konsumen dan membuat sketsa kerangka dari solusi mereka. Kemudian, dari sketsa tersebut lahirlah prototipe produk fisik. 

Namun, proses Design Sprint 2.0 tidak berhenti sampai di sana saja. Setelah membuat prototipe produk, setiap kelompok masih harus melakukan uji coba mendalam dan meningkatkan kualitas prototipe tersebut dari hasil evaluasi. 

Open Space Technology 

Apakah kamu punya tim berisikan orang-orang yang ahli dan berinisiatif untuk belajar secara mandiri? Kamu bisa melibatkan mereka dengan metode  creative problem solving  seperti Open Space Technology. 

Berbeda dengan metode-metode sebelumnya yang memerlukan arahan spesifik, Open Space Technology justru lebih menekankan kebebasan dan fleksibilitas.  

Sebab, setelah kamu memperkenalkan topik yang ingin dibahas, kamu cukup membiarkan semua orang berdiskusi secara leluasa. Lalu, kamu bisa mendengarkan solusi yang mereka presentasikan sebagai proses penyelesaian masalah.

Lightning Decision Jam

Bagi kamu yang punya banyak masalah, tapi bingung harus memulai dari mana, ada satu metode untuk membantumu menentukan prioritas, yaitu Lightning Decision Jam. 

Pada metode  problem solving  ini, kamu akan diminta menuliskan semua kekhawatiran, tantangan, dan kesalahan yang pernah dibuat berkaitan topik tertentu di dalam sebuah buku catatan kecil.  

Lalu, kamu dan anggota tim dapat memilih masalah mana yang sekiranya perlu diselesaikan terlebih dahulu. Jika sudah, barulah kamu bisa merancang solusi untuk mengatasinya bersama-sama ataupun sendirian. ⁠

Mengingat  problem solving skill  adalah kemampuan yang sangat diperlukan untuk kehidupan sehari-hari maupun profesional, mungkin kamu sedang mencari cara untuk meningkatkannya. 

Jangan berkecil hati, kamu bisa mencoba tiga tips berikut untuk mengasah kemampuanmu:

Tingkatkan keterampilan teknis 

Pertama dan yang paling penting, kamu perlu mengasah  kemampuan analitik dan deduksi logis untuk bisa menerapkan  problem solving  dengan baik. 

Kalau kamu merasa kedua hal tersebut adalah kelemahan terbesar dalam dirimu, tenang dulu. Kamu bisa mengasahnya dengan rajin membaca buku, memecahkan studi kasus, atau bahkan mengikuti pelatihan  problem solving . 

Temukan peluang-peluang baru 

Salah satu kunci kesuksesan pemecahan masalah adalah kejelian melihat peluang tersembunyi di sekitar. Maka dari itu, kamu harus selalu mencari berbagai kesempatan untuk menjalankan solusimu.

Nah, agar kamu dapat menemukannya dengan lebih mudah, kamu perlu memiliki pikiran yang terbuka dan bisa melihat sebuah isu dari berbagai sudut pandang.  Dengan demikian, kamu akan lebih bersedia  mendobrak zona nyaman  serta mencoba hal-hal baru yang belum pernah terpikirkan sebelumnya. 

Berfokus pada orang lain 

Sering kali, pengambilan keputusan kita akan berdampak pada nasib orang lain. Oleh sebab itu, saat melakukan  problem solving , pastikan kamu juga mempertimbangkan perasaan, pemikiran, dan situasi mereka. 

Dengan lebih memahami lawan bicara, kamu tidak hanya akan melatih empati untuk menjalin hubungan baik, tapi juga mendapatkan wawasan baru yang dibutuhkan untuk memecahkan masalah. ⁠

Saat berinteraksi dengan banyak orang di sebuah organisasi, kamu akan berhadapan dengan berbagai tantangan. Supaya kamu lebih siap menghadapinya, berikut berbagai contoh kasus problem solving  dalam organisasi secara nyata: 

Contoh 1: Deadline mepet dan beban kerja banyak 

Salah satu contoh  problem solving yang akan sering kamu jumpai di dunia profesional adalah tugas yang menumpuk dengan tenggat waktu berdekatan. Jika kamu berada dalam situasi ini, jangan panik dulu. 

Pertama, tarik napas agar kamu bisa berpikir dengan jernih. Setelah itu, kamu bisa menggunakan pendekatan pemecahan masalah untuk menentukan daftar prioritas tugas, mulai dari yang paling  urgent  hingga yang bisa dikerjakan nanti. 

Kalau kamu sudah membuat prioritas, sekarang tentukan mana yang sekiranya harus kamu kerjakan sendiri dan mana yang bisa kamu delegasikan kepada rekan kerja. Tapi, pastikan kemampuanmu dan orang yang kamu mintai bantuan sesuai dengan persyaratan teknisnya. 

Contoh 2: Konflik internal dalam tim 

Apa yang harus kamu lakukan ketika hubungan antara anggota tim jauh dari kata harmonis hingga mereka enggan bekerja sama? Langkah pertama untuk  problem solving  dalam organisasi seperti ini adalah dengan mengetahui penyebab dari konflik tersebut. 

Misalnya, mungkin ada perbedaan dalam gaya kerja atau kepribadian orang tertentu yang menyebabkan perselisihan. Tapi, terlepas dari penyebabnya, kamu harus tetap tenang dan mendengarkan sudut pandang dari setiap orang. Hargai pendapat mereka, tunjukkan empati, dan berikan solusi yang menguntungkan kedua belah pihak.

Contoh 3: Rasio turnover tinggi dalam tim

 Biaya yang diperlukan untuk mencari karyawan pengganti dan melatih mereka dari awal bisa sangat mahal. Bahkan, menurut estimasi  SHRM , nominal tertingginya bisa mencapai 250.000 dolar AS.

Maka dari itu, kamu perlu segera bertindak saat ada banyak pegawai yang  mengundurkan diri dari tempat kerja  secara bersamaan hingga rasio  turnover  tim kamu meningkat drastis. 

Pertama, ketahui apa saja yang mendorong keputusan tersebut. Terutama, dari segi apakah ada kaitannya dengan kebijakan perusahaan seperti beban kerja, jadwal kerja, dan gaji. 

Kalau kamu sudah memetakan penyebabnya, barulah kamu bisa menyampaikannya kepada petinggi terkait untuk mendiskusikan solusinya bersama-sama agar menguntungkan pegawai. ⁠

Sekarang, kamu sudah memahami esensi dari pengertian  problem solving  dan manfaatnya secara menyeluruh. Intinya, ingatlah bahwa memiliki kemampuan ini akan menguntungkanmu dalam berbagai aspek kehidupan sehari-hari maupun profesional untuk menunjang kesejahteraanmu. 

Untuk mengasah kemampuan tersebut, teruslah berlatih dan belajarlah dari berbagai contoh kasus  problem solving  di kehidupan nyata. Salah satu sumber pembelajaran yang bisa kamu gunakan adalah rubrik  Tips Karier di situs Jobstreet by SEEK. 

Lalu, agar kemampuanmu terus berkembang, jangan lupa terapkan secara nyata di dunia kerja.

Kalau kamu sedang mencari perusahaan yang tepat untuk mengembangkan kariermu, Jobstreet by SEEK menawarkan akses ribuan lowongan pekerjaan yang dapat kamu temukan di website dan aplikasi  smartphone   Android  serta  iOS .  

Setelah mengirimkan lamaran, kamu juga bisa berlatih dengan  alat praktik wawancara supaya lebih siap.  Yuk,  kembangkan dirimu dengan Jobstreet! ⁠

Pertanyaan Seputar Problem Solving

• Apa yang dimaksud dengan problem solving? ⁠ Problem solving  artinya tindakan memecahkan masalah dalam kehidupan sehari-hari dan profesional. ⁠
• Apa langkah-langkah dalam problem solving? Langkah-langkah problem solving melibatkan identifikasi masalah, penyelidikan akar masalah, pembuatan daftar prioritas pemecahan masalah, analisis informasi, pembuatan solusi, memilih solusi, penerapan solusi, dan evaluasi. ⁠
• Apa tujuan dari problem solving? ⁠ Tujuan problem solving adalah untuk mengatasi hambatan yang membuatmu kesulitan memenuhi kebutuhan penting atau meraih pencapaian tertentu. ⁠
• Bagaimana cara meningkatkan kemampuan problem solving? ⁠ Untuk meningkatkan skill problem solving, kamu bisa rutin membaca buku, mempelajari studi kasus, dan mengikuti pelatihan khusus. ⁠
• Strategi apa yang harus dilakukan dalam problem solving? ⁠ Terlepas dari metode yang digunakan, kamu harus bisa menganalisis informasi, melakukan riset, dan mempertimbangkan pendapat orang lain supaya keputusanmu bersifat objektif serta menguntungkan semua pihak. ⁠
• Mengapa strategi problem solving di dunia kerja diperlukan? ⁠ Selain meningkatkan peluang mendapatkan pekerjaan, mengetahui strategi problem  ⁠solving yang tepat akan membantumu mencapai target dan menjaga ⁠hubungan baik  ⁠dengan orang lain. ⁠
• Apa langkah terakhir dalam problem solving? ⁠ Langkah terakhir dalam problem solving adalah menerapkan solusi yang sudah dirancang dan mengevaluasi efektivitasnya.

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Problem Solving: Arti, Proses, Contoh, Manfaat, dan Tips Tingkatkannya

Isi Artikel

Banyak orang yang mengira bahwa problem solving atau pemecahan masalah adalah suatu  skill  yang dapat diasah lewat praktik.

Padahal, hal ini kurang tepat, lho. Meski merupakan bagian dari soft skill , kamu bisa belajar penyelesaian persoalan layaknya hard skill .

Kira-kira, mengapa bisa begitu? Ketahui jawabannya dengan menyimak penjelasan Glints di bawah ini.

Apa Itu Skill Problem Solving?

Seperti namanya, problem solving adalah sebuah kemampuan untuk mencari solusi atas segala halangan dari tujuanmu. 

Semakin baik kamu menguasai skill ini, semakin cepat dan efektif pula persoalanmu selesai. Hal ini juga berlaku sebaliknya.

Metode Problem Solving

Mengutip dari Session Lab dan Chanty , berikut adalah beberapa metode yang bisa digunakan untuk pemecahan masalah.

1.  Brainstorming

Salah satu metode yang ampuh untuk memecahkan masalah adalah  brainstorming .

Ketika melakukan  brainstorming , kamu dan rekan kerja mencari solusi kreatif untuk suatu masalah.

Sehingga, metode ini mendorong setiap orang yang terlibat untuk menyampaikan idenya.

Setelah terkumpul, ide-ide tersebut bisa digabungkan atau diolah untuk menjadi satu solusi utama.

2. 6  thinking hats

Six thinking hats  adalah metode  problem solving  selanjutnya.

Dalam metode ini, kamu dan rekan kerja silih berganti mencoba menghadapi suatu masalah dari beragam perspektif.

Adapun perspektif yang digunakan seperti;

• fakta dan data
• solusi kreatif
• hal positif dari suatu solusi
• hal negatif dari suatu solusi

Fakta-fakta tersebut jadi pendorong dan pendukungmu dalam mencari solusi.

3.  The 5 whys

Metode  problem solving  lain yang bisa kamu gunakan bersama rekan kerja adalah  the 5 whys .

Dalam metode ini, kamu cukup meng- highlight  masalah yang akan dipecahkan.

Lalu, tanyakan pada dirimu dan tim “mengapa” masalah tersebut bisa terjadi. Setelah itu, terus tanyakan “mengapa” atau “ why ” sebanyak 5 kali.

Namun, pastikan untuk menjawab seluruh pertanyaan dengan objektif. Hal ini dapat membantumu capai akar dari permasalahan yang sedang dihadapi.

4.  Lightning decision jam

Dalam metode ini, kamu dan rekan kerja masing-masing menulis tantangan, kekhawatiran, atau kesalahan dalam sebuah catatan kecil.

Kemudian, tim memilih masalah mana yang diselesaikan dan dituntun untuk melihat masalah tersebut dari sudut pandang baru.

Hal ini memungkinkan kamu dan tim untuk membuat solusi dari masalah yang dipilih.

Metode ini pun memastikan bahwa proses penyelesaian masalah dilakukan secara terfokus dan teratur.

5.  Failure mode and effect analysis

Metode  problem solving  lain yang bisa kamu gunakan adalah  failure mode and effect  analysis .

Dalam metode ini, kamu dan tim mencoba menganalisis setiap elemen dari strategi bisnis dan memikirkan hal-hal terburuk yang mungkin terjadi.

Hal-hal terburuk seperti kenapa strategimu gagal dan kapan terjadinya menjadi pokok bahasan dari pemecahan masalah dalam metode ini.

Dengan melihat kemungkinan terburuk dan seberapa mungkin hal itu terjadi, kamu dan tim bisa mencari solusi dari permasalahan tersebut serta mencegahnya.

Contoh Problem Solving

Berikut adalah beberapa contoh kasus yang sering terjadi di dunia kerja di mana kemampuan  problem solving  sangat dibutuhkan.

1. Menyelesaikan komplain pelanggan

Di kasus ini, jelas sebagai seorang profesional, kamu harus memikirkan bagaimana langkah-langkah menyelesaikan masalahnya.

Meski bisa merujuk ke SOP, tidak jarang komplain konsumen bersifat unik yang juga membutuhkan langkah penyesuaian yang  personalized.

2. Mencari jalan keluar ketika ada alat yang rusak

Contoh yang satu ini mungkin terkesan sepele, tetapi dengan  problem solving skill  yang kurang mumpuni, seseorang bisa saja membuatnya menjadi masalah besar.

Ketika mesin fotokopi di kantor rusak, misalnya, kamu dapat langsung menghubungi tim terkait yang bertugas mengelola peralatan kantor, seperti tim operasional atau  general affair.

Yang pasti, jangan menyembunyikan kejadian tersebut karena justru bisa menghambat pekerjaan orang lain ke depannya.

3. Melakukan kesalahan saat bekerja

Contoh  problem solving  selanjutnya adalah ketika berusaha memperbaiki kesalahan saat mengerjakan tugas.

Langkah penyelesaiannya tentu sangat berbeda-beda, tergantung kesalahan yang dibuat.

Misalnya, kamu salah  upload  konten di media sosial.

Dalam hal ini, tentu langkah pertama adalah langsung menghapus konten tersebut secepatnya, lalu upload  ulang konten yang benar. Kamu juga bisa  post  permintaan maaf atau klarifikasi tambahan.

Jangan lupa untuk menginformasikannya pada tim supaya bisa mengantisipasi dampak ke depannya.

4. Menghadapi rekan kerja yang sulit diajak kerja sama

Tak jarang, permasalahan di tempat kerja muncul akibat interaksi yang kurang baik dengan rekan satu tim.

Situasi seperti ini juga sangat memerlukan kemampuan pemecahan masalah yang mumpuni.

Kamu bisa coba beberapa cara, mulai dari berusaha bangun komunikasi langsung dengannya atau konsultasi ke atasan.

5. Menyesuaikan  deadline  ketika ada tugas mendadak

Situasi seperti ini terkadang tidak bisa dihindari di dunia kerja.

Kamu harus bisa mengatur tugas dan waktumu dengan baik sehingga semua tugas tetap bisa diselesaikan sesuai standar dan  timeline.

Untuk menyelesaikan masalah ini, coba delegasi atau tunda beberapa tugasmu sesuai skala prioritas. Diskusikan ini ke atasan supaya tidak ada miskomunikasi.

Proses Problem Solving

Apakah kamu masih bingung dengan pengertian dari penyelesaian masalah? Tak heran, skill yang satu ini memang abstrak.

Meski begitu, kamu tak perlu khawatir. Glints sudah merangkum langkah-langkah memecahkan masalah dari The Balance Careers . 

Dengan penjelasan ini, kamu tentu bisa lebih menggambarkan semuanya. Ada juga tambahan contoh pemecahan masalah berikut ini agar kamu makin paham.

Misalnya, kamu adalah seorang koki di toko kue. Biasanya, kamu menjual 100 buah roti dan 100 buah bolu kukus dalam sehari.

Sayangnya, hari ini, produksi roti terhambat. Ini tentu bisa merugikan toko roti.

Untuk contoh pemecahan masalah ini, langkah-langkahnya adalah:

1. Analisis situasi

Solusi yang tepat tentu menyasar akar masalah. Oleh karena itu, kamu wajib tahu akar masalah ini dulu.

Dalam tahap ini, kamu membutuhkan skill – skill seperti:

• pengumpulan data
• analisis data
• analisis historis

Oleh karena itu, dalam konteks ini, kamu wajib mencari penyebab masalah di toko kue. Misalnya, ternyata, mesin penggiling adonanmu rusak.

2. Buat daftar solusi

Tahap problem solving selanjutnya adalah mencari jalan keluar. 

Tentu saja, tiap masalah punya jalan keluar yang beraneka ragam. Oleh karena itu, daftar dulu berbagai kemungkinan solusi yang ada, ya! 

Untuk melakukan tahap ini, kamu butuh kemampuan:

• berpikir kreatif
• perencanaan proyek
• desain proyek

Untuk masalah di toko kue, alternatif solusinya adalah:

• membeli mesin penggiling baru
• mencoba memperbaiki mesin penggiling
• tidak produksi roti sama sekali, buat bolu kukus saja
• tidak produksi bolu kukus, tenaga dan waktu dipakai untuk membuat roti
• membuat roti tanpa mesin penggiling
• dan lain-lain

Apa pun yang kamu pikirkan, kumpulkan saja dulu menjadi satu.

3. Pilih solusi terbaik

Sudah menuliskan berbagai alternatif solusi? Sekarang, saatnya memilih yang terbaik di antara pilihan itu.

Ingat, tiap pilihan punya konsekuensinya masing-masing. Terlebih lagi, kadang kala, kamu tak jadi satu-satunya orang yang membuat keputusan. 

Oleh karena itu, dalam tahap problem solving ini, kamu butuh skill :

• penentuan prioritas

Kita kembali lagi ke contoh pemecahan masalah toko kue. Misalnya, pada hari itu, ada pesanan 50 roti yang harus selesai hari ini.

Akhirnya, kamu memutuskan untuk mengurangi produksi bolu kukus. Ada tenaga ekstra untuk membuat roti pesanan tanpa mesin penggiling.

Ingat, tiap masalah punya konteks yang berbeda-beda. Oleh karena itu, solusi terbaiknya juga berbeda-beda.

Untuk memahami hal ini, Glints akan memberikan contoh tambahan. Misalnya, ternyata, roti yang kemarin masih bersisa. Roti-roti itu juga sangat layak jual.

Kalau begitu, kamu tak perlu membuatnya lagi. Hari ini, fokuskan saja tenaga untuk mengukus bolu-bolu.

Itulah mengapa, penting bagimu memahami konteks persoalan.

4. Rancang rencana

Sudah punya solusi, waktunya eksekusi. Dalam pelaksanaan ini, kamu membutuhkan kemampuan:

• manajemen proyek
• manajemen waktu

Nah, saat mengurangi produksi bolu kukus, siapa saja yang dipindah ke produksi roti? Berapa waktu yang dibutuhkan? 

Karena tidak ada mesin, bekerja di dapur tentu menjadi lebih lelah. Kamu juga harus memikirkan waktu istirahat pegawai.

Rencanakan semua ini matang-matang, ya! Dengan panduan yang jelas, solusi yang kamu buat tentu bisa terlaksana dengan baik.

5. Evaluasi

Saat sudah selesai, coba lakukan evaluasi dari solusimu. Apakah jalan keluar itu sudah benar-benar bekerja?

Dalam proses ini, skill yang kamu butuhkan antara lain:

Mengapa Skill Problem Solving Penting?

Kamu sudah memahami pengertian dari skill pemecahan masalah. Nah, sekarang, kenapa kamu harus menguasainya?

Dirangkum dari Cleverism & Institute of Chartered Accountants in England and Wales , ini dia informasinya.

1. Bukti mampu terapkan ilmu

Proses belajar tentu mengasah kemampuan analisis. Secara otomatis, kamu bisa lebih memahami masalah dan mencari solusinya.

Sayangnya, seperti yang sudah Glints singgung, tiap konteks masalah punya solusi yang berbeda-beda.

Nah, realitanya, persoalan punya aneka ragam konteks. Secara otomatis, pemilihan solusinya juga berbeda.

Lalu, apa tanda bahwa kamu mampu memahami konteks dan memilih solusi yang tepat? Skill pemecahan masalah adalah jawabannya.

Belajar merupakan tanda bahwa kamu menguasai teori. Praktiknya bisa dibuktikan lewat skill menyelesaikan masalah.

2. Menarik rekruter

Pemecahan masalah merupakan skill yang terdiri dari berbagai macam sub- skill . Ragam sub- skill ini sudah Glints jelaskan tadi.

Analisis, kreativitas, manajemen proyek, bahkan kolaborasi, termasuk di dalamnya.

Dengan alasan ini, rekruter menyukai orang yang mampu menyelesaikan persoalan. Ia jadi penanda bahwa kamu juga punya segudang skill lainnya.

3. Penting untuk promosi

Salah satu ciri pemimpin baik adalah mampu menyelesaikan masalah. Oleh karena itu, kalau ingin promosi jabatan, kamu wajib memilikinya.

Tips Meningkatkan Skill Problem Solving

© Freepik.com

Nah, sekarang, bagaimana cara meningkatkan kemampuan ini ? Dirangkum dari Indeed , ini dia tipsnya:

1. Asah skill teknis

Ternyata, kemampuan teknikal atau hard skill bisa membuatmu mahir memecahkan masalah, lho. 

Padahal, problem solving sendiri adalah contoh soft skill . Ternyata, ia tetap tak bisa lepas dari hard skill . Glints sudah menyebutkan hal ini di atas.

Ini bisa terjadi karena, dengan kemampuan teknikal, kamu jadi punya teori. Solusi dari masalah pun bisa lebih mudah dirumuskan.

2. Cari kesempatan baru

Jangan lupa, asah terus kemampuanmu dengan praktik di berbagai tempat. Kamu bisa melakukannya dengan mencari:

• proyek baru
• tim yang berbeda dengan sekarang
• komunitas atau organisasi di luar tempat kerja

3. Perhatikan orang lain

Selain menempa diri, kamu juga bisa mengamati proses pemecahan masalah orang lain, lho.

Coba perhatikan bagaimana atasan atau kolegamu menghadapi persoalan. Siapa tahu, kamu bisa meniru dan memodifikasi pola pemecahan masalah mereka.

Demikian penjelasan Glints soal kemampuan problem solving . Terus asah skill ini agar kariermu makin berkembang, ya!

Kalau kamu mau belajar lebih banyak tentang kemampuan penting di dunia kerja, yuk, baca artikel lainnya dari Glints!

Ada kumpulan artikel yang secara khusus mengulas topik tentang  hard skill  beserta  soft skill  di dunia kerja.

Baik yang berkaitan dengan pekerjaan tertentu maupun  skill yang secara umum dicari banyak perusahaan.

Tertarik? Ayo klik  link  ini sekarang juga untuk baca artikel lainnya!

• What Are Problem-Solving Skills?
• Problem solving-Cleverism
• Problem solving-Institute of Chartered Accountants in England and Wales
• Problem-Solving Skills: Definitions and Examples

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TAMBAH ILMU & SKILL

CONCEPTUAL ANALYSIS article

Complex problem solving: what it is and what it is not.

• 1 Department of Psychology, University of Bamberg, Bamberg, Germany
• 2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

(a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.

(b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.

(c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.

(d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.

(e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

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Keywords : complex problem solving, validity, assessment, definition, MicroDYN

Citation: Dörner D and Funke J (2017) Complex Problem Solving: What It Is and What It Is Not. Front. Psychol. 8:1153. doi: 10.3389/fpsyg.2017.01153

Received: 14 March 2017; Accepted: 23 June 2017; Published: 11 July 2017.

Reviewed by:

Copyright © 2017 Dörner and Funke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Joachim Funke, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Bagaimana meningkatkan kemampuan untuk menyelesaikan masalah kompleks ?

Complex Problem Solving adalah kemampuan seseorang untuk mengidentifikasi masalah yang kompleks, serta mengerti dan mereview informasi yang berkaitan, agar dapat menciptakan solusi untuk masalah tersebut. Kemampuan ini sangat esensial di lingkup kerja.

Lalu, bagimana caranya seseorang dapat meningkatkan kemampuan mereka untuk hal ini ?

Kemampuan menyelesaikan masalah dapat ditingkatkan dengan berbagai cara, antara lain:

Mengidentifikasi Masalah Awal mula dari kemampuan seseorang untuk dapat menyelesaikan masalah adalah dengan mengidentifikasi asal-usul masalah tersebut, mencoba untuk menelaah dan kemudian merencanakan langkah-langkah yang tepat agar kita dapat menyelesaikan masalah tersebut. Dalam beberapa situasi, banyak mahasiswa merasa terbebani dengan masalah di depan mereka dan hanya melihat sebuah rintangan. Tetapi, pemecah masalah yang ulung akan mencoba untuk melihat sebuah masalah dari akarnya, yang dapat dipelajari dan kemudian dari situ kita dapat bertindak.

Albert Einstein pernah berkata bahwa perumusan dari sebuah masalah lebih krusial dari solusinya.

Menentukan Elemen Utama Masalah Langkah selanjutnya adalah kemampuan untuk mencacah masalah menjadi masalah yang lebih kecil dan lebih kecil lagi, dengan menentukan element utama sebuah masalah. Hal ini dapat mempermudah kerja kita, ketika dihadapi sebuah masalah yang ibaratnya gunung kita akan berfokus dengan bukit-bukit kecilnya terlebih dahulu. Ketika dihadapi dengan masalah yang lebih kecil kita dapat menentukan langkah-langkah kita untuk menyelesaikan masalah dengan benar sehingga kita bisa menciptakan solusi yang tepat.

Melihat Kemungkinan Solusi Menemukan solusi yang mungkin adalah langkah yang sangat rumit dalam proses pemecahan masalah, seperti pada permukaan, sepertinya sebagian besar pekerjaan sudah selesai dan tujuan akhirnya sudah dekat. Pada kenyataannya, siswa tidak boleh hanya mencari cara sederhana untuk mengatasi unsur-unsur masalah. Mereka harus menemukan cara yang paling efektif dan mengubahnya menjadi sebuah kesempatan untuk membuat sebuah kisah sukses yang kuat

Bertindak menyelesaikan masalah Mengembangkan rencana pelaksanaan langkah-demi-langkah dan bertindak secara efektif dan tegas adalah sentuhan terakhir dalam proses pemecahan masalah. Ini juga merupakan keterampilan penting karena tidak masalah seberapa efektif siswa mengidentifikasi masalah, menentukan elemennya dan memeriksa kemungkinan solusi; semuanya masih bermuara pada kemampuan untuk melakukan langkah konkret untuk melaksanakan rencana aksi. Dalam formula pemecahan masalah ini siswa juga harus menguasai keterampilan seperti memantau dan mengevaluasi keseluruhan proses pelaksanaan tindakan dan - jika itu adalah usaha kelompok - pelajari bagaimana mendelegasikan bagian-bagian tertentu dari pekerjaan satu sama lain atau kepada pemangku kepentingan eksternal.

Mencari pelajaran untuk dipelajari Pada saat masalah dipecahkan, saya menyarankan agar siswa duduk bersama semua pohon pemecahan masalah dan rencana tindakan mereka, baik sendiri atau bersama-sama jika itu adalah proyek kelompok. Inilah saatnya untuk melihat ke belakang dan melihat apakah ada kebutuhan untuk menyetel pekerjaan yang telah selesai. Yang sangat berharga adalah meluangkan waktu untuk mengevaluasi keseluruhan proses dan merumuskan pelajaran yang akan dipelajari sehingga proyek pemecahan masalah berikutnya akan lebih efektif dan menghasilkan solusi yang lebih

Selain dengan melakukan hal-hal yang berkaitan dengan penyelesaian masalah itu sendiri, kita bisa melakukan hal lain yang memepengaruhi kemampuan kita untuk menyelesaikan masalah. Antara lain adalah mendapat tidur yang cukup, melakukan kegiatan yang bersifat jasmani, bermain game puzzle atau yang berbasis logika, menggunakan mind-mapping untuk membantu memberi gambaran atas masalah, dan banyak hal lainnya. Hal-hal yang baru saja disebutkan mempengaruhi sisi psikologi dari seseorang, sehingga bisa memberi mereka jalan pikiran yang lebih terbuka dan kritis, sehingga mempermudah dalam penyelesaian masalah. Pikiran yang sehat dapat membantu kita.

Setiap hari kita dihadapkan dengan masalah yang harus dipecahkan. Masalah muncul dari berbagai hal. Mulai dari masalah yang kecil, masalah sehari-hari, bahkan masalah yang kompleks. Kemampuan untuk menyelesaikan masalah sangat diperlukan. Pemikiran analitis dan keterampilan memecahkan masalah adalah bagian dari kemampuan menyelesaikan masalah. Semakin banyak kita menyelesaikan masalah, semakin baik pula keterampilan kita dalam menyelesaikan masalah.

Dimanapun anda berada, keterampilan memecahankan masalah akan berguna untuk kehidupan sehari-hari. Anda akan dinilai berdasarkan kemampuan anda dalam memecahkan masalah. Pemecahan masalah sangat penting karena kita hidup di dunia yang penuh akan pilihan dan kita harus melakukan keputusan untuk dibuat. Lalu apa yang bisa kita lakukan untuk meningkatkan kemampuan memecahkan masalah?

Pemecahan masalah melibatkan metode dan keterampilan untuk menemukan solusi terbaik untuk menyelesaikan masalah. Kebanyakan orang berpikir bahwa anda harus sangat cerdas untuk menjadi pemecah masalah yang baik, tapi itu tidak benar. Anda tidak harus super pintar untuk menjadi pemecah masalah yang baik, anda hanya perlu banyak berlatih. Bila anda memahami berbagai langkah untuk memecahkan masalah, anda akan dapat menemukan solusi hebat.

1. Memahami Persoalan Tentukan masalah dan definisikan secara jelas. Jika anda tidak memahami persoalan dengan benar, bisa jadi solusi anda akan tidak efektif atau gagal sama sekali. Cobalah merumuskan pertanyaan. Dengan berulang kali mengajukan pertanyaan “mengapa” pada sebuah masalah, anda dapat menggali akar penyebab masalah dan memahami persoalan. Begitulah cara untuk bisa menemukan solusi terbaik dalam mengatasi masalah.

2. Tentukan tujuan dan kumpulkan informasi Seiring dengan menentukan masalah dan tujuan, anda harus mengumpulkan sebanyak mungkin fakta tentang masalah ini agar bisa mendapatkan gambaran yang jelas. Kumpulkan data, tanyakan kepada orang atau ahli yang terkait, carilah sumber daya secara online, cetak, atau di tempat lain. Begitu Anda memiliki data, aturlah. Cobalah untuk melakukan ini dengan cara memutar, mengkondensasi, atau meringkasnya. Mungkin Anda bahkan bisa memetakannya dalam grafik. Anda mungkin tidak perlu repot dengan langkah ini untuk masalah sederhana, tapi ini penting untuk sifat yang lebih kompleks.

3. Menganalisis informasi Selanjutnya adalah menganalisis informasi dari data yang telah dikumpulkan. Dari situ anda akan mencari hubungan dengan harapan bisa lebih memahami keseluruhan situasi. Mulailah dengan data mentah. Terkadang, informasi perlu dipecah menjadi bagian yang lebih kecil agar lebih mudah diatur. Alat seperti bagan, grafik, atau model sebab-akibat akan membantu untuk melakukan hal ini.

4. Sederhanakan dan Menyusun Rencana Buat daftar solusi yang memungkinan. Cobalah semua kemungkinan bahkan walaupun terdengar konyol pada awalnya. Penting bagi kita untuk tetap berpikir terbuka dan meningkatkan pemikiran kreatif yang dapat memicu solusi potensial. Gunakan beberapa strategi untuk membantu anda menghasilkan solusi, pecahkan masalah menjadi bagian – bagian kecil, gunakan analogi dan cobalah untuk menemukan kemiripan dengan masalah yang sebelumnya pernah dipecahkan. Jika anda menemukan kesamaan dengan situasi yang telah anda hadapi sebelumnya, anda mungkin bisa menyesuaikan beberapa solusi untuk digunakan sekarang.

5. Menerapkan dan Mengevaluasi Rencana Pilih dan evaluasi solusi. Anda juga harus menganalisis semua solusi untuk melihat konsekuensinya. Pilih solusi yang paling sesuai dengan kebutuhan anda. Setelah Anda memilih solusi terbaik, terapkan solusi, setelah anda menerapkan solusi, anda harus memantau dan meninjau hasilnya. Tanyakan pada diri anda apakah solusinya bekerja. Evaluasi dan sesuaikan solusi yang pas dengan kebutuhan.

Banyak orang percaya bahwa kamu harus pintar sekali untuk menjadi seorang pemecah masalah yang baik, tetapi itu salah. Ketika kamu mengerti tahap – tahap untuk memecahkan masalah, kamu dapat dengan mudah muncul dengan solusi yang hebat. Berikut cara untuk meningkatkan kemampuan memecahkan masalah.

1. Fokus kepada solusi, bukan masalahnya Ahli saraf sudah membuktikan bahwa otak kita tidak dapat mencari solusi jika kita fokus kepada masalah. Ini terjadi karena ketika kita fokus kepada masalah, kita memberi ‘makan’ otak energi negatif yang mengaktifkan emosi negatif di otak. Alih – alih memikirkan masalah, lebih baik yang kita lakukan adalah tenang. Tenang membantu kita untuk mengetahui masalahnya dan kemudian cari solusinya.

2. Biasakan gunakan 5 WHY Dengan mengulang – ulang menanyakan pertanyaan mengapa di dalam masalah, kita dapat menggali menuju akar dari permasalahan, dan itulah bagaimana kita dapat mencari solusi terbaik. Contoh:

Mengapa saya tidak bisa bangun pagi? Karena saya kurang tidur.

Mengapa saya kurang tidur? Karena saya begadang.

Mengapa saya begadang? Karena saya mengerjakan tugas sampai larut malam.

Mengapa saya mengerjakan tugas sampai larut malam? Karena saya menunda mengerjakan tugas

Mengapa saya menunda mengerjakan tugas? Karena saya terlalu banyak bermain game

Dari contoh diatas, kita dapat mengetahui akar permasalahannya yaitu terlalu banyak main game, sehingga untuk kedepannya kita harus bisa lebih mengatur waktu.

3. Menyerdehanakan segala sesuatu Sebagai manusia, tentu kita pernah membuat segala sesuatu menjadi rumit dan tentu saja itu akan sangat merepotkan. Mulai sekarang, cobalah untuk menyederhanakan masalah kita dengan cara mencari simpulan dari permasalahan itu. Mulailah dari awal, cobalah mencari solusi yang mudah. Dan mungkin hasilnya akan mengejutkan bagi kita.

4. Buatlah list solusi sebanyak mungkin Cobalah untuk membuat ‘Solusi Yang Mungkin Berhasil’ walaupun jika ada solusi yang terdengar aneh atau konyol. Penting sekali bagi kita untuk open mind untuk meningkatkan kreativitas. Apapun yang kita lakukan, jangan menertawakan diri sendiri karena menemukan ‘solusi bodoh’ karena sering kali gagasan gila itu bisa menjadi solusi yang lebih baik lagi.

5. Berpikir dari sisi lain Ada kalanya nanti kita herus berhadapan dengan jalan buntu, yang perlu kita lakukan adalah merubah arah berpikir kita dengan berpikir dari sisi lain. Cobalah untuk mencari jalan lain dan melihat masalah dari sisi lain. Kita bisa mencoba dengan membalik objektif sekitar kita dan melihat solusi baru. Mungkin jika ini terasa sangat bodoh, pemikiran yang baru dan unik biasanya merangsang solusi baru.

6. Gunakan kata – kata yang membuat kemungkinan Gunakan pemikiran anda dengan ungkapan – ungkapan seperti ‘bagaimana jika …’ dan ‘bayangkan jika …’ Istilah – istilah ini membuat otak kita berpikir lebih kreatif dan membuat kita memikirkan sebuah solusi. Hindari bahasa yang membuat kita berpikir negatif seperti ‘Saya tidak berpikir …’ atau ‘Ini tidak benar tapi …’.

Cobalah mulai sekarang untuk tidak melihat masalah sebagai sesuatu yang mengerikan. Semua masalah berkata kepada kita bahwa ada sesuatu yang tidak berjalan dengan lancar dan yang kamu harus lakukan adalah mencari jalan baru. Jadi coba dan dekati masalah tanpa ada pertimbangan. Berlatihlah fokus dalam mendefinisikan masalah, tetap tenang dan jangan membuat masalah semakin rumit.

Sumber : 6 Ways to Enhance Your Problem Solving Skills Effectively - Lifehack

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Complex Problem Solving

Program Studi Manajemen Sumber Daya Manusia, Politeknik Ketenagakerjaan

Complex Problem Solving (CPS) adalah paradigma baru dalam menyelesaikan masalah atau permasalahan. Dalam hal ini masalah dimaksudkan sebagai problem, sedangkan permasalahan adalah problematics.

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Prinsip Dasar Memecahkan Masalah (Problem Solving)

• Posted by by Arry Rahmawan
• September 13, 2020

Pada kesempatan kali ini izinkan saya untuk menulis tentang prinsip dasar memecahkan masalah atau problem solving . Ketika artikel ini saya tulis, media-media informasi sedang ramai membahas isu diberlakukannya kembali PSBB di Jakarta karena kembali meningkatnya kasus COVID-19. Di satu sisi ada banyak pihak yang mendukung, namun tidak sedikit juga pihak yang menolaknya. Pihak yang mendukung mengatakan PSBB total akan sangat bermanfaat untuk menurunkan kasus COVID-19, di sisi lain pihak yang menolak mengatakan bahwa PSBB total di Jakarta akan mematikan roda perekonomian dan membuat Indonesia semakin terjerumus ke jurang resesi. Semenjak diumumkan kasus perdana sejak Bulan Maret, total pertumbuhan kasus aktif COVID-19 tidak juga kunjung turun – bahkan naik.

Kasus di atas adalah sebuah kasus riil dari perlunya seseorang memiliki kemampuan  complex problem solving,  atau pemecahan masalah yang kompleks di tingkat negara atau kebijakan. Conn dan McLean (2018) mengungkapkan bahwa  complex problem solving, critical thinking,  dan  creativity  adalah 3 keterampilan terpenting untuk dikuasai di tahun 2020 dan sampai beberapa dekade setelahnya. Saat saya mengajar mata kuliah pengantar kewirausahaan teknologi di Departemen Teknik Industri UI , saya selalu menekankan 3 hal ini kepada mahasiswa, dan mereka banyak saya berikan latihan agar terasah dalam memecahkan masalah, berpikir kritis, dan juga menjadi mahasiswa solutif.

Mengenal Apa Itu Masalah

Lalu, apa itu   problem solving?  Pertama mari kita pahami dulu apa itu masalah.

Apakah Anda tahu, apa yang dimaksud dengan masalah?

Saya yakin selama ini Anda memiliki banyak masalah dalam hidup (begitu juga saya). Tentu kita ingin semua masalah yang ada di hidup kita bisa diselesaikan dengan cepat. Namun, bagaimana kita bisa menyelesaikan masalah, jika kita tidak tahu apa itu masalah (ga bingung kan, hehe)?

Collins Dictionary, mengartikan masalah adalah kondisi yang tidak sesuai dengan yang diharapkan, menyebabkan kesulitan dalam menjalani hidup. Berdasarkan definisi ini, kita tahu bahwa masalah itu adalah adanya  gap  antara “Realita” dan hal “Ideal” yang ingin kita capai.

Sebagai mahasiswa, Anda pasti pernah mengalami ada mata kuliah atau mata pelajaran yang Anda susah sekali mengikutinya. Dosen sudah memberikan batas bawah kelas yaitu Anda harus dapat 60 di ujian. Tapi setelah ikut nilai Anda 40, sehingga Anda tidak lulus. Ada ‘jarak’ antara realita (Anda dapat 40) dan nilai ideal untuk Anda lulus (minimal 60), yang kalau jarak ini tidak dipecahkan Anda tidak lulus dan harus mengulang lagi mata kuliah tersebut di tahun berikutnya.

Upaya Anda untuk menaikkan nilai Anda dari 40 menjadi lebih dari 60 (let’s say, 80) adalah bentuk sederhana dari problem solving.

Mengenal prinsip dasar memecahkan masalah ( problem solving )

Lalu, apa saja prinsip – prinsip yang perlu Anda ketahui dalam memecahkan masalah? Watanabe (2009) dalam bukunya 101 Problem Solving, memetakan ada 4 langkah dasar yang merupakan prinsip problem solving. 4 langkah dasar tersebut dijelaskan di gambar berikut ini,

Saya menggunakan model yang diajukan Watanabe (2009) karena simpel dan juga konsisten dengan beragam literatur lain tentang pemecahan masalah. Intinya, ada 4 hal yang wajib kita lakukan jika kita ingin memecahkan masalah:

1. Memahami situasi atau mendefinisikan masalah dengan baik (understand the situation )

Banyak orang yang tidak bisa memecahkan masalah karena tidak bisa mendefinisikan masalah yang dihadapi dengan baik. Misalnya, “Saya tidak bisa mendapatkan nilai 80 di kelas karena saya tidak punya teman diskusi selama PSBB.”

Mengapa definisi masalah tersebut kurang bagus? Ya, karena definisi masalah tersebut sudah mengandung solusi. Jika masalahnya seperti itu, maka kita tinggal langsung saja cari teman diskusi. Nah, tapi apakah dengan punya teman diskusi nilai kita langsung naik jadi 80? Belum tentu.

Lalu, bagaimana mendefinisikan masalah dengan lebih baik?

Contohnya seperti ini, “Saat ini saya mendapat nilai 40 di mata kuliah X dan saya menargetkan untuk mendapatkan nilai 80 di ujian berikutnya. Hal ini harus saya capai, karena jika di bawah 60 saya harus mengulang kelas lagi yang akan menghabiskan uang sebesar Rpxxxxx dan waktu sebanyak xxxxx jam yang saya miliki.”

Dengan menggunakan definisi masalah tersebut, Anda pun jadi sadar bagaimana kondisi Anda saat ini, apa yang ingin Anda raih, dan apa dampak yang muncul jika Anda tidak meraihnya. Sampai sini paham? Jika kurang paham bisa bertanya di kotak komentar :).

Satu contoh lagi: “Saat ini saya punya hutang satu juta ke X, dan harus mengembalikannya di tanggal 25 September 2020. Jika tidak mengembalikannya, saya akan ditagih dan kepercayaan orang kepada saya menjadi hilang.”

Nah, jika masih belum paham boleh ditanyakan di kotak komentar.

2.  Mengidentifikasi akar penyebabnya (identify the root cause of the problem )

Setelah mendefinisikan masalah, baru kita mencari apa akar penyebab dari masalah kita. Teknik paling mudah adalah dengan menggunakan teknik “5 Why”. Teknik ini adalah dengan bertanya kepada diri kita terkait dengan mengapa kita bisa mendapat nilai jelek, misalnya.

Why 1: Mengapa saya mendapat nilai 40 di ujian matematika? Karena saya banyak salah di konsep geometri

Why 2: Kenapa banyak salah konsep di geometri? Karena saya tidak mempelajarinya dengan sungguh – sungguh

Why 3: Kenapa saya tidak belajar geometri sungguh – sungguh? Karena saya tidak menyukai bagian tersebut

Why 4: Kenapa saya tidak suka? Karena saya tidak tahu apa hubungan geometri dengan cita – cita saya

Why 5: Kenapa saya tidak tahu hubungan geometri dengan cita – cita saya? Karena saya tidak mencari tahu informasi terkait hal itu

Ternyata di sini ‘akar’ masalahnya bukan semata – mata kita tidak suka dengan bagian geometri, tetapi juga kita tidak termotivasi untuk mempelajarinya karena tidak tahu apa manfaatnya. Dengan teknik 5 why ini, kita jadi tahu apa akar masalahnya dan bisa merumuskan alternatif solusi dengan baik.

3.  Memilih dan membuat action plan  (Development of an effective action plan)

Jika sudah dari fase 2, maka fase berikutnya adalah berpikir kreatif dan kritis terhadap alternatif solusi yang mungkin dilakukan. Sebagai contoh:

• Mencari tahu apa manfaat ilmu geometri dalam kehidupan sehari – hari (Googling)
• Menonton film atau movie terkait dengan pentingnya ilmu geometri
• Belajar geometri dengan bantuan video dari internet
• Mengajarkan geometri ke orang lain secara online

Silakan tuliskan alternatif solusi sebanyak – banyaknya dalam fase ini. Kemudian pilih mana yang sekiranya paling efektif untuk menyelesaikan masalah tersebut dengan penggunaan sumber daya yang paling sedikit (hemat waktu dan biaya yang dikeluarkan).

4. Eksekusi solusi secara total, perbaiki jika tidak efektif (Execute and modify, until it is solved)

Jika sudah yakin dengan suatu solusi, maka tahap berikutnya adalah eksekusi secara total. Namun perlu diingat bahwa solusi yang kita terapkan perlu dimonitor dan dievaluasi, apakah sudah efektif? Jika belum, maka kita cari alternatif solusi lain yang lebih efektif dan efisien (hal ini dinamakan iterasi).

Bagaimana jika strateginya sudah efektif dan kita dapat nilai sesuai dengan apa yang ditargetkan? Maka kita tingkatkan target yang lebih tinggi, misal mencapai nilai 100. Hal ini dinamakan dengan  improvement,  dan akan terus seperti itu secara kontinu.

Nah, sampai sini Anda sudah belajar tentang prinsip – prinsip dalam pemecahan masalah, dan juga beberapa tekniknya. Sekarang kita akan membahas apakah prinsip ini bisa dipakai oleh pengambil kebijakan di tengah pandemi COVID-19?

Problem-Solving dan COVID-19

Ilmu problem solving sebenarnya sangat simpel. Kenapa pemerintah atau instansi terkait tidak bisa efektif menyelesaikan masalah COVID-19? Apa mereka tidak menggunakan prinsip ini?

Saya yakin banyak pakar yang menjadi tim ahli di pemerintah dan mereka jauh lebih tahu daripada saya terkait bagaimana penanganan COVID-19 ini.

Satu hal yang perlu dipahami masyarakat adalah, problem solving untuk tatanan negara itu memiliki tingkat kerumitan yang sangat tinggi. Tingkat kerumitannya ada di sifat masalahnya itu sendiri yaitu  multiple problems, actors, interests, uncertainties. 

Multiple problems , di mana masalahnya ada banyak dan multi dimensi. COVID-19 tidak hanya tentang kesehatan, tapi juga ekonomi, sosial, transportasi, dan lain sebagainya. Multiple actors , yaitu masalahnya dimiliki oleh pihak yang beragam, mulai dari presiden, menteri, pemprov, tenaga kesehatan, dan lainnya. Multiple interests , yaitu masalahnya aktor tersebut memiliki kepentingan yang berbeda-beda. Ada yang interestnya menyelamatkan rakyat, dengan mengurangi mortality rate, ada yang interestnya mendapatkan keuntungan, dsb Multiple uncertainties ,  yaitu ketidakpastian yang menghadang di masa depan macam – macam, mulai dari kemunculan virus baru, perilaku masyarakat yang tiba – tiba susah diatur, di luar kapasitas dari pemerintah sebagai pengambil kebijakan. Multiple rationalities ,  yaitu setiap aktor yang terlibat memiliki rasionalitas yang berbeda dalam memandang masalah. Ada yang dia berbasis pada data karena suka membaca, ada yang berbasis pada bisikan karena dia minta tolong dibacakan staf ahli, dan ada yang berbasis intuisi karena dia sudah merasa berpengalaman menangani hal – hal tersebut di masa lalu.

Kelima faktor itu masing – masing saling terkoneksi satu sama lain, menyebabkan masalah megakompleks yang sedang dihadapi oleh Indonesia saat ini. Jujur kadang saya seringkali gemas dengan netizen sok tahu yang menggampangkan cara pengambilan keputusan di tingkat wilayah atau nasional yang mega kompleks ini, padahal cara pengambilan kebijakan di negara tidak sesederhana menyelesaikan masalah Anda mau masuk kampus mana dan memilih jurusan apa untuk melanjutkan studi.

Namun, berkaca dari prinsip problem solving yang saya jelaskan tadi, saya jadi kepikiran satu hal. Apakah carut marutnya penanganan COVID-19 di Indonesia karena kita tidak memiliki atau tidak tahu apa masalah yang kita hadapi sebagai suatu bangsa? Apakah belum ada definisi masalah yang jelas (fase 1) yang bisa disepakati oleh satu bangsa untuk kita perjuangkan bersama menyelesaikan masalah tersebut?

Apakah kita bisa memiliki satu atau  single problem statement,  yang mana itu menjadi masalah yang kita harus selesaikan bersama sebagai satu bangsa? Jadi apapun peran kita di negara saat ini, single problem statement  tersebut mewakili semua kepentingan kita, sehingga kita berfokus saja untuk menyelesaikan masalah itu agar Indonesia bisa menyelesaikan penanganan COVID-19 dengan lebih baik.

Jika belum ada dan tidak mencoba ditemukan, maka Indonesia dalam kondisi saat ini belum melewati fase 1 dari tahap penyelesaian masalah dan buat saya itu mengerikan.

Jika ada yang bisa merumuskannya, saya yakin Anda akan sangat berjasa kepada negara karena besar kemungkinan Anda dapat mempersatukan bangsa.

Semoga artikel ini bisa sedikit membuka jalan agar kita bisa menjadi seorang pengambil keputusan yang lebih bijaksana.

Salam, Arry Rahmawan

Arry Rahmawan

Arry Rahmawan adalah seorang pembelajar yang memiliki ketertarikan dalam mempelajari ilmu tentang produktivitas hidup, entrepreneurship, dan pengembangan bisnis. Sejak tahun 2012, Arry rutin menulis dan membuat konten terkait tiga topik tersebut di blog ini. Arry menamatkan pendidikan S1 dan S2 nya di Departemen Teknik Industri, Universitas Indonesia dan menjadi dosen tetap non-PNS di Departemen yang sama sejak tahun 2016. Untuk meningkatkan kapasitas keilmuannya, Arry banyak mengambil sertifikasi, workshop, course, dan mentorship dari berbagai institusi kelas dunia. Selain aktif mengajar di UI dan beberapa kampus di Indonesia, Arry juga berpengalaman menjadi konsultan, trainer, dan coach independen untuk ketiga topik yang diminatinya tersebut. Klien yang sudah ditanganinya sangat beragam, mulai dari instansi pemerintahan, kementerian, BUMN, korporasi/swasta, lembaga pendidikan, serta lembaga non-profit. Saat ini Arry berdomisili di Belanda dalam rangka tugas belajar di Delft University of Technology, Faculty of Technology, Policy, and Management. Untuk menghubunginya, silakan kontak melalui direct message LinkedIn atau Instagram

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Problem Solving: Pengertian, Proses, dan Metodenya

Problem solving adalah proses penyelesaian suatu masalah.

Tiffany Revita - 24 February 2023

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Problem Solving pada Rubik / unsplash

Problem solving merupakan salah satu skill penting yang diperlukan dalam dunia kerja. Pasalnya, problem solving berkaitan erat dengan kemampuan seseorang untuk memecahkan masalah dan menemukan solusi terbaik sebagai bentuk penyelesaiannya.

Namun, problem solving tidak hanya berguna untuk diterapkan dalam hal pekerjaan saja, tetapi juga dapat digunakan untuk memecahkan suatu masalah dalam kehidupan sehari-hari. Lantas, bagaimana prosesnya dan seperti apa metode yang digunakannya?

Simak penjelasan selengkapnya dalam artikel ini!

Apa Itu Problem Solving ?

Pada dasarnya, problem solving adalah sebuah cara untuk menemukan solusi dari sebuah masalah. Menurut Oemar Hamalik, problem solving merupakan suatu proses mental dan intelektual dalam menemukan masalah.

Kemampuan ini berkaitan dengan berbagai hal, seperti kemampuan mendengar, menganalisa, meneliti, kreativitas, komunikasi, kerja tim, hingga pengambilan keputusan. Tujuannya, agar sebuah masalah dapat dipecahkan secara efektif berdasarkan data serta informasi yang akurat.

Proses Problem Solving

Dalam prosesnya, ada empat tahapan dasar problem solving , yakni:

1. Mengidentifikasi Masalah

Langkah pertama dalam proses problem solving adalah mendefinisikan sebuah masalah berdasarkan gejala yang ada. Pasalnya, sebuah masalah biasanya dipengaruhi oleh berbagai faktor.

Faktor-faktor tersebut harus diuraikan terlebih dahulu dengan cara identifikasi agar penyelesainnya dapat dilakukan dengan baik.

2. Menemukan Solusi Terbaik

Problem solving bertujuan untuk menemukan solusi terbaik atas sebuah masalah. Untuk mendapatkan hal tersebut, diperlukan pemahaman yang mendalam mengenai masalah tersebut agar dapat terselesaikan secara efektif.

3. Melakukan Evaluasi

Evaluasi merupakan tahap paling akhir dalam proses problem solving . Dalam tahap ini, solusi yang sudah diputuskan sebelumnya dapat diterapkan. Namun, hal tersebut tidak hanya sampai di situ saja, karena solusi tersebut juga harus ditindaklanjuti agar dapat menyelesaikan masalah secara menyeluruh.

Metode Problem Solving

1. brainstorming.

Brainstorming merupakan metode problem solving yang paling banyak digunakan oleh orang-orang. Pasalnya, metode ini efektif untuk digunakan sebagai pemecahan masalah melalui solusi kreatif.

Prosesnya adalah setiap orang harus menyampaikan ide-ide maupun pendapat yang kemudian dapat diolah menjadi satu solusi utama.

2. 6 Thinking Hats

Dalam metode ini, setiap orang akan mencoba memberikan penyelesaian terhadap suatu masalah dari beragam perspektif. Caranya adalah dengan mengelompokkan ide-ide yang ada ke dalam daftar pro-cons. Dengan begitu, kamu bisa melihat ide mana yang memiliki kelebihan yang paling banyak.

3. The 5 Whys

Metode ini dilakukan dengan cara meng-highlight masalah yang ingin dipecahkan. Kemudian, cari tahu jawaban mengenai “mengapa” masalah tersebut bisa terjadi sebanyak lima kali hingga kamu mendapatkan jawaban yang objektif tentang pertanyaanmu.

4. Lightning Decision Jam

Metode ini memungkinkanmu untuk menulis berbagai hal, mulai dari tantangan, kekhawatiran, hingga kesalahan dalam sebuah catatan kecil. Dengan hal tersebut, kamu bisa memilih masalah mana yang ingin diselesaikan terlebih dahulu dengan melihatnya dari sudut pandang baru. Dengan begitu, penyelesaian masalah dapat dilakukan secara tertatur.

5. Failure Mode and Effect Analysis

Terakhir, metode ini digunakan untuk menganalisis setiap elemen dari strategi bisnis serta kemungkinan-kemungkinan buruk yang akan terjadi. Dengan begitu, kamu bisa menemukan solusi dari masalahmu serta langkah preventif untuk mencegahnya secara lebih mudah.

Nah, itulah penjelasan mengenai problem solving . Dari penjelasan di atas, dapat diketahui bahwa problem solving merupakan kemampuan pemecahan masalah yang dilakukan dengan proses yang cukup panjang.

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Pentingnya Memiliki Skill Problem Solving dan Cara Meningkatkannya

Gulman azkiya.

January 20, 2023 • 11 minutes read

Artikel ini membahas mengenai kemampuan problem solving dan cara meningkatkannya. —

“Memiliki skill problem solving yang baik”

Jika kamu adalah seorang jobseeker , tentu tak akan asing dengan salah satu requirement atau kualifikasi di atas. Pasalnya, tak jarang hal tersebut menjadi persyaratan pada sebuah lowongan kerja.

Tetapi, apakah problem solving hanya sekedar artian dari penyelesaian masalah? Tentu saja tidak. Pasalnya, ada banyak langkah, metode, serta langkah dasar yang tercakup dalam proses  problem solving .

Dengan membaca artikel ini, kamu akan lebih bisa memahami apa itu kemampuan problem solving,  bagaimana cara menuntaskan masalah yang ada, serta cara meningkatkannya. Simak baik-baik!

Apa itu Problem Solving?

Dilansir dari asq.org, p roblem solving adalah sebuah tindakan penyelesaian masalah dengan melakukan beberapa tahapan mulai dari menentukan penyebab masalah, mengidentifikasi, memilih, dan menerapkan solusi.

Sederhananya, problem solving artinya kemampuan untuk menemukan solusi terbaik dari suatu masalah dengan mengidentifikasi penyebabnya.

Mengapa Kemampuan Problem Solving itu Penting?

Tidak dapat dipungkiri, semua orang pasti sering berhadapan dengan masalah. Permasalahan tersebut dapat muncul dari berbagai sisi seperti, urusan pribadi hingga pekerjaan. Setiap masalah juga memiliki tingkatannya masing-masing, mulai dari yang sederhana sampai kompleks.

Misalnya dalam aktivitas sehari-hari seperti pekerjaan, tak jarang apa yang kamu rencanakan pada saat bekerja tidak berjalan sesuai yang diharapkan (masalah).

“Wah, apakah ini termasuk ekspektasi vs realita?” Yap , ini bisa juga disebut sebuah masalah.

Dengan memiliki kemampuan problem solving, kamu dapat mengidentifikasi masalah tersebut, mencari tahu kenapa tidak berjalan sesuai dengan harapan, dan menentukan tindakan (solusi) untuk memperbaikinya. Pada akhirnya, kamu dapat mengontrol kehidupan jadi untuk menjadi lebih baik.

Kemampuan problem solving juga memungkinkan kamu untuk dapat memanfaatkan sebuah masalah menjadi peluang di kehidupan.

Misalnya ketika di tempat kerja, kamu dan anggota tim sedang mengalami sebuah masalah. Kemudian, kamu berinisiatif dengan menawarkan diri untuk mencari solusi terbaik atas masalah tersebut. Nah , ketika kamu dapat menyelesaikannya, maka dirimu bisa dianggap sebagai seorang problem solver.

Problem solver adalah orang yang dianggap sebagai pemecah masalah atau orang yang dapat diandalkan untuk menyelesaikan berbagai masalah. Sehingga akan berdampak positif bagi kinerja dan karier kamu.

4 Langkah Dasar dalam Problem Solving

Ketika hendak menyelesaikan sebuah masalah, berikut 4 langkah dasar yang harus kamu lakukan:

1. Cari tahu dan pahami masalah

Langkah pertama dalam melakukan problem solving adalah memahami situasi yang terjadi pada sebuah masalah. Cari tahu apa yang sebenarnya terjadi. Apakah hal tersebut benar-benar sebuah masalah atau tidak. Tak jarang pada sebuah masalah, kamu lebih terpaku dengan gejalanya daripada masalah yang sebenarnya.

Misalnya, kamu dan tim tidak pernah bersemangat dalam bekerja sehingga target harian tidak tercapai, hal ini adalah gejala. Semangat rendah tidak terjadi dengan sendirinya. Masalah yang mendasarinya mungkin berupa lembur yang berat, kebosanan, atau manajemen tim yang buruk. Masalah inilah yang seharusnya diselesaikan.

2. Menentukan akar masalah

Selanjutnya yaitu memastikan bahwa masalah yang ingin kamu selesaikan adalah masalah yang tepat. Pada tahap ini, kamu akan melihat lebih dalam dan mencoba menemukan akar masalah.

Dalam mencari akar sebuah masalah, kamu dapat mencoba menggunakan konsep 5 why . 5 why adalah proses menganalisa masalah dengan teknik tanya-jawab sederhana sebanyak 5 kali untuk menyelidiki hubungan sebab-akibat yang menjadi akar dari suatu permasalahan . Berikut contohnya:

Pada contoh di atas, kamu mencoba mencari akar permasalahan dengan mempertanyakan kenapa “pekerjaan saya sering tidak selesai tepat waktu”? Ternyata penyebabnya adalah karena tidak bersemangat. Kemudian kamu mempertanyakan lagi kenapa tidak bersemangat dan penyebabnya adalah sering mengantuk.

Setiap jawaban yang muncul akan kamu pertanyakan hingga menemukan penyebab yang tidak bisa dipertanyakan lagi atau bisa disebut akar masalah. Akar permasalahan pada contoh ini yaitu manajemen waktu yang buruk.

3. Mencari dan memilih solusi

Terkadang solusi muncul dari hal-hal yang tak terduga. Salah satunya dengan melakukan diskusi dengan anggota tim atau rekan kerjamu. Setelah menemukan akar masalah, cobalah menggali beberapa ide yang berkaitan dengan masalah tersebut.

Dengan saling berbagi pendapat terkait masalah yang dihadapi dapat menghasilkan beberapa solusi yang dibutuhkan. Selain itu, cari informasi sebanyak-banyaknya. Seperti bertanya kepada orang yang berpengalaman atau yang pernah memiliki masalah yang serupa.

Nantinya setelah menemukan beberapa solusi, selanjutnya adalah memilih solusi yang tepat. Karena tidak semua solusi yang ditemukan bisa diterapkan. Ada beberapa faktor yang mempengaruhi, mulai dari biaya, sumber daya manusia, dan dampak dari solusi itu sendiri (jangka panjang atau pendek).

Baca Juga: Mengenal Perbedaan Hard Skill dan Soft Skill (Beserta Contoh-contohnya)

4. Menerapkan solusi dan evaluasi

Setelah menentukan solusi yang akan digunakan, tahap selanjutnya adalah menerapkan solusi tersebut ke dalam masalah.

Misalnya, ketika motor kamu rusak akan ada 2 pilihan solusi dari masalah tersebut, melakukan service biasa (jangka pendek) atau mengganti spare part yang rusak (jangka panjang). Dengan berbagai pertimbangan, kamu akhirnya memutuskan untuk mengganti bagian yang rusak. Hal tersebut merupakan salah satu contoh sederhana dari penerapan solusi.

Selanjutnya, yaitu melakukan evaluasi dari solusi yang kamu terapkan. Dengan melakukan evaluasi, kamu bisa melihat seberapa efektif solusi tersebut, apakah bisa diterapkan pada semua masalah atau tidak.

Selain itu, dengan melakukan evaluasi kamu akan bisa mengatur strategi problem solving selanjutnya untuk permasalahan yang serupa.

11 Metode Problem Solving untuk Menyelesaikan Masalah

Sebetulnya ada beragam metode  problem solving yang bisa kamu gunakan. Ada 11 metode problem solving yang paling efektif, yaitu:

1. Ajukan pertanyaan terlebih dahulu

Ini merupakan tahap pra-pemecahan masalah. Jadi, sebelum mencari solusi, kamu bisa bertanya pada diri sendiri untuk mencari berbagai skenario dalam pencarian keputusan. 

Contoh beberapa pertanyaannya adalah, “Bagaimana jika…”, “Mengapa tidak…..”, “Bisakah kita…..”, dan lain sebagainya.

2. Temukan  poin of view dari orang lain

Apakah kamu termasuk orang yang terlalu fokus dengan pemikiran dan sudut pandang diri sendiri? Jika iya, maka proses  problem solving biasanya akan lebih sulit.

Pasalnya, jika terlalu fokus pada diri sendiri, kamu tidak akan mendapat gambaran lain yang lebih luas. Misal, kamu berjualan sebuah produk makanan. Segala strategi marketing sudah kamu jalankan, tetapi penjualan terus menurun dari bulan ke bulan.

Dalam kasus tersebut, kmau bisa saja berpikir bahwa produkmu kurang enak. Namun, bagaimana jika ternyata alasannya bukan hanya itu? So , carilah sudut pandang yang lain.

Kamu bisa coba mengadakan survey dan bertanya pada target pasar, apa hal-hal yang harus diperbaiki dari produk tersebut.

3. Lakukan brainstorming

brainsotrming adalah sesei mencurahkan pendapat untuk mencari ide-ide segar dalam proses pemecahan masalah.

Agar berjalan dengan lancar, sesi ini harus dilakukan dalam lingkungan yang ramah dan tidak menghakimi. 

Jika semua orang dalam forum sudah menuangkan ide mereka, maka carilah satu ide paling baik dan juga rasional untuk membantu proses  problem solving .

4. Brainstorming dengan teknik “The Round-Robin”

Brainstorming pada poin sebelumnya disebut juga dengan sesi brainstorming tradisional. Jika metode tersebut tidak berhasil, kamu dapat mencoba the round-robin brainstorming .

Secara sederhana, teknik ini akan menuntut setiap peserta untuk terlibat aktif dalam sesi brainstorming. Teknisnya:

– Peserta bergiliran menyumbangkan ide, jika belum ada ide, maka harus menyebut “pass (lewati dulu)”.

– Sesi brainstorming selesai setelah semua orang lulus.

5. Teknik “silent” brainstorming

Salah satu maslaah yang paling banyak dihadapi dalam sesi brainstorming adalah, kemungkinan besar forum akan lebih mendengarkan peserta yang “aktif” dan dianggap keren. Padahal, bisa jadi anggota kelompok lain memiliki ide yang bagus, tetapi mereka enggan untuk mengungkapkannya.

Dengan metode “silent” brainstorming, setiap individu akan diajak untuk menyumbangkan ide mereka, dengan catatan setiap ide akan dianggap memiliki “bobot” yang sama. Teknik ini akan lebih baik jika dilakukan dalam metode  online .

6. Six-thinking hats

Metode ini mengajak setiap anggota kelompok untuk berpikir menggunakan enam “topi” yang berbeda (white, red, black, yellow, green, blue) untuk mengevaluasi masalah dari berbagai sudut pandang.

Teknik ini diciptakan oleh Edward de Bono, rinciannya adalah:

1. Topi putih dianggap netral untuk mengungkapkan fakta dan angka 2. Topi merah untuk menunjukkan emosi dan intuisi, sehingga peserta dapat menyampaikan ide-ide secara naluriah 3. Topi hitam untuk memperlihatkan kehati-hatian terhadap sudut pandang kritis 4. Topi kuning sebagai penyeimbang untuk topi hitam. Ini digunakan ketika peserta ingin mengidentifikasi hal yang positif dari setiap ide yang ada. 5. Topi hijau untuk mengeksplorasi kreativitas, kemungkinan, alternatif, dan ide segar 6. Topi biru untuk mengatur semua hal yang terlibat dalam proses pengambilan keputusan.

7. Konsep 5 Whys

Seperti yang sudah kita bahas sebelumnya, metode ini mengajak setiap peserta untuk mengidentifikasi akar masalah dengan mengajukan pertanyaan “kenapa” sebanyak lima kali.

8. Failure mode and effect analysis (FMEA)

Ingin mengevaluasi potensi kegagalan dari suatu sistem atau proses dan mencari solusi untuk mencegah atau mengurangi dampak negatif dari kegagalan tersebut? Maka gunakanlah teknik FMEA,  guys .

9. Teknik wanderer problem solving

Ketika menghadapi sebuah masalah, ada baiknya jika kamu menjauh sejenak dari masalah tersebut. Hal ini memungkinkan kamu untuk berada dalam kondisi otak yang lebih rileks.

Artinya, ketika kembali pada masalah yang akan dipecahkan, mungkin kamu akan “melihat” masalah tersebut dengan sudut pandang yang baru sehingga  problem solving akan semakin mudah.

10. Sisakan ruang untuk imajinasi

Alih-alih menyelesaikan masalah dengan fakta, bukan hal yang tak mungkin ketika seseorang ingin melakukan problem solving dengan ruang iamjinasi dan kreativitas.

Dalam hal ini, kamu bisa mencari suatu hal yang dianggap dapat mengganggu kreativitasmu saat mencari ide,  guys .

11. Wrapping up

Melalui teknik ini, kamu dapat melihat bahwa masalah terjadi setiap saat dan akan terus terjadi di masa-masa selanjutnya. 

Pikirkanlan bahwa setiap masalah akan memberikan informasi tentang hal-hal yang harus kita perbaiki. Selain itu, masalah yang datang akan membawa kita lebih terbuka terhadap konflik sehingga kita akan lebih terbiasa dalam proses  problem solving .

Cara Meningkatkan Kemampuan Problem Solving

Sama halnya dengan soft skill lainya, kemampuan problem solving muncul dari pengalaman dan juga latihan. Kamu masih bisa melatihnya dan mengembangkan kemampuan tersebut.

Bagi kamu yang merasa kurang dalam ability yang satu ini, jangan berkecil hati. Berikut adalah beberapa cara yang dapat kamu terapkan untuk meningkatkan atau melatih kemampuan problem solving :

1. Ubah pola pikir

Tidak ada satupun orang ingin menghadapi sesuatu yang membuat frustasi, melelahkan, atau tampaknya tidak mungkin? Ya, kamu mungkin juga tidak mau.

Tetapi, daripada mencoba menghindarinya, kamu dapat melihat masalah tersebut secara positif dengan menganggapnya sebagai kesempatan untuk meningkatkan kemampuan.

Oleh sebab itu, mengubah pola pikir dapat membuat kamu merasa tidak terbebani, sehingga dapat menganalisis masalah dengan baik.

2. Membiasakan diri menggunakan mind mapping

Dikutip dari mindmeister.com, bahwa dengan memvisualisasikan tujuan, masalah, ide, dan poin tindakan dalam mind mapping dapat membantu seseorang untuk melihat gambaran yang lebih besar hingga menemukan ide-ide yang mungkin terlewatkan.

Mind mapping juga dapat mengasah pola pikir untuk menyusun konsep kreatif untuk membantumu menemukan solusi dengan lebih mudah.

3. Lakukan secara bertahap

Ketika menghadapi sebuah masalah, jangan langsung fokus untuk mencari solusi akhir. Coba untuk melakukannya secara bertahap.

Dimulai dengan mengidentifikasi masalah, mencari penyebab, dan menemukan solusi yang tepat. Dengan melakukan secara bertahap akan melatih kemampuan berpikir strategis dan dapat mengambil keputusan yang sesuai.

4. Jangan sungkan meminta bantuan

Selalu beranikan diri kamu untuk meminta bantuan pada orang terdekat. Ketika kamu sudah tidak dapat lagi mencari jalan keluar dari masalah yang dihadapi, bertanya kepada teman atau rekan kerja mungkin bisa menjadi solusi.

Berdiskusi dengan orang lain dapat membawa ide-ide segar dan sudut pandang yang tidak akan pernah kamu kembangkan sendiri.

Namun, kamu perlu mengetahui orang yang tepat untuk meminta bantuan/berdiskusi. Jangan lupa untuk menyesuaikan konteks masalahnya dengan orang tersebut.

Jika kamu memiliki masalah mengenai pekerjaan di bidang pemasaran, rekan dari tim marketing mungkin dapat menjadi orang yang tepat untuk dimintai pendapatnya.

5. Nikmati setiap proses

Terkadang karena terlalu fokus pada upaya penyelesaian masalah, membuat kita tidak menyadari apa saja yang telah dilakukan selama proses tersebut. Coba untuk menikmati setiap tahapan yang kamu lakukan dan rasakan dampak baiknya untuk pribadi atau lingkungan sekitar.

Ketika kamu dapat menikmatinya, kamu akan lebih dapat menghargai kemampuan yang ada pada dirimu. Jadi, ketika sedang menghadapi masalah, kamu sudah mengetahui seberapa besar kemampuanmu untuk menyelesaikan masalah tersebut.

Bagaimana guys? Pastinya kamu sudah lebih paham dong, mengenai skill problem solving . Selain dapat meningkatkan performa di tempat kerja, dengan memiliki kemampuan tersebut, kamu juga dapat berpikir secara strategis pada setiap masalah yang dihadapi. Itulah sedikit penjelasan mengenai apa itu problem solving .

Bagi kamu yang ingin mengetahui lebih dalam, langsung saja menuju Skill Academy . Karena di Skill Academy tersedia berbagai kelas pelatihan mulai dari hard skill hingga soft skill seperti problem solving . Yuk , terus kembangkan kemampuanmu!

ASQ.org (2021). What Is Problem Solving?.https://asq.org/quality-resources/problem-solving#Process [Daring]. (Diakses, 3 Mei 2021)

Hall, John (2019). 6 Techniques to Better Your Problem-Solving Skills. https://www.inc.com/john-hall/6-techniques-to-better-your-problem-solving-skills.html [Daring]. (Diakses, 4 Mei 2021)

Stottler, Wayne (2017). What is problem solving and why is it important. https://www.kepner-tregoe.com/blog/what-is-problem-solving-and-why-is-it-important/ [Daring]. (Diakses, 3 Mei 2021)

Smart, James (2020). How to improve your problem solving skills and build effective problem solving strategies. https://www.sessionlab.com/blog/problem-solving-skills-and-strategies/ [Daring]. (Diakses, 4 Mei 2021)

Živkovic, Mile (2020). 11 Brilliant Problem Solving Techniques Nobody Taught You. https://www.chanty.com/blog/problem-solving-techniques/ [Daring]. (Diakses, 20 Januari 2023)

Artikel ini telah diperbarui oleh Intan Aulia Husnunnisa pada tanggal 20 Januari 2023.

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How to master the seven-step problem-solving process

In this episode of the McKinsey Podcast , Simon London speaks with Charles Conn, CEO of venture-capital firm Oxford Sciences Innovation, and McKinsey senior partner Hugo Sarrazin about the complexities of different problem-solving strategies.

Podcast transcript

Simon London: Hello, and welcome to this episode of the McKinsey Podcast , with me, Simon London. What’s the number-one skill you need to succeed professionally? Salesmanship, perhaps? Or a facility with statistics? Or maybe the ability to communicate crisply and clearly? Many would argue that at the very top of the list comes problem solving: that is, the ability to think through and come up with an optimal course of action to address any complex challenge—in business, in public policy, or indeed in life.

Looked at this way, it’s no surprise that McKinsey takes problem solving very seriously, testing for it during the recruiting process and then honing it, in McKinsey consultants, through immersion in a structured seven-step method. To discuss the art of problem solving, I sat down in California with McKinsey senior partner Hugo Sarrazin and also with Charles Conn. Charles is a former McKinsey partner, entrepreneur, executive, and coauthor of the book Bulletproof Problem Solving: The One Skill That Changes Everything [John Wiley & Sons, 2018].

Charles and Hugo, welcome to the podcast. Thank you for being here.

Hugo Sarrazin: Our pleasure.

Charles Conn: It’s terrific to be here.

Simon London: Problem solving is a really interesting piece of terminology. It could mean so many different things. I have a son who’s a teenage climber. They talk about solving problems. Climbing is problem solving. Charles, when you talk about problem solving, what are you talking about?

Charles Conn: For me, problem solving is the answer to the question “What should I do?” It’s interesting when there’s uncertainty and complexity, and when it’s meaningful because there are consequences. Your son’s climbing is a perfect example. There are consequences, and it’s complicated, and there’s uncertainty—can he make that grab? I think we can apply that same frame almost at any level. You can think about questions like “What town would I like to live in?” or “Should I put solar panels on my roof?”

You might think that’s a funny thing to apply problem solving to, but in my mind it’s not fundamentally different from business problem solving, which answers the question “What should my strategy be?” Or problem solving at the policy level: “How do we combat climate change?” “Should I support the local school bond?” I think these are all part and parcel of the same type of question, “What should I do?”

I’m a big fan of structured problem solving. By following steps, we can more clearly understand what problem it is we’re solving, what are the components of the problem that we’re solving, which components are the most important ones for us to pay attention to, which analytic techniques we should apply to those, and how we can synthesize what we’ve learned back into a compelling story. That’s all it is, at its heart.

I think sometimes when people think about seven steps, they assume that there’s a rigidity to this. That’s not it at all. It’s actually to give you the scope for creativity, which often doesn’t exist when your problem solving is muddled.

Simon London: You were just talking about the seven-step process. That’s what’s written down in the book, but it’s a very McKinsey process as well. Without getting too deep into the weeds, let’s go through the steps, one by one. You were just talking about problem definition as being a particularly important thing to get right first. That’s the first step. Hugo, tell us about that.

Hugo Sarrazin: It is surprising how often people jump past this step and make a bunch of assumptions. The most powerful thing is to step back and ask the basic questions—“What are we trying to solve? What are the constraints that exist? What are the dependencies?” Let’s make those explicit and really push the thinking and defining. At McKinsey, we spend an enormous amount of time in writing that little statement, and the statement, if you’re a logic purist, is great. You debate. “Is it an ‘or’? Is it an ‘and’? What’s the action verb?” Because all these specific words help you get to the heart of what matters.

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Simon London: So this is a concise problem statement.

Hugo Sarrazin: Yeah. It’s not like “Can we grow in Japan?” That’s interesting, but it is “What, specifically, are we trying to uncover in the growth of a product in Japan? Or a segment in Japan? Or a channel in Japan?” When you spend an enormous amount of time, in the first meeting of the different stakeholders, debating this and having different people put forward what they think the problem definition is, you realize that people have completely different views of why they’re here. That, to me, is the most important step.

Charles Conn: I would agree with that. For me, the problem context is critical. When we understand “What are the forces acting upon your decision maker? How quickly is the answer needed? With what precision is the answer needed? Are there areas that are off limits or areas where we would particularly like to find our solution? Is the decision maker open to exploring other areas?” then you not only become more efficient, and move toward what we call the critical path in problem solving, but you also make it so much more likely that you’re not going to waste your time or your decision maker’s time.

How often do especially bright young people run off with half of the idea about what the problem is and start collecting data and start building models—only to discover that they’ve really gone off half-cocked.

Hugo Sarrazin: Yeah.

Charles Conn: And in the wrong direction.

Simon London: OK. So step one—and there is a real art and a structure to it—is define the problem. Step two, Charles?

Charles Conn: My favorite step is step two, which is to use logic trees to disaggregate the problem. Every problem we’re solving has some complexity and some uncertainty in it. The only way that we can really get our team working on the problem is to take the problem apart into logical pieces.

What we find, of course, is that the way to disaggregate the problem often gives you an insight into the answer to the problem quite quickly. I love to do two or three different cuts at it, each one giving a bit of a different insight into what might be going wrong. By doing sensible disaggregations, using logic trees, we can figure out which parts of the problem we should be looking at, and we can assign those different parts to team members.

Simon London: What’s a good example of a logic tree on a sort of ratable problem?

Charles Conn: Maybe the easiest one is the classic profit tree. Almost in every business that I would take a look at, I would start with a profit or return-on-assets tree. In its simplest form, you have the components of revenue, which are price and quantity, and the components of cost, which are cost and quantity. Each of those can be broken out. Cost can be broken into variable cost and fixed cost. The components of price can be broken into what your pricing scheme is. That simple tree often provides insight into what’s going on in a business or what the difference is between that business and the competitors.

If we add the leg, which is “What’s the asset base or investment element?”—so profit divided by assets—then we can ask the question “Is the business using its investments sensibly?” whether that’s in stores or in manufacturing or in transportation assets. I hope we can see just how simple this is, even though we’re describing it in words.

When I went to work with Gordon Moore at the Moore Foundation, the problem that he asked us to look at was “How can we save Pacific salmon?” Now, that sounds like an impossible question, but it was amenable to precisely the same type of disaggregation and allowed us to organize what became a 15-year effort to improve the likelihood of good outcomes for Pacific salmon.

Simon London: Now, is there a danger that your logic tree can be impossibly large? This, I think, brings us onto the third step in the process, which is that you have to prioritize.

Charles Conn: Absolutely. The third step, which we also emphasize, along with good problem definition, is rigorous prioritization—we ask the questions “How important is this lever or this branch of the tree in the overall outcome that we seek to achieve? How much can I move that lever?” Obviously, we try and focus our efforts on ones that have a big impact on the problem and the ones that we have the ability to change. With salmon, ocean conditions turned out to be a big lever, but not one that we could adjust. We focused our attention on fish habitats and fish-harvesting practices, which were big levers that we could affect.

People spend a lot of time arguing about branches that are either not important or that none of us can change. We see it in the public square. When we deal with questions at the policy level—“Should you support the death penalty?” “How do we affect climate change?” “How can we uncover the causes and address homelessness?”—it’s even more important that we’re focusing on levers that are big and movable.

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Simon London: Let’s move swiftly on to step four. You’ve defined your problem, you disaggregate it, you prioritize where you want to analyze—what you want to really look at hard. Then you got to the work plan. Now, what does that mean in practice?

Hugo Sarrazin: Depending on what you’ve prioritized, there are many things you could do. It could be breaking the work among the team members so that people have a clear piece of the work to do. It could be defining the specific analyses that need to get done and executed, and being clear on time lines. There’s always a level-one answer, there’s a level-two answer, there’s a level-three answer. Without being too flippant, I can solve any problem during a good dinner with wine. It won’t have a whole lot of backing.

Simon London: Not going to have a lot of depth to it.

Hugo Sarrazin: No, but it may be useful as a starting point. If the stakes are not that high, that could be OK. If it’s really high stakes, you may need level three and have the whole model validated in three different ways. You need to find a work plan that reflects the level of precision, the time frame you have, and the stakeholders you need to bring along in the exercise.

Charles Conn: I love the way you’ve described that, because, again, some people think of problem solving as a linear thing, but of course what’s critical is that it’s iterative. As you say, you can solve the problem in one day or even one hour.

Charles Conn: We encourage our teams everywhere to do that. We call it the one-day answer or the one-hour answer. In work planning, we’re always iterating. Every time you see a 50-page work plan that stretches out to three months, you know it’s wrong. It will be outmoded very quickly by that learning process that you described. Iterative problem solving is a critical part of this. Sometimes, people think work planning sounds dull, but it isn’t. It’s how we know what’s expected of us and when we need to deliver it and how we’re progressing toward the answer. It’s also the place where we can deal with biases. Bias is a feature of every human decision-making process. If we design our team interactions intelligently, we can avoid the worst sort of biases.

Simon London: Here we’re talking about cognitive biases primarily, right? It’s not that I’m biased against you because of your accent or something. These are the cognitive biases that behavioral sciences have shown we all carry around, things like anchoring, overoptimism—these kinds of things.

Both: Yeah.

Charles Conn: Availability bias is the one that I’m always alert to. You think you’ve seen the problem before, and therefore what’s available is your previous conception of it—and we have to be most careful about that. In any human setting, we also have to be careful about biases that are based on hierarchies, sometimes called sunflower bias. I’m sure, Hugo, with your teams, you make sure that the youngest team members speak first. Not the oldest team members, because it’s easy for people to look at who’s senior and alter their own creative approaches.

Hugo Sarrazin: It’s helpful, at that moment—if someone is asserting a point of view—to ask the question “This was true in what context?” You’re trying to apply something that worked in one context to a different one. That can be deadly if the context has changed, and that’s why organizations struggle to change. You promote all these people because they did something that worked well in the past, and then there’s a disruption in the industry, and they keep doing what got them promoted even though the context has changed.

Simon London: Right. Right.

Hugo Sarrazin: So it’s the same thing in problem solving.

Charles Conn: And it’s why diversity in our teams is so important. It’s one of the best things about the world that we’re in now. We’re likely to have people from different socioeconomic, ethnic, and national backgrounds, each of whom sees problems from a slightly different perspective. It is therefore much more likely that the team will uncover a truly creative and clever approach to problem solving.

Simon London: Let’s move on to step five. You’ve done your work plan. Now you’ve actually got to do the analysis. The thing that strikes me here is that the range of tools that we have at our disposal now, of course, is just huge, particularly with advances in computation, advanced analytics. There’s so many things that you can apply here. Just talk about the analysis stage. How do you pick the right tools?

Charles Conn: For me, the most important thing is that we start with simple heuristics and explanatory statistics before we go off and use the big-gun tools. We need to understand the shape and scope of our problem before we start applying these massive and complex analytical approaches.

Simon London: Would you agree with that?

Hugo Sarrazin: I agree. I think there are so many wonderful heuristics. You need to start there before you go deep into the modeling exercise. There’s an interesting dynamic that’s happening, though. In some cases, for some types of problems, it is even better to set yourself up to maximize your learning. Your problem-solving methodology is test and learn, test and learn, test and learn, and iterate. That is a heuristic in itself, the A/B testing that is used in many parts of the world. So that’s a problem-solving methodology. It’s nothing different. It just uses technology and feedback loops in a fast way. The other one is exploratory data analysis. When you’re dealing with a large-scale problem, and there’s so much data, I can get to the heuristics that Charles was talking about through very clever visualization of data.

You test with your data. You need to set up an environment to do so, but don’t get caught up in neural-network modeling immediately. You’re testing, you’re checking—“Is the data right? Is it sound? Does it make sense?”—before you launch too far.

Simon London: You do hear these ideas—that if you have a big enough data set and enough algorithms, they’re going to find things that you just wouldn’t have spotted, find solutions that maybe you wouldn’t have thought of. Does machine learning sort of revolutionize the problem-solving process? Or are these actually just other tools in the toolbox for structured problem solving?

Charles Conn: It can be revolutionary. There are some areas in which the pattern recognition of large data sets and good algorithms can help us see things that we otherwise couldn’t see. But I do think it’s terribly important we don’t think that this particular technique is a substitute for superb problem solving, starting with good problem definition. Many people use machine learning without understanding algorithms that themselves can have biases built into them. Just as 20 years ago, when we were doing statistical analysis, we knew that we needed good model definition, we still need a good understanding of our algorithms and really good problem definition before we launch off into big data sets and unknown algorithms.

Simon London: Step six. You’ve done your analysis.

Charles Conn: I take six and seven together, and this is the place where young problem solvers often make a mistake. They’ve got their analysis, and they assume that’s the answer, and of course it isn’t the answer. The ability to synthesize the pieces that came out of the analysis and begin to weave those into a story that helps people answer the question “What should I do?” This is back to where we started. If we can’t synthesize, and we can’t tell a story, then our decision maker can’t find the answer to “What should I do?”

Simon London: But, again, these final steps are about motivating people to action, right?

Charles Conn: Yeah.

Simon London: I am slightly torn about the nomenclature of problem solving because it’s on paper, right? Until you motivate people to action, you actually haven’t solved anything.

Charles Conn: I love this question because I think decision-making theory, without a bias to action, is a waste of time. Everything in how I approach this is to help people take action that makes the world better.

Simon London: Hence, these are absolutely critical steps. If you don’t do this well, you’ve just got a bunch of analysis.

Charles Conn: We end up in exactly the same place where we started, which is people speaking across each other, past each other in the public square, rather than actually working together, shoulder to shoulder, to crack these important problems.

Simon London: In the real world, we have a lot of uncertainty—arguably, increasing uncertainty. How do good problem solvers deal with that?

Hugo Sarrazin: At every step of the process. In the problem definition, when you’re defining the context, you need to understand those sources of uncertainty and whether they’re important or not important. It becomes important in the definition of the tree.

You need to think carefully about the branches of the tree that are more certain and less certain as you define them. They don’t have equal weight just because they’ve got equal space on the page. Then, when you’re prioritizing, your prioritization approach may put more emphasis on things that have low probability but huge impact—or, vice versa, may put a lot of priority on things that are very likely and, hopefully, have a reasonable impact. You can introduce that along the way. When you come back to the synthesis, you just need to be nuanced about what you’re understanding, the likelihood.

Often, people lack humility in the way they make their recommendations: “This is the answer.” They’re very precise, and I think we would all be well-served to say, “This is a likely answer under the following sets of conditions” and then make the level of uncertainty clearer, if that is appropriate. It doesn’t mean you’re always in the gray zone; it doesn’t mean you don’t have a point of view. It just means that you can be explicit about the certainty of your answer when you make that recommendation.

Simon London: So it sounds like there is an underlying principle: “Acknowledge and embrace the uncertainty. Don’t pretend that it isn’t there. Be very clear about what the uncertainties are up front, and then build that into every step of the process.”

Hugo Sarrazin: Every step of the process.

Simon London: Yeah. We have just walked through a particular structured methodology for problem solving. But, of course, this is not the only structured methodology for problem solving. One that is also very well-known is design thinking, which comes at things very differently. So, Hugo, I know you have worked with a lot of designers. Just give us a very quick summary. Design thinking—what is it, and how does it relate?

Hugo Sarrazin: It starts with an incredible amount of empathy for the user and uses that to define the problem. It does pause and go out in the wild and spend an enormous amount of time seeing how people interact with objects, seeing the experience they’re getting, seeing the pain points or joy—and uses that to infer and define the problem.

Simon London: Problem definition, but out in the world.

Hugo Sarrazin: With an enormous amount of empathy. There’s a huge emphasis on empathy. Traditional, more classic problem solving is you define the problem based on an understanding of the situation. This one almost presupposes that we don’t know the problem until we go see it. The second thing is you need to come up with multiple scenarios or answers or ideas or concepts, and there’s a lot of divergent thinking initially. That’s slightly different, versus the prioritization, but not for long. Eventually, you need to kind of say, “OK, I’m going to converge again.” Then you go and you bring things back to the customer and get feedback and iterate. Then you rinse and repeat, rinse and repeat. There’s a lot of tactile building, along the way, of prototypes and things like that. It’s very iterative.

Simon London: So, Charles, are these complements or are these alternatives?

Charles Conn: I think they’re entirely complementary, and I think Hugo’s description is perfect. When we do problem definition well in classic problem solving, we are demonstrating the kind of empathy, at the very beginning of our problem, that design thinking asks us to approach. When we ideate—and that’s very similar to the disaggregation, prioritization, and work-planning steps—we do precisely the same thing, and often we use contrasting teams, so that we do have divergent thinking. The best teams allow divergent thinking to bump them off whatever their initial biases in problem solving are. For me, design thinking gives us a constant reminder of creativity, empathy, and the tactile nature of problem solving, but it’s absolutely complementary, not alternative.

Simon London: I think, in a world of cross-functional teams, an interesting question is do people with design-thinking backgrounds really work well together with classical problem solvers? How do you make that chemistry happen?

Hugo Sarrazin: Yeah, it is not easy when people have spent an enormous amount of time seeped in design thinking or user-centric design, whichever word you want to use. If the person who’s applying classic problem-solving methodology is very rigid and mechanical in the way they’re doing it, there could be an enormous amount of tension. If there’s not clarity in the role and not clarity in the process, I think having the two together can be, sometimes, problematic.

The second thing that happens often is that the artifacts the two methodologies try to gravitate toward can be different. Classic problem solving often gravitates toward a model; design thinking migrates toward a prototype. Rather than writing a big deck with all my supporting evidence, they’ll bring an example, a thing, and that feels different. Then you spend your time differently to achieve those two end products, so that’s another source of friction.

Now, I still think it can be an incredibly powerful thing to have the two—if there are the right people with the right mind-set, if there is a team that is explicit about the roles, if we’re clear about the kind of outcomes we are attempting to bring forward. There’s an enormous amount of collaborativeness and respect.

Simon London: But they have to respect each other’s methodology and be prepared to flex, maybe, a little bit, in how this process is going to work.

Hugo Sarrazin: Absolutely.

Simon London: The other area where, it strikes me, there could be a little bit of a different sort of friction is this whole concept of the day-one answer, which is what we were just talking about in classical problem solving. Now, you know that this is probably not going to be your final answer, but that’s how you begin to structure the problem. Whereas I would imagine your design thinkers—no, they’re going off to do their ethnographic research and get out into the field, potentially for a long time, before they come back with at least an initial hypothesis.

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Hugo Sarrazin: That is a great callout, and that’s another difference. Designers typically will like to soak into the situation and avoid converging too quickly. There’s optionality and exploring different options. There’s a strong belief that keeps the solution space wide enough that you can come up with more radical ideas. If there’s a large design team or many designers on the team, and you come on Friday and say, “What’s our week-one answer?” they’re going to struggle. They’re not going to be comfortable, naturally, to give that answer. It doesn’t mean they don’t have an answer; it’s just not where they are in their thinking process.

Simon London: I think we are, sadly, out of time for today. But Charles and Hugo, thank you so much.

Charles Conn: It was a pleasure to be here, Simon.

Hugo Sarrazin: It was a pleasure. Thank you.

Simon London: And thanks, as always, to you, our listeners, for tuning into this episode of the McKinsey Podcast . If you want to learn more about problem solving, you can find the book, Bulletproof Problem Solving: The One Skill That Changes Everything , online or order it through your local bookstore. To learn more about McKinsey, you can of course find us at McKinsey.com.

Charles Conn is CEO of Oxford Sciences Innovation and an alumnus of McKinsey’s Sydney office. Hugo Sarrazin is a senior partner in the Silicon Valley office, where Simon London, a member of McKinsey Publishing, is also based.

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Masalah dapat didefinisikan sebagai situasi atau tantangan yang memerlukan tindakan atau pemecahan untuk mencapai tujuan yang diinginkan. Dalam hal ini, masalah dapat didefinisikan sebagai proses kognitif yang melibatkan identifikasi, pemahaman, dan penyelesaian suatu masalah.

Proses penyelesaian masalah dimulai dengan pengenalan masalah, kemudian analisis masalah untuk mengetahui penyebabnya dan solusi yang mungkin. Setelah itu, langkah-langkah konkret diambil untuk menerapkan solusi tersebut, dan hasilnya dievaluasi untuk memastikan bahwa masalah telah diselesaikan secara efektif.

Dalam penyelesaian masalah, berbagai keterampilan dapat diperlukan, termasuk kreativitas, pemikiran kritis, pengambilan keputusan, dan kemampuan untuk membangun dan menguji solusi. Ini adalah proses penting dalam kehidupan sehari-hari, baik dalam konteks profesional maupun pribadi. Kemampuan untuk mengatasi masalah dengan efektif dapat membantu seseorang mengatasi masalah, mencapai tujuan, dan membuat keputusan yang lebih baik.

Bagaimana Proses Problem Solving Terjadi?

Untuk mengatasi masalah atau situasi tantangan, seringkali seseorang menggunakan proses penyelesaian masalah. Pada tahap pertama, masalah diidentifikasi. Ini berarti masalah dikenali dengan jelas. Setelah itu, analisis masalah dilakukan untuk memahami sumber masalah, serta akibatnya. 

Pada tahap ketiga, ide kreatif digunakan untuk menghasilkan berbagai alternatif solusi. Setelah itu, evaluasi solusi dilakukan untuk menentukan solusi terbaik berdasarkan hasilnya. Tahap berikutnya adalah menerapkan solusi melalui rencana tindakan yang jelas, dan terakhir, evaluasi hasilnya. 

Proses penyelesaian masalah membantu orang mengatasi masalah dengan cara yang terorganisir dan efektif, menghasilkan solusi yang lebih baik, dan membuat keputusan yang lebih baik.

Manfaat Problem Solving

Delapan berikut adalah manfaat utama dari memiliki kemampuan menyelesaikan masalah yang perlu kamu tau:

1. Peningkatan Kemampuan Pemecahan Masalah  

Manfaat utama problem solving adalah kemampuan untuk mengatasi masalah dengan lebih efektif. Seseorang yang telah memiliki kemampuan pemecahan masalah akan dapat menghadapi tantangan dengan lebih percaya diri, mencari solusi yang lebih baik, dan mengurangi tingkat stres yang dihadapi ketika menghadapi masalah.

2. Meningkatkan Kemampuan Pengambilan Keputusan

Proses analisis dan evaluasi yang dikenal sebagai penyelesaian masalah membantu orang membuat keputusan yang lebih baik dalam kehidupan pribadi dan profesional, seperti memilih karir, investasi, atau keputusan-keputusan penting lain dalam hidup.

3. Meningkatkan Kreativitas 

Saat menghadapi masalah, seseorang seringkali harus berpikir kreatif untuk menemukan cara baru untuk menyelesaikannya. Hal ini dapat membantu meningkatkan kemampuan kreatif dan inovasi.

4. Meningkatkan Komunikasi 

Untuk meningkatkan kemampuan komunikasi interpersonal, penyelesaian masalah sering melibatkan kerja tim, di mana orang harus berkomunikasi dan bekerja sama dengan orang lain.

5. Meningkatkan Produktivitas

Dengan memecahkan masalah secara efektif, individu dan kelompok dapat meningkatkan produktivitas dan efisiensi, yang berkontribusi pada pencapaian tujuan dan hasil yang diinginkan.

6. Meningkatkan Kepercayaan Diri 

Mengatasi masalah dengan sukses dapat meningkatkan kepercayaan diri seseorang. Ini karena mereka sadar bahwa mereka memiliki kemampuan untuk menghadapi tantangan.

7. Pengembangan Karier

Dalam konteks karir, kemampuan pemecahan masalah sangat dihargai. Orang yang memiliki kemampuan pemecahan masalah yang baik memiliki kemungkinan lebih besar untuk mencapai kesuksesan di tempat kerja.

8. Meningkatkan Kualitas Hidup 

Kemampuan menyelesaikan masalah dapat meningkatkan kualitas hidup seseorang. Ini karena kemampuan pemecahan masalah memungkinkan orang untuk mengatasi masalah yang mungkin menghalangi mereka dari mencapai tujuan dan kebahagiaan pribadi mereka.

Oleh karena itu, mempelajari kemampuan menyelesaikan masalah adalah langkah yang bagus untuk membangun diri sendiri dan meningkatkan kualitas hidup secara keseluruhan.

Penerapan Problem Solving di Kehidupan

Dalam kehidupan sehari-hari, memecahkan masalah berarti mengatasi berbagai situasi dan masalah. Pertama-tama, penting untuk mengidentifikasi masalah dengan jelas. Ini berarti merumuskan masalah dengan tepat, menemukan sumbernya, dan memahami bagaimana masalah tersebut akan mempengaruhi kehidupan kita. Misalnya, beban kerja yang berlebihan adalah masalah jika seseorang mengalami stres karena terlalu banyak tugas yang harus mereka selesaikan.

Analisis dilakukan setelah masalah ditemukan. Ini mencakup mengumpulkan informasi, memikirkan solusi yang mungkin, dan memahami akibat dari setiap solusi. Orang mungkin perlu mempertimbangkan contoh di atas atau meminta bantuan rekan kerja.

Selanjutnya, langkah ketiga adalah membuat dan menerapkan solusi. Ini mencakup membuat rencana tindakan yang jelas, mengambil tindakan konkrit untuk mengatasi masalah, dan dengan konsisten mengikuti rencana tersebut. Mengatur prioritas tugas, menggunakan alat manajemen waktu, atau berbicara dengan atasan tentang cara memberikan tugas yang lebih seimbang adalah beberapa solusi untuk beban kerja yang berlebihan.

Terakhir, refleksi dan evaluasi adalah langkah penting dalam menyelesaikan masalah. Setelah penerapan solusi, sangat penting untuk menilai apakah masalah telah diselesaikan dengan baik dan apakah solusi itu efektif. Jika hasil yang diinginkan belum dicapai, orang harus siap untuk merevisi rencana dan mencari solusi yang lebih baik atau perbaikan.

Problem solving membantu orang mengatasi masalah dengan lebih baik, mengurangi stres, meningkatkan kualitas hidup, dan membuat keputusan yang lebih baik. Ini juga membantu mereka tumbuh dalam keterampilan penting yang mereka miliki secara pribadi dan profesional. Problem solving dapat menjadi alat yang kuat untuk menghadapi masalah dalam kehidupan sehari-hari jika dilakukan dengan cara yang sistematis dan berpikir kritis.

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Problem Solving Adalah: Manfaat, Proses, Contoh, dan Tips Meningkatkannya

Problem Solving Adalah

Manfaat problem solving, proses problem solving dan contohnya, tips meningkatkan kemampuan problem solving.

Secara bahasa, problem solving adalah penyelesaian masalah. Kenali lebih dalam apa maksud dari problem solving, apa saja manfaatnya dan bagaimana prosesnya. Kita akan ulas pula tips meningkatkan kemampuan problem solving beserta contohnya.

Problem solving adalah kemampuan menyelesaikan masalah dengan pengambilan keputusan yang tepat. Berdasarkan buku Konsep Adversity & Problem Solving Skill yang disusun Risma Anita Puriani dan Ratna Sari Dewi, problem solving merupakan salah satu soft skill yang harus dimiliki seseorang.

Untuk mampu memecahkan masalah, orang harus bisa berpikir positif, logis dan sistematis. Kemampuan ini juga berkaitan dengan soft skill lainnya, seperti kemampuan analisis, inovasi, kerja sama tim, komunikasi dan pengambilan keputusan.

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Dilansir dari jurnal penelitian di Universitas Kristen Satya Wacana, problem solving adalah keterampilan intelektual yang diperoleh dari hasil belajar. Pentingnya kemampuan ini antara lain bisa dilihat dari banyaknya perhatian berbagai aliran psikologi terhadap problem solving skill.

Kegiatan keilmuan atau pendidikan tentang pemecahan masalah sebenarnya sudah lama berkembang di berbagai negara, yakni mulai tahun 1927. Selama ini pun sudah berkembang berbagai teori, model, desain, strategi, teknik, dan evaluasi pembelajaran tentang problem solving.

Kemampuan problem solving ini memiliki banyak manfaat. Berikut ini beberapa manfaat yang dilansir dari realprojects.org dan penelitian UIN Sunan Gunung Djati.

1. Memperbaiki yang Rusak

Dalam hidup, kita pasti selalu menemui masalah, baik di rumah, sekolah, atau tempat kerja. Masalah bisa saja membuat sesuatu menjadi rusak bahkan hancur. Misalnya masalah di perusahaan yang mungkin bisa membuat bangkrut, atau masalah dengan teman yang membuat hubungan rusak. Seseorang dengan kemampuan problem solving dapat memperbaiki sesuatu yang rusak menjadi baik.

2. Kemampuan Manajemen Risiko

Menyelesaikan masalah biasanya diikuti dengan pertimbangan manajemen risiko. Sering kali masalah memiliki banyak risiko yang harus dihitung agar dampak positif bisa lebih besar daripada dampak negatifnya.

3. Stabilitas Emosi

Semakin sering orang menghadapi masalah dan berhasil menyelesaikannya, maka akan mendapatkan kecerdasan emosional yang tinggi sehingga memperoleh stabilitas emosi.

4. Semakin Kreatif dan Kritis

Semakin beragam masalah yang kita tuntaskan, kita akan semakin kreatif. Sebab dalam proses pemecahan masalah, kita dituntut mencari jalan dengan pemikiran kritis. Di situlah proses kreatif akan tercipta.

5. Terampil Mengambil Keputusan

Pemecahan masalah dan pengambilan keputusan seperti dua sisi mata uang yang tidak terpisahkan. Mungkin kita tak selalu mengambil keputusan secara tepat. Seiring banyaknya masalah yang dihadapi, kita akan semakin terampil mengambil keputusan.

6. Memperluas Pengetahuan

Masalah akan menuntun kita pada pengetahuan-pengetahuan baru yang mungkin belum pernah kita temui. Jika kita mau belajar dari masalah, tentu pengetahuan kita akan semakin luas. Pengetahuan akan suatu masalah yang sudah kita kuasai pun dapat kita bagi kepada orang lain sehingga menjadi lebih bermanfaat.

Pemecahan masalah dilakukan melalui beberapa tahap atau proses. Berikut ini sejumlah proses problem solving dan contohnya, seperti dirangkum dari buku Ruslia Isnawati berjudul Pentingnya Problem Solving Bagi Seorang Remaja dan Universitas Sampoerna.

1. Definisi Masalah

Tahap paling pertama adalah mendefinisikan masalah. Anda harus mencari tahu, apa sebenarnya inti dari masalah itu dan dari mana sumbernya. Misalnya ketika menghadapi masalah kinerja karyawan yang menurun, Anda harus tahu apa penyebabnya. Untuk menelusuri ini mungkin tidak mudah, tetapi harus dilakukan mendalam.

2. Identifikasi Masalah

Setelah mengetahui akar masalahnya, maka identifikasi dan petakan hal-hal yang berkaitan dengan masalah itu, seperti dampak langsung dan tidak langsung, siapa saja yang terlibat. Misal pada masalah di atas, ternyata diketahui penyebabnya ada beberapa hal, yaitu komunikasi yang kurang efektif dan adanya konflik beberapa orang. Pada tahap ini, mungkin Anda harus memanggil beberapa orang untuk dimintai keterangan.

3. Cari Alternatif Solusi

Dari hasil identifikasi, kita akan menemukan beberapa alternatif solusi. Beberapa solusi pada kasus di atas misalnya melakukan rotasi pegawai, mengeluarkan pegawai yang menjadi sumber masalah, melakukan kegiatan santai bersama, atau mungkin membuat peraturan baru.

4. Pilih Solusi Terbaik

Dari alternatif solusi yang muncul, Anda bisa memilih solusi terbaik. Pada tahap ini, Anda dituntut bisa melakukan manajemen risiko dan mengambil keputusan yang tepat. Dalam kasus tadi, jika masalahnya masih ringan mungkin bisa ditangani dengan melakukan kegiatan santai agar pikiran seluruh pegawai kembali segar, baru kemudian diberi pemahaman agar konflik mereda dan kembali bekerja seperti seharusnya.

5. Terapkan dan Evaluasi

Setelah memilih solusi yang dianggap terbaik, terapkan sesuai rencana. Setelah berjalan, lakukan evaluasi apakah sudah efektif. Lakukan perbaikan-perbaikan lagi jika diperlukan.

Kemampuan problem solving sebetulnya akan meningkat dengan sendirinya seiring banyaknya pengalaman menghadapi masalah. Berikut ini ada beberapa tips meningkatkan kemampuan problem solving yang dirangkum dari buku Berdamai dengan Quarter Life Crisis yang disusun Jewellius Kistom M dan situs hayz.net.nz.

1. Tambah Pengetahuan

Untuk bisa memecahkan masalah dalam pekerjaan misalnya, diperlukan pengetahuan yang banyak karena hal itu merupakan salah satu cara meningkatkan kemampuan problem solving. Memperbanyak pengetahuan teknis dalam bidang pekerjaan yang digeluti tentu membuat lebih mudah mengatasi masalah yang sedang dihadapi.

2. Ikut Terlibat dalam Pemecahan Masalah

Jika terjadi masalah di lingkaran Anda, cobalah ikut terlibat dalam memecahkan masalah. Anda mungkin bisa ikut mengidentifikasi masalah dan memberikan saran solusi kepada pengambil keputusan.

3. Sering Berdiskusi

Sering-seringlah berdiskusi dengan siapa pun. Diskusi tidak selalu formal, tetapi bisa juga mengobrol dengan teman untuk membahas suatu masalah. Dengan berdiskusi, Anda akan mendapatkan pandangan baru yang mungkin tidak Anda pikirkan. Hal ini mungkin bermanfaat suatu hari nanti.

4. Lakukan Aktivitas Kreatif

Banyak aktivitas kreatif yang bisa kita lakukan, misalnya menulis cerita, membuat lagu, membaca buku, mendaur ulang barang, bermain musik, olahraga, dan bermain game dengan level bertingkat.

Mungkin aktivitas ini tidak berkaitan langsung dengan pemecahan masalah di dunia nyata, namun otak kita akan mampu berpikir kreatif sehingga dapat menemukan solusi-solusi yang tak terpikirkan.

Nah itulah tadi penjelasan lengkap mengenai problem solving yang merupakan kemampuan penting bagi setiap orang, beserta manfaat, proses, contoh dan tips meningkatkannya. Semoga bermanfaat.

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Complex vs Complicated Problems: What's the Difference?

One of the best parts of the 2021 Cascade Strategy Fest was hearing speakers and participants share mental models that they used to cultivate strong strategic thinking. Often these models encoded some timeless business wisdom but did so in a way that was easy to remember and act. And once one of these clicks, it can completely transform the way you think about a particular concept or idea.

One of these particular moments came from Jessica Nordlander who shared with us a subtle but powerful distinction between what she calls complex and complicated problems.

This distinction was the catalyst for a change in our thinking when it comes to strategy – and it might just do the same for you.

The Bridge Analogy

The best way to kickstart this is to run through an analogy that Jessica shared that perfectly illustrates the two different kinds of problems.

Jessica moved to a small mountain town in British Columbia, Canada because of the serene environment, great skiing conditions, and a generally great place to work remotely. In the middle of the town, there is a beautiful river that personifies the nature that is in the region.

Imagine that the mayor of the town has a strategic plan for its infrastructure and wants to build a new bridge over the river to unlock future economic benefits. In order to do so, the mayor and their team must solve a range of different problems, all of which can be separated into two different types.

The actual act of building the bridge is a complicated problem. It’s not easy to build a bridge, especially over a river of that size. You need a lot of engineers to work together and come up with the right plans. You need to select the right materials that fit the budget and the construction plans. You need to go through lots of checks and balances to ensure that the bridge is structurally sound and is going to hold the weight that you’re expecting. All of this is complicated.

But, with enough resources and the right people, you can solve this problem. It’s not going to be easy, but it is tractable. And we can be relatively confident that we can find a way to get this bridge built. It’s a complicated problem.

On the other side of the coin, you might find that one of the problems that this project faces is convincing the people in the town that the bridge is a good idea. Imagine that half of the town isn't buying into the project and they think that it will actually have detrimental effects by ruining the natural beauty and bringing more unwanted tourists into this small mountain town.

Dealing with these social dynamics is a complex problem. When you’re trying to come up with a viable solution here, there are no experts that you can turn to and outsource the work to. You can’t just throw money at the problem. You can’t even solve it necessarily.

Instead, you have to accept that this complex problem is one that you’re going to have to manage over time. The social disagreement is going to linger forever, and all you can do is try to make the best decision you can, and manage whatever conflict comes along with it.

With that example in mind, let’s now apply this mental model to business strategy.

Complicated Problems

In the world of business strategy, we like complicated problems. There’s a certain reward to tackling something complicated and coming up with our perfect plan to tackle it. We speak to the right people, we adjust the right budgets, and we arrive at a plan that’s going to solve everything.

A common example of a complicated problem in the space of strategy development is figuring out what the size of your total addressable market might be.

In these situations, we are able to use data, expertise, and experience to make good progress in the direction we’re aiming – and even if we don’t get it right immediately, we have the comfort of knowing that it is solvable if we just apply the right efforts and incentives.

We also have tools that can help us here. We have project management tools, Gantt charts, financial models, and a myriad of other resources that can help us think through these problems and solve them over time. We can work against expectations and make the necessary adjustments to move in the right direction.

This tight feedback loop makes for decisive action and it is a key part of any company’s strategy. This is the core of the stated objectives, it’s the goals we set for ourselves. But just because a problem is tractable, does not mean that it is easy to overcome. It takes a high level of skill, resourcefulness, and perseverance to execute the planned solution.

One of the most common obstacles we encounter when dealing with complicated problems is that we don’t have the right people on board. Without knowing it, we can often bang our heads against the wall time and time again because we just can’t see the right way to look at a particular problem. We have blind spots that impede our ability to make progress.

However, when we get the right person at the table that can bring their skills and expertise to bear on the problem, things can change dramatically. It can often just be a change in perspective that makes all the difference. And if we can get that sooner, rather than later, we’ll save ourselves a lot of time.

This doesn’t necessarily mean that we have to hire people. We can leverage knowledge in a myriad of ways including partnerships, advisors, mentors, trade exchanges, and so much more. All that matters is that we can bring the right expertise into play to solve the problem in front of us.

This requires us to put our ego aside for a bit and recognize that we probably don’t have all the answers. But when we do that, we can greatly improve our effectiveness and drive the company forward because we have the right people.

Your human resources are paramount for solving complicated problems.

Don’t take them for granted.

Complex Problems

Complex problems are much more insidious because they are often not tractable in the way we want them to be. It’s very common for companies to misdiagnose complex problems as complicated ones because we overestimate our abilities to change things that are actually out of our control.

It requires a dose of realism and a keen awareness to identify complex problems for what they are. These are the problems that you’re never truly going to have a handle on. These are the issues that will forever live in contradiction, and you need to come to terms with that.

Businesses get into trouble when they don’t make that realization and instead, they think that they can maneuver their way out of it by throwing resources or people at it.

“The more complex a problem is, the less likely it is that it will be solved by having a group of experts hacking away at it.”

Jessica Nordlander

As such, it’s crucial that we are always looking out for complex problems that need to be managed rather than solved. Because once we get to that point, we can take steps in the right direction. We can stop deluding ourselves with the perfect solution that we could find if we just had more time. Instead, we can start to put into action our plan to manage the situation over the long term.

“There's no way of really solving a complex problem, you can just manage it well or less well.”

The first thing we can do is to gather more information about the complex problem and try to understand both sides of the dilemma. As a business, you might think that you understand why people disagree with your proposed idea, but often your intuition is way off. You need to genuinely reach out to the detractors and spend time with them to understand why they feel what they feel.

Is there a point of view that you’re ignoring?

Is there an assumption of yours that is incorrect?

These discussions help to unearth the real reasons for the dilemma and when you do this effectively, you’re in a much better position to traverse the distance between the two sides. In fact, you’ll often find that a lot of the conflict stems simply from miscommunication and once you get that right – things are a lot more aligned. But you can never get to that point unless you are willing to hear the other side and take their point of view seriously.

In some cases, the mere act of listening is enough to bring people around and make them comfortable with the decision. If they feel that their objections have been heard and acknowledged, they are more likely to come on board, rather than shutting off completely.

After listening, you then need to take action and look for a compromise wherever possible.

If there are concerns that you come across that can be mitigated, then see what you can do in that regard. It’s often small things that you can improve on that show that you’re willing to compromise to try and reach a solution that works for everyone involved.

This back and forth is what managing a complex problem is all about. Keeping your stakeholders happy and content is what is going to give your project the time it needs to breathe. This is a continuous effort and requires regular maintenance, which is why so many decision-makers shy away from it. But it's part and parcel of what running a business is all about and it can be a significant factor in your overall success.

There is no manual or book that’s going to tell you how to solve these problems. There is no software tool that’s going to deliver the perfect solution. You have to be comfortable in the uncertainty. You have to acknowledge things for what they are and not let that get in the way of you taking it seriously.

It’s here in the depths of nuance, that companies can make their mark on the world, for good and for bad. This is because it deals with humanity at its core.

The Power of the Distinction 

The great companies are those who can make this distinction effectively. When you’re able to differentiate between those problems that can be solved with resources and expertise, and those that deal with much more nuanced human complexity, you’re in the best possible position to succeed.

“So, what you probably need to do is involve as many people as possible to tap into the collective intelligence, democratize the process, increase the understanding, and ensure ownership of the execution.”

Jessica’s main takeaway here is that we should leave the complicated problems to the experts, while we activate the whole organization to solve the complex problems. This common alignment and open-minded thinking make for more harmonious and sustainable solutions that perform well over time.

It’s the dichotomy between these two different responses that help us better prioritize how we make strategic decisions. Too often we assume that there are only complicated problems and so we end up throwing more and more resources at something that actually isn’t tractable in any meaningful way. We’ve seen it time and time again where companies are searching for a perfect solution that is going to appease everyone- but it never comes. And so instead of moving forward with a workable solution, they remain paralyzed in the research phase as they try to figure it out.

There is something to be said about working your problems out in the open, especially when they are complex in nature. Even though it can be tiring and difficult, there is magic to be found when you engage meaningfully with your stakeholders to understand why they’re against this or that.

It goes beyond the problem in front of you and gives you a tremendous level of insight into the people around you that you might not have had previously. It also helps to build relationships and rapport because you are genuinely trying to manage what can often be difficult circumstances. This small piece of humanity goes a long way and it can even be transformational in how your business is perceived.

If this mental model acts as a trojan horse that gets you closer to your stakeholders, then it’s well worth it. Don’t shy away from this. Embrace the distinction for what it is and your entire organization can shift.

Monitoring Progress

One last point that is worth mentioning here is that the way you track progress for each type of problem is going to vary. When you have a complicated problem, it's often quite easy to map out the step-by-step process to solve it, and you can track your progress according to that in order to stay accountable and on track. This is not the case with complex problems.

Complex problems are, by their nature, less tangible than their counterparts and so it’s more challenging to decipher whether you’re making progress or not. As such, you need to be a bit more creative with how you plan to monitor these issues. There may be some indirect ways in which you can quantify progress here but typically you’re going to rely on your intuition based on what reactions you’re getting from stakeholders.

What is key here is that you set up a time for regular reflection on these problems. Don’t let the lack of direct feedback mean you leave things to run as they are. As you’re managing complex problems over the medium and long term, you should be continually going back to the issue and evaluating how you’re doing. It’s only through forcing this internal feedback that you can adjust and adapt as you go along.

In summary, Jessica’s mental model in distinguishing between complicated and complex problems can prove incredibly useful when you take it seriously. Understanding the nuances of each type will serve you well in deploying the right resources and tools towards tackling each problem in your business setup.

In strategic decision-making, everything that we can do to better systematize these decisions is going to benefit us. So, it’s worth taking an internal inventory of all the problems you’re faced with right now as a business so you can categorize them accordingly. We think that if you take the time to work through this exercise, you’ll find that there are some complex problems that you’ve been treating as complicated ones. And when you realize that those issues can only be managed, rather than solved- you’ll take a big weight off of your shoulders.

Then, the remaining complicated problems can be handed off to the necessary experts while you seek to democratize the information for the complex ones. It might just radically shift how you view your business and its potential.

And the only way to find that out is to look inward. We certainly are.

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Menghadapi revolusi industri 4.0 di era digital harus memiliki keahlian yang dibutuhkan agar dapat sukses menghadapi dinamika yang terus berubah. Keahlian tersebut menjadi pengukur kita dalam  bersaing di era digital yang semakin berkembang. Tentu saja masalah yang akan muncul memiliki tingkat kerumitan yang tinggi. Kita dihadapkan dengan berbagai masalah dan dituntut untuk dapat memecahkan masalah dengan segera.

Problem solving ability adalah kemampuan untuk mengidentifikasi masalah dan kendala, dan memberikan beberapa solusi alternative sehingga didapat  keputusan terbaik, sehingga pilihan yang tersedia sebagai pemecah masalah (solusi) yang berdampak positif dalam penyelesaian tugas atau pekerjaan.

Baca juga : Manfaat Pelatihan Problem Solving Decision Making

Setiap bisnis atau life activity sukses salah satunya adalah memiliki kemampuan dalam memecahkan masalah (problem solving). Sudah jelas jika kita sebagai seorang pegawai atau entrepreneur yang berani menghadapi masalah dan mampu memecahkannya menjadi asset berharga bagi organisasi atau kelangsungan bisnis kita. Bahkan, menurut World Economic Forum pemecahan masalah (problem solving) yang kompleks adalah salah satu dari 10 keterampilan kunci yang dibutuhkan untuk pekerjaan di masa depan. Lalu tipe problem solver seperti apakah anda?

Baca juga : Definisi & Contoh: Bagaimana Mengembangkan Problem Solving Skills

Jika penyelesaian masalah bukan salah satu keahlian terbaik Anda, tentu saja Anda dapat mempelajari kemampuan dalam memecahkan masalah, dalam artikel kali ini ada beberapa tipe pemecah masalah (problem solver) yang akan membantu Anda untuk mengetahui tipe seperti apa Anda dalam memecahkan masalah, serta menemukan kekurangan dan kekuatan dalam menghadapi masalah. Simak empat tipe problem solver yang dapat menggambarkan diri Anda:

• Inspirer Inspirer atau si pemberi inspirasi memelihara hubungan dengan orang lain dan memiliki kemampuan menularkan rasa percaya diri. Kapanpun mereka menghadapi masalah, orang-orang berkumpul disekitarnya dan mengandalkannya, bahkan menawarkan bantuan. Mereka memiliki akses mudah kepada informasi yang dibutuhkan untuk memecahkan permasalahan.
• Reflector Reflector atau si pemikir tidak pernah terburu-buru mengambil keputusan. Mereka biasa mengambil waktu untuk berpikir, mencerna segala hal pelan-pelan, mengambil langkah mundur dari situasi tersebut untuk mendapat perspektif baru, lalu bertindak. Setelah mereka mendapat semua informasi yang mereka butuhkan, mereka mengolahnya selama beberapa waktu sebelum mengambil langkah perbaikan.
• Innovator Inovator atau si ahli inovasi memiliki kemampuan unik memunculkan solusi kreatif untuk setiap tantangan dan masalah yang mereka hadapi. Solusinya bisa jadi baru, atau bisa juga merupakan gabungan antara dua solusi yang telah dikenal menjadi sesuatu yang baru dan inovatif.
• Influencer Influencer atau si pembawa pengaruh sangat ahli membuat orang di sekelilingnya mendukung segala sesuatu yang sedang ia kerjakan. Mereka cakap dalam menemukan solusi untuk “masalah bersama” yang melibatkan perubahan. Mereka jago mendapatkan “buy in” dari orang lain.

Kemampuan memecahkan masalah atau problem solving skill sangat penting dan sangat dicari di dunia bisnis. Mereka yang menguasai skill tersebut akan membuat kemajuan lebih cepat dibandingkan rekan-rekan sejawatnya. Problem solving memang salah satu skill terbaik yang bisa dimiliki seseorang. Berita baiknya, skill tersebut bisa dipelajari dan ada banyak sumber yang bisa anda eksplorasi untuk meningkatkan kemampuan memecahkan masalah.

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Complex Problem Solving: What It Is and What It Is Not

Dietrich dörner.

1 Department of Psychology, University of Bamberg, Bamberg, Germany

Joachim Funke

2 Department of Psychology, Heidelberg University, Heidelberg, Germany

Computer-simulated scenarios have been part of psychological research on problem solving for more than 40 years. The shift in emphasis from simple toy problems to complex, more real-life oriented problems has been accompanied by discussions about the best ways to assess the process of solving complex problems. Psychometric issues such as reliable assessments and addressing correlations with other instruments have been in the foreground of these discussions and have left the content validity of complex problem solving in the background. In this paper, we return the focus to content issues and address the important features that define complex problems.

Succeeding in the 21st century requires many competencies, including creativity, life-long learning, and collaboration skills (e.g., National Research Council, 2011 ; Griffin and Care, 2015 ), to name only a few. One competence that seems to be of central importance is the ability to solve complex problems ( Mainzer, 2009 ). Mainzer quotes the Nobel prize winner Simon (1957) who wrote as early as 1957:

The capacity of the human mind for formulating and solving complex problems is very small compared with the size of the problem whose solution is required for objectively rational behavior in the real world or even for a reasonable approximation to such objective rationality. (p. 198)

The shift from well-defined to ill-defined problems came about as a result of a disillusion with the “general problem solver” ( Newell et al., 1959 ): The general problem solver was a computer software intended to solve all kind of problems that can be expressed through well-formed formulas. However, it soon became clear that this procedure was in fact a “special problem solver” that could only solve well-defined problems in a closed space. But real-world problems feature open boundaries and have no well-determined solution. In fact, the world is full of wicked problems and clumsy solutions ( Verweij and Thompson, 2006 ). As a result, solving well-defined problems and solving ill-defined problems requires different cognitive processes ( Schraw et al., 1995 ; but see Funke, 2010 ).

Well-defined problems have a clear set of means for reaching a precisely described goal state. For example: in a match-stick arithmetic problem, a person receives a false arithmetic expression constructed out of matchsticks (e.g., IV = III + III). According to the instructions, moving one of the matchsticks will make the equations true. Here, both the problem (find the appropriate stick to move) and the goal state (true arithmetic expression; solution is: VI = III + III) are defined clearly.

Ill-defined problems have no clear problem definition, their goal state is not defined clearly, and the means of moving towards the (diffusely described) goal state are not clear. For example: The goal state for solving the political conflict in the near-east conflict between Israel and Palestine is not clearly defined (living in peaceful harmony with each other?) and even if the conflict parties would agree on a two-state solution, this goal again leaves many issues unresolved. This type of problem is called a “complex problem” and is of central importance to this paper. All psychological processes that occur within individual persons and deal with the handling of such ill-defined complex problems will be subsumed under the umbrella term “complex problem solving” (CPS).

Systematic research on CPS started in the 1970s with observations of the behavior of participants who were confronted with computer simulated microworlds. For example, in one of those microworlds participants assumed the role of executives who were tasked to manage a company over a certain period of time (see Brehmer and Dörner, 1993 , for a discussion of this methodology). Today, CPS is an established concept and has even influenced large-scale assessments such as PISA (“Programme for International Student Assessment”), organized by the Organization for Economic Cooperation and Development ( OECD, 2014 ). According to the World Economic Forum, CPS is one of the most important competencies required in the future ( World Economic Forum, 2015 ). Numerous articles on the subject have been published in recent years, documenting the increasing research activity relating to this field. In the following collection of papers we list only those published in 2010 and later: theoretical papers ( Blech and Funke, 2010 ; Funke, 2010 ; Knauff and Wolf, 2010 ; Leutner et al., 2012 ; Selten et al., 2012 ; Wüstenberg et al., 2012 ; Greiff et al., 2013b ; Fischer and Neubert, 2015 ; Schoppek and Fischer, 2015 ), papers about measurement issues ( Danner et al., 2011a ; Greiff et al., 2012 , 2015a ; Alison et al., 2013 ; Gobert et al., 2015 ; Greiff and Fischer, 2013 ; Herde et al., 2016 ; Stadler et al., 2016 ), papers about applications ( Fischer and Neubert, 2015 ; Ederer et al., 2016 ; Tremblay et al., 2017 ), papers about differential effects ( Barth and Funke, 2010 ; Danner et al., 2011b ; Beckmann and Goode, 2014 ; Greiff and Neubert, 2014 ; Scherer et al., 2015 ; Meißner et al., 2016 ; Wüstenberg et al., 2016 ), one paper about developmental effects ( Frischkorn et al., 2014 ), one paper with a neuroscience background ( Osman, 2012 ) 1 , papers about cultural differences ( Güss and Dörner, 2011 ; Sonnleitner et al., 2014 ; Güss et al., 2015 ), papers about validity issues ( Goode and Beckmann, 2010 ; Greiff et al., 2013c ; Schweizer et al., 2013 ; Mainert et al., 2015 ; Funke et al., 2017 ; Greiff et al., 2017 , 2015b ; Kretzschmar et al., 2016 ; Kretzschmar, 2017 ), review papers and meta-analyses ( Osman, 2010 ; Stadler et al., 2015 ), and finally books ( Qudrat-Ullah, 2015 ; Csapó and Funke, 2017b ) and book chapters ( Funke, 2012 ; Hotaling et al., 2015 ; Funke and Greiff, 2017 ; Greiff and Funke, 2017 ; Csapó and Funke, 2017a ; Fischer et al., 2017 ; Molnàr et al., 2017 ; Tobinski and Fritz, 2017 ; Viehrig et al., 2017 ). In addition, a new “Journal of Dynamic Decision Making” (JDDM) has been launched ( Fischer et al., 2015 , 2016 ) to give the field an open-access outlet for research and discussion.

This paper aims to clarify aspects of validity: what should be meant by the term CPS and what not? This clarification seems necessary because misunderstandings in recent publications provide – from our point of view – a potentially misleading picture of the construct. We start this article with a historical review before attempting to systematize different positions. We conclude with a working definition.

Historical Review

The concept behind CPS goes back to the German phrase “komplexes Problemlösen” (CPS; the term “komplexes Problemlösen” was used as a book title by Funke, 1986 ). The concept was introduced in Germany by Dörner and colleagues in the mid-1970s (see Dörner et al., 1975 ; Dörner, 1975 ) for the first time. The German phrase was later translated to CPS in the titles of two edited volumes by Sternberg and Frensch (1991) and Frensch and Funke (1995a) that collected papers from different research traditions. Even though it looks as though the term was coined in the 1970s, Edwards (1962) used the term “dynamic decision making” to describe decisions that come in a sequence. He compared static with dynamic decision making, writing:

• simple  In dynamic situations, a new complication not found in the static situations arises. The environment in which the decision is set may be changing, either as a function of the sequence of decisions, or independently of them, or both. It is this possibility of an environment which changes while you collect information about it which makes the task of dynamic decision theory so difficult and so much fun. (p. 60)

The ability to solve complex problems is typically measured via dynamic systems that contain several interrelated variables that participants need to alter. Early work (see, e.g., Dörner, 1980 ) used a simulation scenario called “Lohhausen” that contained more than 2000 variables that represented the activities of a small town: Participants had to take over the role of a mayor for a simulated period of 10 years. The simulation condensed these ten years to ten hours in real time. Later, researchers used smaller dynamic systems as scenarios either based on linear equations (see, e.g., Funke, 1993 ) or on finite state automata (see, e.g., Buchner and Funke, 1993 ). In these contexts, CPS consisted of the identification and control of dynamic task environments that were previously unknown to the participants. Different task environments came along with different degrees of fidelity ( Gray, 2002 ).

According to Funke (2012) , the typical attributes of complex systems are (a) complexity of the problem situation which is usually represented by the sheer number of involved variables; (b) connectivity and mutual dependencies between involved variables; (c) dynamics of the situation, which reflects the role of time and developments within a system; (d) intransparency (in part or full) about the involved variables and their current values; and (e) polytely (greek term for “many goals”), representing goal conflicts on different levels of analysis. This mixture of features is similar to what is called VUCA (volatility, uncertainty, complexity, ambiguity) in modern approaches to management (e.g., Mack et al., 2016 ).

In his evaluation of the CPS movement, Sternberg (1995) compared (young) European approaches to CPS with (older) American research on expertise. His analysis of the differences between the European and American traditions shows advantages but also potential drawbacks for each side. He states (p. 301): “I believe that although there are problems with the European approach, it deals with some fundamental questions that American research scarcely addresses.” So, even though the echo of the European approach did not enjoy strong resonance in the US at that time, it was valued by scholars like Sternberg and others. Before attending to validity issues, we will first present a short review of different streams.

Different Approaches to CPS

In the short history of CPS research, different approaches can be identified ( Buchner, 1995 ; Fischer et al., 2017 ). To systematize, we differentiate between the following five lines of research:

• simple (a) The search for individual differences comprises studies identifying interindividual differences that affect the ability to solve complex problems. This line of research is reflected, for example, in the early work by Dörner et al. (1983) and their “Lohhausen” study. Here, naïve student participants took over the role of the mayor of a small simulated town named Lohhausen for a simulation period of ten years. According to the results of the authors, it is not intelligence (as measured by conventional IQ tests) that predicts performance, but it is the ability to stay calm in the face of a challenging situation and the ability to switch easily between an analytic mode of processing and a more holistic one.
• simple (b) The search for cognitive processes deals with the processes behind understanding complex dynamic systems. Representative of this line of research is, for example, Berry and Broadbent’s (1984) work on implicit and explicit learning processes when people interact with a dynamic system called “Sugar Production”. They found that those who perform best in controlling a dynamic system can do so implicitly, without explicit knowledge of details regarding the systems’ relations.
• simple (c) The search for system factors seeks to identify the aspects of dynamic systems that determine the difficulty of complex problems and make some problems harder than others. Representative of this line of research is, for example, work by Funke (1985) , who systematically varied the number of causal effects within a dynamic system or the presence/absence of eigendynamics. He found, for example, that solution quality decreases as the number of systems relations increases.
• simple (d) The psychometric approach develops measurement instruments that can be used as an alternative to classical IQ tests, as something that goes “beyond IQ”. The MicroDYN approach ( Wüstenberg et al., 2012 ) is representative for this line of research that presents an alternative to reasoning tests (like Raven matrices). These authors demonstrated that a small improvement in predicting school grade point average beyond reasoning is possible with MicroDYN tests.
• simple (e) The experimental approach explores CPS under different experimental conditions. This approach uses CPS assessment instruments to test hypotheses derived from psychological theories and is sometimes used in research about cognitive processes (see above). Exemplary for this line of research is the work by Rohe et al. (2016) , who test the usefulness of “motto goals” in the context of complex problems compared to more traditional learning and performance goals. Motto goals differ from pure performance goals by activating positive affect and should lead to better goal attainment especially in complex situations (the mentioned study found no effect).

To be clear: these five approaches are not mutually exclusive and do overlap. But the differentiation helps to identify different research communities and different traditions. These communities had different opinions about scaling complexity.

The Race for Complexity: Use of More and More Complex Systems

In the early years of CPS research, microworlds started with systems containing about 20 variables (“Tailorshop”), soon reached 60 variables (“Moro”), and culminated in systems with about 2000 variables (“Lohhausen”). This race for complexity ended with the introduction of the concept of “minimal complex systems” (MCS; Greiff and Funke, 2009 ; Funke and Greiff, 2017 ), which ushered in a search for the lower bound of complexity instead of the higher bound, which could not be defined as easily. The idea behind this concept was that whereas the upper limits of complexity are unbound, the lower limits might be identifiable. Imagine starting with a simple system containing two variables with a simple linear connection between them; then, step by step, increase the number of variables and/or the type of connections. One soon reaches a point where the system can no longer be considered simple and has become a “complex system”. This point represents a minimal complex system. Despite some research having been conducted in this direction, the point of transition from simple to complex has not been identified clearly as of yet.

Some years later, the original “minimal complex systems” approach ( Greiff and Funke, 2009 ) shifted to the “multiple complex systems” approach ( Greiff et al., 2013a ). This shift is more than a slight change in wording: it is important because it taps into the issue of validity directly. Minimal complex systems have been introduced in the context of challenges from large-scale assessments like PISA 2012 that measure new aspects of problem solving, namely interactive problems besides static problem solving ( Greiff and Funke, 2017 ). PISA 2012 required test developers to remain within testing time constraints (given by the school class schedule). Also, test developers needed a large item pool for the construction of a broad class of problem solving items. It was clear from the beginning that MCS deal with simple dynamic situations that require controlled interaction: the exploration and control of simple ticket machines, simple mobile phones, or simple MP3 players (all of these example domains were developed within PISA 2012) – rather than really complex situations like managerial or political decision making.

As a consequence of this subtle but important shift in interpreting the letters MCS, the definition of CPS became a subject of debate recently ( Funke, 2014a ; Greiff and Martin, 2014 ; Funke et al., 2017 ). In the words of Funke (2014b , p. 495):

• simple  It is funny that problems that nowadays come under the term ‘CPS’, are less complex (in terms of the previously described attributes of complex situations) than at the beginning of this new research tradition. The emphasis on psychometric qualities has led to a loss of variety. Systems thinking requires more than analyzing models with two or three linear equations – nonlinearity, cyclicity, rebound effects, etc. are inherent features of complex problems and should show up at least in some of the problems used for research and assessment purposes. Minimal complex systems run the danger of becoming minimal valid systems.

Searching for minimal complex systems is not the same as gaining insight into the way how humans deal with complexity and uncertainty. For psychometric purposes, it is appropriate to reduce complexity to a minimum; for understanding problem solving under conditions of overload, intransparency, and dynamics, it is necessary to realize those attributes with reasonable strength. This aspect is illustrated in the next section.

Importance of the Validity Issue

The most important reason for discussing the question of what complex problem solving is and what it is not stems from its phenomenology: if we lose sight of our phenomena, we are no longer doing good psychology. The relevant phenomena in the context of complex problems encompass many important aspects. In this section, we discuss four phenomena that are specific to complex problems. We consider these phenomena as critical for theory development and for the construction of assessment instruments (i.e., microworlds). These phenomena require theories for explaining them and they require assessment instruments eliciting them in a reliable way.

The first phenomenon is the emergency reaction of the intellectual system ( Dörner, 1980 ): When dealing with complex systems, actors tend to (a) reduce their intellectual level by decreasing self-reflections, by decreasing their intentions, by stereotyping, and by reducing their realization of intentions, (b) they show a tendency for fast action with increased readiness for risk, with increased violations of rules, and with increased tendency to escape the situation, and (c) they degenerate their hypotheses formation by construction of more global hypotheses and reduced tests of hypotheses, by increasing entrenchment, and by decontextualizing their goals. This phenomenon illustrates the strong connection between cognition, emotion, and motivation that has been emphasized by Dörner (see, e.g., Dörner and Güss, 2013 ) from the beginning of his research tradition; the emergency reaction reveals a shift in the mode of information processing under the pressure of complexity.

The second phenomenon comprises cross-cultural differences with respect to strategy use ( Strohschneider and Güss, 1999 ; Güss and Wiley, 2007 ; Güss et al., 2015 ). Results from complex task environments illustrate the strong influence of context and background knowledge to an extent that cannot be found for knowledge-poor problems. For example, in a comparison between Brazilian and German participants, it turned out that Brazilians accept the given problem descriptions and are more optimistic about the results of their efforts, whereas Germans tend to inquire more about the background of the problems and take a more active approach but are less optimistic (according to Strohschneider and Güss, 1998 , p. 695).

The third phenomenon relates to failures that occur during the planning and acting stages ( Jansson, 1994 ; Ramnarayan et al., 1997 ), illustrating that rational procedures seem to be unlikely to be used in complex situations. The potential for failures ( Dörner, 1996 ) rises with the complexity of the problem. Jansson (1994) presents seven major areas for failures with complex situations: acting directly on current feedback; insufficient systematization; insufficient control of hypotheses and strategies; lack of self-reflection; selective information gathering; selective decision making; and thematic vagabonding.

The fourth phenomenon describes (a lack of) training and transfer effects ( Kretzschmar and Süß, 2015 ), which again illustrates the context dependency of strategies and knowledge (i.e., there is no strategy that is so universal that it can be used in many different problem situations). In their own experiment, the authors could show training effects only for knowledge acquisition, not for knowledge application. Only with specific feedback, performance in complex environments can be increased ( Engelhart et al., 2017 ).

These four phenomena illustrate why the type of complexity (or degree of simplicity) used in research really matters. Furthermore, they demonstrate effects that are specific for complex problems, but not for toy problems. These phenomena direct the attention to the important question: does the stimulus material used (i.e., the computer-simulated microworld) tap and elicit the manifold of phenomena described above?

Dealing with partly unknown complex systems requires courage, wisdom, knowledge, grit, and creativity. In creativity research, “little c” and “BIG C” are used to differentiate between everyday creativity and eminent creativity ( Beghetto and Kaufman, 2007 ; Kaufman and Beghetto, 2009 ). Everyday creativity is important for solving everyday problems (e.g., finding a clever fix for a broken spoke on my bicycle), eminent creativity changes the world (e.g., inventing solar cells for energy production). Maybe problem solving research should use a similar differentiation between “little p” and “BIG P” to mark toy problems on the one side and big societal challenges on the other. The question then remains: what can we learn about BIG P by studying little p? What phenomena are present in both types, and what phenomena are unique to each of the two extremes?

Discussing research on CPS requires reflecting on the field’s research methods. Even if the experimental approach has been successful for testing hypotheses (for an overview of older work, see Funke, 1995 ), other methods might provide additional and novel insights. Complex phenomena require complex approaches to understand them. The complex nature of complex systems imposes limitations on psychological experiments: The more complex the environments, the more difficult is it to keep conditions under experimental control. And if experiments have to be run in labs one should bring enough complexity into the lab to establish the phenomena mentioned, at least in part.

There are interesting options to be explored (again): think-aloud protocols , which have been discredited for many years ( Nisbett and Wilson, 1977 ) and yet are a valuable source for theory testing ( Ericsson and Simon, 1983 ); introspection ( Jäkel and Schreiber, 2013 ), which seems to be banned from psychological methods but nevertheless offers insights into thought processes; the use of life-streaming ( Wendt, 2017 ), a medium in which streamers generate a video stream of think-aloud data in computer-gaming; political decision-making ( Dhami et al., 2015 ) that demonstrates error-proneness in groups; historical case studies ( Dörner and Güss, 2011 ) that give insights into the thinking styles of political leaders; the use of the critical incident technique ( Reuschenbach, 2008 ) to construct complex scenarios; and simulations with different degrees of fidelity ( Gray, 2002 ).

The methods tool box is full of instruments that have to be explored more carefully before any individual instrument receives a ban or research narrows its focus to only one paradigm for data collection. Brehmer and Dörner (1993) discussed the tensions between “research in the laboratory and research in the field”, optimistically concluding “that the new methodology of computer-simulated microworlds will provide us with the means to bridge the gap between the laboratory and the field” (p. 183). The idea behind this optimism was that computer-simulated scenarios would bring more complexity from the outside world into the controlled lab environment. But this is not true for all simulated scenarios. In his paper on simulated environments, Gray (2002) differentiated computer-simulated environments with respect to three dimensions: (1) tractability (“the more training subjects require before they can use a simulated task environment, the less tractable it is”, p. 211), correspondence (“High correspondence simulated task environments simulate many aspects of one task environment. Low correspondence simulated task environments simulate one aspect of many task environments”, p. 214), and engagement (“A simulated task environment is engaging to the degree to which it involves and occupies the participants; that is, the degree to which they agree to take it seriously”, p. 217). But the mere fact that a task is called a “computer-simulated task environment” does not mean anything specific in terms of these three dimensions. This is one of several reasons why we should differentiate between those studies that do not address the core features of CPS and those that do.

What is not CPS?

Even though a growing number of references claiming to deal with complex problems exist (e.g., Greiff and Wüstenberg, 2015 ; Greiff et al., 2016 ), it would be better to label the requirements within these tasks “dynamic problem solving,” as it has been done adequately in earlier work ( Greiff et al., 2012 ). The dynamics behind on-off-switches ( Thimbleby, 2007 ) are remarkable but not really complex. Small nonlinear systems that exhibit stunningly complex and unstable behavior do exist – but they are not used in psychometric assessments of so-called CPS. There are other small systems (like MicroDYN scenarios: Greiff and Wüstenberg, 2014 ) that exhibit simple forms of system behavior that are completely predictable and stable. This type of simple systems is used frequently. It is even offered commercially as a complex problem-solving test called COMPRO ( Greiff and Wüstenberg, 2015 ) for business applications. But a closer look reveals that the label is not used correctly; within COMPRO, the used linear equations are far from being complex and the system can be handled properly by using only one strategy (see for more details Funke et al., 2017 ).

Why do simple linear systems not fall within CPS? At the surface, nonlinear and linear systems might appear similar because both only include 3–5 variables. But the difference is in terms of systems behavior as well as strategies and learning. If the behavior is simple (as in linear systems where more input is related to more output and vice versa), the system can be easily understood (participants in the MicroDYN world have 3 minutes to explore a complex system). If the behavior is complex (as in systems that contain strange attractors or negative feedback loops), things become more complicated and much more observation is needed to identify the hidden structure of the unknown system ( Berry and Broadbent, 1984 ; Hundertmark et al., 2015 ).

Another issue is learning. If tasks can be solved using a single (and not so complicated) strategy, steep learning curves are to be expected. The shift from problem solving to learned routine behavior occurs rapidly, as was demonstrated by Luchins (1942) . In his water jar experiments, participants quickly acquired a specific strategy (a mental set) for solving certain measurement problems that they later continued applying to problems that would have allowed for easier approaches. In the case of complex systems, learning can occur only on very general, abstract levels because it is difficult for human observers to make specific predictions. Routines dealing with complex systems are quite different from routines relating to linear systems.

What should not be studied under the label of CPS are pure learning effects, multiple-cue probability learning, or tasks that can be solved using a single strategy. This last issue is a problem for MicroDYN tasks that rely strongly on the VOTAT strategy (“vary one thing at a time”; see Tschirgi, 1980 ). In real-life, it is hard to imagine a business manager trying to solve her or his problems by means of VOTAT.

What is CPS?

In the early days of CPS research, planet Earth’s dynamics and complexities gained attention through such books as “The limits to growth” ( Meadows et al., 1972 ) and “Beyond the limits” ( Meadows et al., 1992 ). In the current decade, for example, the World Economic Forum (2016) attempts to identify the complexities and risks of our modern world. In order to understand the meaning of complexity and uncertainty, taking a look at the worlds’ most pressing issues is helpful. Searching for strategies to cope with these problems is a difficult task: surely there is no place for the simple principle of “vary-one-thing-at-a-time” (VOTAT) when it comes to global problems. The VOTAT strategy is helpful in the context of simple problems ( Wüstenberg et al., 2014 ); therefore, whether or not VOTAT is helpful in a given problem situation helps us distinguish simple from complex problems.

Because there exist no clear-cut strategies for complex problems, typical failures occur when dealing with uncertainty ( Dörner, 1996 ; Güss et al., 2015 ). Ramnarayan et al. (1997) put together a list of generic errors (e.g., not developing adequate action plans; lack of background control; learning from experience blocked by stereotype knowledge; reactive instead of proactive action) that are typical of knowledge-rich complex systems but cannot be found in simple problems.

Complex problem solving is not a one-dimensional, low-level construct. On the contrary, CPS is a multi-dimensional bundle of competencies existing at a high level of abstraction, similar to intelligence (but going beyond IQ). As Funke et al. (2018) state: “Assessment of transversal (in educational contexts: cross-curricular) competencies cannot be done with one or two types of assessment. The plurality of skills and competencies requires a plurality of assessment instruments.”

There are at least three different aspects of complex systems that are part of our understanding of a complex system: (1) a complex system can be described at different levels of abstraction; (2) a complex system develops over time, has a history, a current state, and a (potentially unpredictable) future; (3) a complex system is knowledge-rich and activates a large semantic network, together with a broad list of potential strategies (domain-specific as well as domain-general).

Complex problem solving is not only a cognitive process but is also an emotional one ( Spering et al., 2005 ; Barth and Funke, 2010 ) and strongly dependent on motivation (low-stakes versus high-stakes testing; see Hermes and Stelling, 2016 ).

Furthermore, CPS is a dynamic process unfolding over time, with different phases and with more differentiation than simply knowledge acquisition and knowledge application. Ideally, the process should entail identifying problems (see Dillon, 1982 ; Lee and Cho, 2007 ), even if in experimental settings, problems are provided to participants a priori . The more complex and open a given situation, the more options can be generated (T. S. Schweizer et al., 2016 ). In closed problems, these processes do not occur in the same way.

In analogy to the difference between formative (process-oriented) and summative (result-oriented) assessment ( Wiliam and Black, 1996 ; Bennett, 2011 ), CPS should not be reduced to the mere outcome of a solution process. The process leading up to the solution, including detours and errors made along the way, might provide a more differentiated impression of a person’s problem-solving abilities and competencies than the final result of such a process. This is one of the reasons why CPS environments are not, in fact, complex intelligence tests: research on CPS is not only about the outcome of the decision process, but it is also about the problem-solving process itself.

Complex problem solving is part of our daily life: finding the right person to share one’s life with, choosing a career that not only makes money, but that also makes us happy. Of course, CPS is not restricted to personal problems – life on Earth gives us many hard nuts to crack: climate change, population growth, the threat of war, the use and distribution of natural resources. In sum, many societal challenges can be seen as complex problems. To reduce that complexity to a one-hour lab activity on a random Friday afternoon puts it out of context and does not address CPS issues.

Theories about CPS should specify which populations they apply to. Across populations, one thing to consider is prior knowledge. CPS research with experts (e.g., Dew et al., 2009 ) is quite different from problem solving research using tasks that intentionally do not require any specific prior knowledge (see, e.g., Beckmann and Goode, 2014 ).

More than 20 years ago, Frensch and Funke (1995b) defined CPS as follows:

• simple  CPS occurs to overcome barriers between a given state and a desired goal state by means of behavioral and/or cognitive, multi-step activities. The given state, goal state, and barriers between given state and goal state are complex, change dynamically during problem solving, and are intransparent. The exact properties of the given state, goal state, and barriers are unknown to the solver at the outset. CPS implies the efficient interaction between a solver and the situational requirements of the task, and involves a solver’s cognitive, emotional, personal, and social abilities and knowledge. (p. 18)

The above definition is rather formal and does not account for content or relations between the simulation and the real world. In a sense, we need a new definition of CPS that addresses these issues. Based on our previous arguments, we propose the following working definition:

• simple  Complex problem solving is a collection of self-regulated psychological processes and activities necessary in dynamic environments to achieve ill-defined goals that cannot be reached by routine actions. Creative combinations of knowledge and a broad set of strategies are needed. Solutions are often more bricolage than perfect or optimal. The problem-solving process combines cognitive, emotional, and motivational aspects, particularly in high-stakes situations. Complex problems usually involve knowledge-rich requirements and collaboration among different persons.

The main differences to the older definition lie in the emphasis on (a) the self-regulation of processes, (b) creativity (as opposed to routine behavior), (c) the bricolage type of solution, and (d) the role of high-stakes challenges. Our new definition incorporates some aspects that have been discussed in this review but were not reflected in the 1995 definition, which focused on attributes of complex problems like dynamics or intransparency.

This leads us to the final reflection about the role of CPS for dealing with uncertainty and complexity in real life. We will distinguish thinking from reasoning and introduce the sense of possibility as an important aspect of validity.

CPS as Combining Reasoning and Thinking in an Uncertain Reality

Leading up to the Battle of Borodino in Leo Tolstoy’s novel “War and Peace”, Prince Andrei Bolkonsky explains the concept of war to his friend Pierre. Pierre expects war to resemble a game of chess: You position the troops and attempt to defeat your opponent by moving them in different directions.

“Far from it!”, Andrei responds. “In chess, you know the knight and his moves, you know the pawn and his combat strength. While in war, a battalion is sometimes stronger than a division and sometimes weaker than a company; it all depends on circumstances that can never be known. In war, you do not know the position of your enemy; some things you might be able to observe, some things you have to divine (but that depends on your ability to do so!) and many things cannot even be guessed at. In chess, you can see all of your opponent’s possible moves. In war, that is impossible. If you decide to attack, you cannot know whether the necessary conditions are met for you to succeed. Many a time, you cannot even know whether your troops will follow your orders…”

In essence, war is characterized by a high degree of uncertainty. A good commander (or politician) can add to that what he or she sees, tentatively fill in the blanks – and not just by means of logical deduction but also by intelligently bridging missing links. A bad commander extrapolates from what he sees and thus arrives at improper conclusions.

Many languages differentiate between two modes of mentalizing; for instance, the English language distinguishes between ‘thinking’ and ‘reasoning’. Reasoning denotes acute and exact mentalizing involving logical deductions. Such deductions are usually based on evidence and counterevidence. Thinking, however, is what is required to write novels. It is the construction of an initially unknown reality. But it is not a pipe dream, an unfounded process of fabrication. Rather, thinking asks us to imagine reality (“Wirklichkeitsfantasie”). In other words, a novelist has to possess a “sense of possibility” (“Möglichkeitssinn”, Robert Musil; in German, sense of possibility is often used synonymously with imagination even though imagination is not the same as sense of possibility, for imagination also encapsulates the impossible). This sense of possibility entails knowing the whole (or several wholes) or being able to construe an unknown whole that could accommodate a known part. The whole has to align with sociological and geographical givens, with the mentality of certain peoples or groups, and with the laws of physics and chemistry. Otherwise, the entire venture is ill-founded. A sense of possibility does not aim for the moon but imagines something that might be possible but has not been considered possible or even potentially possible so far.

Thinking is a means to eliminate uncertainty. This process requires both of the modes of thinking we have discussed thus far. Economic, political, or ecological decisions require us to first consider the situation at hand. Though certain situational aspects can be known, but many cannot. In fact, von Clausewitz (1832) posits that only about 25% of the necessary information is available when a military decision needs to be made. Even then, there is no way to guarantee that whatever information is available is also correct: Even if a piece of information was completely accurate yesterday, it might no longer apply today.

Once our sense of possibility has helped grasping a situation, problem solvers need to call on their reasoning skills. Not every situation requires the same action, and we may want to act this way or another to reach this or that goal. This appears logical, but it is a logic based on constantly shifting grounds: We cannot know whether necessary conditions are met, sometimes the assumptions we have made later turn out to be incorrect, and sometimes we have to revise our assumptions or make completely new ones. It is necessary to constantly switch between our sense of possibility and our sense of reality, that is, to switch between thinking and reasoning. It is an arduous process, and some people handle it well, while others do not.

If we are to believe Tuchman’s (1984) book, “The March of Folly”, most politicians and commanders are fools. According to Tuchman, not much has changed in the 3300 years that have elapsed since the misguided Trojans decided to welcome the left-behind wooden horse into their city that would end up dismantling Troy’s defensive walls. The Trojans, too, had been warned, but decided not to heed the warning. Although Laocoön had revealed the horse’s true nature to them by attacking it with a spear, making the weapons inside the horse ring, the Trojans refused to see the forest for the trees. They did not want to listen, they wanted the war to be over, and this desire ended up shaping their perception.

The objective of psychology is to predict and explain human actions and behavior as accurately as possible. However, thinking cannot be investigated by limiting its study to neatly confined fractions of reality such as the realms of propositional logic, chess, Go tasks, the Tower of Hanoi, and so forth. Within these systems, there is little need for a sense of possibility. But a sense of possibility – the ability to divine and construe an unknown reality – is at least as important as logical reasoning skills. Not researching the sense of possibility limits the validity of psychological research. All economic and political decision making draws upon this sense of possibility. By not exploring it, psychological research dedicated to the study of thinking cannot further the understanding of politicians’ competence and the reasons that underlie political mistakes. Christopher Clark identifies European diplomats’, politicians’, and commanders’ inability to form an accurate representation of reality as a reason for the outbreak of World War I. According to Clark’s (2012) book, “The Sleepwalkers”, the politicians of the time lived in their own make-believe world, wrongfully assuming that it was the same world everyone else inhabited. If CPS research wants to make significant contributions to the world, it has to acknowledge complexity and uncertainty as important aspects of it.

For more than 40 years, CPS has been a new subject of psychological research. During this time period, the initial emphasis on analyzing how humans deal with complex, dynamic, and uncertain situations has been lost. What is subsumed under the heading of CPS in modern research has lost the original complexities of real-life problems. From our point of view, the challenges of the 21st century require a return to the origins of this research tradition. We would encourage researchers in the field of problem solving to come back to the original ideas. There is enough complexity and uncertainty in the world to be studied. Improving our understanding of how humans deal with these global and pressing problems would be a worthwhile enterprise.

Author Contributions

JF drafted a first version of the manuscript, DD added further text and commented on the draft. JF finalized the manuscript.

Authors Note

After more than 40 years of controversial discussions between both authors, this is the first joint paper. We are happy to have done this now! We have found common ground!

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The authors thank the Deutsche Forschungsgemeinschaft (DFG) for the continuous support of their research over many years. Thanks to Daniel Holt for his comments on validity issues, thanks to Julia Nolte who helped us by translating German text excerpts into readable English and helped us, together with Keri Hartman, to improve our style and grammar – thanks for that! We also thank the two reviewers for their helpful critical comments on earlier versions of this manuscript. Finally, we acknowledge financial support by Deutsche Forschungsgemeinschaft and Ruprecht-Karls-Universität Heidelberg within their funding programme Open Access Publishing .

1 The fMRI-paper from Anderson (2012) uses the term “complex problem solving” for tasks that do not fall in our understanding of CPS and is therefore excluded from this list.

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The Critical Difference Between Complex and Complicated

Featured excerpt from it’s not complicated: the art and science of complexity for business.

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It’s time to call out the real culprit in far too many business failures — Dr. Peter Mark Roget and his insidious thesaurus. Roget is long dead, but his gang of modern-day editors still assert that the words “complex” and “complicated” are synonyms . Unfortunately, as Rick Nason, an associate professor of finance at Dalhousie University’s Rowe School of Business, ably explains in his new book, It’s Not Complicated , if you manage complex things as if they are merely complicated, you’re likely to be setting your company up for failure.

Complicated problems can be hard to solve, but they are addressable with rules and recipes, like the algorithms that place ads on your Twitter feed. They also can be resolved with systems and processes, like the hierarchical structure that most companies use to command and control employees.

The solutions to complicated problems don’t work as well with complex problems, however. Complex problems involve too many unknowns and too many interrelated factors to reduce to rules and processes. A technological disruption like blockchain is a complex problem. A competitor with an innovative business model — an Uber or an Airbnb — is a complex problem. There’s no algorithm that will tell you how to respond.

This could be dismissed as an exercise in semantics, except for one thing: When facing a problem, says Nason, managers tend to automatically default to complicated thinking. Instead, they should be “consciously managing complexity.” In the excerpt that follows, which is edited for space, Nason explains how.

About the Author

Theodore Kinni is a contributing editor for MIT Sloan Management Review . He tweets @tedkinni .

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Chris lawer.