problem solving mental models

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Mental models: 13 thinking tools to boost your problem-solving skills

Imagine you've gone out to dinner with friends. You’ve just sat down at your favorite table at your favorite restaurant, looking forward to the evening ahead.

The waiter brings over your menus and tells you about the specials. It sounds like one of the dishes is really good — you've always wanted to try it, and the way they've described it sounds amazing.

You're mulling it over in your mind while the others order, and then it's your turn — and you just ask for the same meal you always get.

Sound familiar?

Whether it’s your favorite meal or the perfectly worn-in pair of jeans in your closet, this tendency to fall back on what we know rather than risk something unknown is the result of a common thinking tool called a mental model.

Mental models, like the status quo bias in the scenario above, represent how we perceive something to operate in the world based on what we have learned in our lives. We all use them to help us understand complex situations and predict what will happen. If leveraged well, they can be powerful thinking tools.

This article will explore the concept of mental models as thinking tools and uncover 13 mental models you can add to your toolkit of thinking skills.

Mental models as thinking tools

Most of the time, we're not as thoughtful as we think. While many of us consider ourselves capable of critical thinking, researchers say we tend to make snap judgments without using our knowledge.

For example, let’s try an exercise. Take a look at this image:

Did you immediately react based on what you think is about to happen?

Although there isn’t a picture showing what takes place next, most of us made a guess using a tool we weren't even aware of — a mental model. Through our mental model, we could predict a possible outcome (which hopefully didn’t involve any scratches or falls).

Many of our snap judgments and reactions — whether about a photo we see or a problem we encounter — are shaped by the mental models we use to view the world. We begin to develop mental models as soon as we are born and continue to develop them throughout our lives, using them as a thinking tool to make sense of life, solve problems, and make decisions.

We all start out with different sets of mental models — after all, we all have different experiences that shape our early lives. As we gain experiences and knowledge, we add more models to our toolkit and learn to see things in new ways.

Sometimes our mental models work against us. If we limit our thinking to only a few mental models, we can suffer from critical thinking barriers . However, when we actively pursue thoughtful learning and collect many mental models, they can be extremely valuable tools for critical and creative thinking.

Munger's Latticework of Mental Models

Mental models as thinking tools were first made popular by Charlie Munger in his 1995 " The Psychology of Human Misjudgment " speech at Harvard University. Entrepreneurs and thinkers have since embraced mental models to achieve success.

According to Munger's Latticework of Mental Models theory, we can use various thinking tools to see problems from several points of view. Combining mental models increases original thinking, creativity, and problem-solving skills instead of relying on one frame of reference.

As Munger said , "All the wisdom of the world is not to be found in one little academic department ... 80 or 90 important models will carry about 90 percent of the freight in making you a worldly-wise person. And, of those, only a mere handful really carry very heavy freight."

This is why we need to keep learning — to expand our toolbox. The more mental models we have in our toolkit, the easier it is to find one that works for the situation.

A well-stocked toolbox is more effective at solving a problem than a single nail.

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13 valuable tools for your thinking skills toolkit

There are hundreds of mental models and thinking tools available, which can be overwhelming. Most of us are familiar with concepts like the Eisenhower Matrix and brainstorming. However, we can use many other mental models for creative and critical thinking. Here are 13 thinking tools to boost decision-making, problem-solving, and creative thinking skills.

1. First Principles

First principle thinking is a mental model that can be used for problem-solving by breaking things down to the most basic level. This thinking tool is based on the idea that all complex problems can be reduced to more specific, fundamental parts. Using first-principles thinking, you identify the underlying causes of a problem and then find the best solutions that address those root causes.

For instance, it would be impossible to pack up your entire house at once if you were moving. To pack efficiently and safely, you’d need to go room by room, tackling one room at a time.

2. Inversion

Inversion is a technique used to generate ideas of creative solutions to problems by imagining the opposite of them. Inversion is higher-order thinking that requires thinking about the solution you don't want. With inverted thinking, you consider how something might fail and then try to avoid those mistakes. This approach differs from "working backward," another way of doing things that encourages you to begin with the desired end solution in mind.

3. Occam's Razor

Occam's Razor is a mental model that can simplify complex problems and situations by determining which explanation is most likely. This thinking tool is based on the principle that the simplest answer is usually correct. When using Occam's Razor, you should look for the most obvious, straightforward reasoning that fits all facts.

4. Bloom's Taxonomy

Bloom's Taxonomy is a mental model used for categorizing the knowledge levels of learners. The cognitive, affective, and psychomotor learning domains are grouped into three hierarchical levels, with each level encompassing the previous one. In a hierarchical structure, areas of knowledge begin with simple skills and progress to higher-order thinking.

The six levels of Bloom's Taxonomy are:

• Knowledge: Recalling or recognizing facts and information
• Comprehension: Understanding the meaning of information
• Application: Using information in new ways
• Analysis: Breaking down information into smaller parts
• Synthesis: Putting pieces of information together to form a new whole
• Evaluation: Making judgments about the value of information

By applying the actions from each level of this tool, we can analyze situations from different angles and find more comprehensive solutions.

5. Incentives

Incentives are a model that can be used to encourage desired behavior. Based on a cause and effect concept, people will be more likely to act if they are given an incentive to do so. The incentives can be monetary, such as a bonus or commission, or non-monetary, such as recognition or privileges.

6. Fundamental Attribution Error

The fundamental attribution error is characterized by the tendency to focus too much on personal characteristics and not enough on circumstances when judging others. This mental model believes that people's actions reflect who they are without considering their point of view. This can lead to misunderstanding and conflict.

For example, it's easy to get angry and lash out at someone who cuts you off in traffic without considering that maybe they are rushing to the hospital for an emergency. Keeping this model in mind can help us avoid over-simplifying behavior.

7. Law of Diminishing Returns

The Law of Diminishing Returns provides a way to determine when it’s no longer efficient to continue investing in something. This thinking tool is based on the idea that there’s a point at which additional investment in something will result in diminishing returns.

The law of diminishing returns is often used in higher-level business decisions to determine when to stop investing in a project, but it’s also used in other forms of decision-making. Research has found that decision-makers tend to use a "matching" strategy in which they make their choice based on the relative value each option has.

8. Redundancy

The redundancy theory suggests that learners retain less new knowledge if the same information is presented in multiple ways or if it’s unnecessarily elaborate. Studies have shown that using several sources to relay information, such as text, visuals, and audio can create a lack of focus and less learning. Integrating the redundancy model can help teachers and leaders make learning more efficient.

9. Hanlon's Razor

Hanlon's Razor is a mental model that suggests most mistakes are not made maliciously. The purpose of this tool is to remind us not to assume the worst in the actions of others. Hanlon's Razor can help us see the situation from another's point of view and have more empathy, therefore avoiding making wrong assumptions.

For example, friends who aren't answering their mobile phones most likely aren't mad at you. Maybe they're just busy, or perhaps there are various other reasons to explain their delay.

10. Common Knowledge

We usually think of common knowledge as universal facts most people understand. However, the mental model of common knowledge is a little different. Used as a thinking tool, it focuses on pooling together the knowledge we don't share and taking into account the wisdom of others to help us make better decisions. Brainstorming, creating concept maps, and integrating feedback are useful tools we can use to share common knowledge.

11. Survivorship Bias

Survivorship bias refers to the tendency to focus on successful people, businesses, and strategies while overlooking failed ones.

For example, the idea that all 21st-century Hollywood stars got there through hard work may underestimate the amount of networking used to achieve fame. The idea dismisses the millions of other actors who worked just as hard but didn't have the same connections.

This thinking process can lead to decision-making errors because it causes people to overestimate their chances of success. However, when used to frame thinking, understanding the survivorship bias can help us consider other points of view and avoid making incorrect decisions.

12. The Ladder of Inference

The Ladder of Inference is a mental model that helps explain why we make judgments quickly and unconsciously. The ladder illustrates the rapid steps our minds go through to make decisions and take action in any given situation. The seven steps are:

• Observations: The data or information that we carry in through our senses
• Selected Data: The process of our brain choosing which information is important and which to ignore
• Meanings: Making interpretations and judgments based on our experiences, beliefs, and values
• Assumptions: The views or beliefs that we hold that help us interpret the facts
• Conclusions: The decision or opinion that we form based on our assumptions
• Beliefs: The convictions that we have about ourselves and the world around us
• Actions: The way we act or respond based on our thoughts

Using the Ladder of Inference as a thinking tool can help us avoid rash judgments based on assumptions and ensure sound thinking.

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13. 80/20 Rule

The 80/20 Rule is a thinking tool that we can use to understand the relationship between inputs and outputs. This model is based on the idea that 80% of the results come from 20% of the effort. The 80/20 rule can be used to decide how to allocate resources.

Thinking tools are essential for a learner's toolkit

Every lifelong learner should have a toolbox of thinking tools. Mental models are helpful thinking tools that can enhance the creative and critical thinking processes. By having more tools at your disposal, you can approach any situation from various angles, increasing the probability of finding a successful solution.

Remember — building your thinking toolkit is an ongoing process. Keep learning, and you'll soon find that you're making better decisions consistently and solving problems more quickly.

I hope you have enjoyed reading this article. Feel free to share, recommend and connect 🙏

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7 Mental Models For Problem-Solving To Avoid Catastrophic Mess

• Self-improvement

Problem-solving rarely comes to mind until we’ve implemented a fix. That’s why we often say: “Oh, I had to do this, instead. Not go with the usual fix.” We think more about alternative solutions after we’ve encountered a problem, not before that. Why this is the common way we act? And can we do something about it?

To solve problems faster and better. It’s not enough to have experience in a given field. After all, life places us in all kinds of situations. Incidents where we don’t always have prior knowledge, but we still need to act in some way.

This is among the reasons why it is so difficult to emerge successful in every possible situation. We fail. And we fail often.

Fortunately for us. We can learn basic rules that can be applied to virtually everything.

No, I’m not talking about wishy-washy things like “Believe in Yourself!”

I’m talking about something more practical.

I’m talking about the process of problem-solving.

Good problem-solving is good thinking. And good thinking happens when you add more cognitive shortcuts to your mental toolbox. That is, understand the best mental models for problem-solving.

Why Mental Models Are Important In Problem-Solving?

Mental models are exceptionally useful when dealing with problems. They provide a cure that prevents our flawed way of thinking from steering us towards the wrong choice.

The main benefits are the following three:

• Pause and don’t allow your initial solution to be the actual solution.
• Understand what type of biases can distort your thinking.
• Find better solutions by avoiding assumptions and focusing on real facts.

Now, since we know how mental models help with problem-solving. Let’s see what are the best tools you should consider adding to your mental toolbox:

The 7 Mental Models For Problem Solving:

1. the map is not the territory, 2. do something syndrome, 3. first-conclusion bias, 4. social proof (safety in numbers), 5. tendency to distort due to liking/loving or disliking/hating, 6. two-front war, 7. the law of diminishing returns.

The meaning of the expression the map is not the territory is the following: Maps are representations of reality, but they are not reality.

The latter is extremely important.

You can’t blindly trust your GPS device when you drive. You use it for navigation. But you still need to keep your eyes on the road.

With this in mind, think about a recent problem you’ve encountered.

Looking solely at the “map” – statistic, data, what people share. Won’t always solve the problem. Most of the time, you need more information. More real-world data.

Rarely things are exactly like the map. After all, maps don’t show fallen trees and flooded rivers.

Similarly, a resume doesn’t show the actual skills of the person. They only represent what the person thinks he knows. Two different things.

So, when there is an unpleasant situation. Don’t simply stare at data and statistics. Explore the terrain by yourself to see what’s actually happening.

The do something syndrome explains that we are taught to act. When something happens, we want to “do something” with the intention to solve the problem faster.

But doing something is not always the best decision. A lot of times, not doing, is way better.

To illustrate this, think about investing your money in stocks.

If you always do something when stock prices go down, you will not only lose money. You will lose your sanity.

Doing things is a wonderful method to create the illusion of making things better. Making progress. But always doing something is a sure way that will lead to inefficiency and confusion.

When problems arise. Before you act. Pause for a moment and consider doing nothing. You will find out that the opposite of doing – not doing. Is way better on a lot of occasions.

The first-conclusion bias mental model is an interesting way of thinking we all have.

Charlie Munger explained it best: “The mind works a bit like a sperm and egg: the first idea gets in and then the mind shuts.”

Even if your first idea sounds good. Don’t settle.

Remember that the mind is always trying to save energy. When we’re looking for new solutions to old problems – or problems in general. The moment an idea morphs in your brain is the moment you’ll stop trying hard to find new ideas.

Once you understand that first ideas are the things that block our thinking. Strive to come up with something fresh. Go to a different place. Take a shower. Go for a run. Change your location to stimulate your thinking.

When we see a lot of positive reviews for a product. We are immediately sold on the idea that “this” product is the right one because it has the right amount of reviews.

But before you try to solve your problems with the most expensive solution because it’s flooded with 5-star ratings that we are not sure if they are real. Think about alternatives. What else can you do? Are the reviews real?

When we don’t know how to act, we turn to others for advice. And when many people are doing the same thing, we tend to mimic their behavior.

But is their behavior the thing we should do in our situation?

The safety in numbers mental model explains that we look at the crowd – what others are doing – to justify our behavior. But this doesn’t mean that it’s the right behavior.

For example, if we smoke, even though we know that smoking is bad. We feel fine because there are these other people who do the same. “We can’t all be wrong,” we tell ourselves.

If your thinking is based on the thinking of other people. You’re not thinking at all. 

Don’t accept things simply because they have a nice rating, for example. Or because a lot of people are doing a certain activity. Question everything to find the best solution.

We tend to favor comments and suggestions from people we like and disregard the same things from people we are not particularly close to.

You might be skeptical, but even the people we dislike have things to teach us. 

Conversely, the ones we adore often suggest absurd solutions.

We adjust the way we think about what someone said based on our relationship with the person. How is this helpful, though?

Well, it’s not.

Our tendency to distort our thinking based on how much we like someone is detrimental.

Being objective is crucial when you communicate with others. Don’t add or reduce the value of the statement simply because of how you feel about the person. Think about what the person said. Not who said it.

When you need to make a couple of important decisions. Pause. Don’t try to solve different problems at the same time. Don’t split your cognitive power in different locations. Focus your efforts.

The two-front war mental model explains that when our forces are split, we weaken their power. The same thing happens when we try to solve – or try to do – a couple of things at the same time.

We are stronger when we’re fully concentrated on one thing. So, when confronted with a two-front war, avoid one.

But there is an additional application contrary to the above.

You can deliberately open a two-front war to focus your efforts.

If you’re trying to quit social media , for example, but if you’re too tempted when your smartphone is with you. You can replace the smart device with a flip phone to focus your efforts on something else – figure out how to operate in the smart world with an unsmart device.

When we have a problem, we don’t always need to add more people or more resources to solve the situation. Often, it’s helpful to reduce units or the number of people working on the issue to correct the situation.

Theoretically, the law of diminishing returns means that after a system reaches an optimal level of productivity, adding additional adjustments can result in smaller gains.

For example, let say that you are a construction worker. To increase your salary, you start working 2 hours extra a day. This might work for a bit but after a while. This might lead to burnout and even get you sick. Therefore, more hours do not always mean more pay.

Here’s another example:

Technical progress might allow us to extract more resources from forests, but if we don’t plant trees. And if we don’t wait for them to grow. Eventually, we won’t have forests to extract resources from.

When approaching problems, it’s useful to think about this principle for a lot of reasons. The main one is that you can reduce efficiency by trying too hard to be more efficient.

Some Closing Thoughts

What’s the best solution when facing a problem?

It’s difficult for a person to know. We rely on our prior experience and on what’s visible.

But these two are only part of the reality. To be a bit closer to the best solution depending on the situation. Besides being active learner, we need to consistently challenge our initial assumptions.

Consider the list of mental models above and the shared examples every time when you’re facing a problem.

We don’t make poor decisions. We have a poor decision-making process.

Improving the process will improve the quality of our choices.

Hopefully, the mental models for problem-solving will give you extra power when facing an issue.

For more on mental models, consider the following:

• Mental Models in Psychology
• Mental Models for Learning
• Mental Models in Business
• Mental Models for Decision-Making
• Mental Models In Systems Thinking

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Unleash Your Inner Problem-Solver: Adopting Mental Models for Success

Unlocking your problem-solving potential.

When it comes to problem-solving, embracing mental models can be a game-changer. Mental models are frameworks or structures that help us make sense of the world and navigate complex situations. By adopting these models, you can enhance your problem-solving abilities and approach challenges with a fresh perspective.

Embracing Mental Models for Success

To unlock your problem-solving potential, it’s important to embrace the power of mental models . These models provide you with a set of tools and frameworks that can help you analyze problems, generate creative solutions, and make informed decisions.

Mental models are derived from various disciplines, including psychology, cognitive science, and philosophy. They are cognitive constructs that represent how we understand and interpret the world around us. By utilizing mental models, you can tap into a wealth of cognitive processes and decision-making models that have been developed and refined over time.

How Mental Models Can Enhance Problem-Solving Abilities

Mental models can significantly enhance your problem-solving abilities in several ways. First, they provide cognitive strategies and thinking frameworks that help you break down complex problems into manageable chunks. These models allow you to see patterns, connections, and relationships that may not be immediately apparent.

Second, mental models enable you to leverage cognitive shortcuts and mental shortcuts to quickly evaluate options and make effective decisions. By relying on these established frameworks, you can save time and mental energy, allowing you to focus on finding innovative solutions.

Third, mental models encourage critical thinking by challenging assumptions and biases. They provide a structured approach to problem-solving, helping you to identify potential blind spots and consider alternative perspectives. This promotes a more comprehensive and balanced analysis of the problem at hand.

By adopting mental models, you will expand your cognitive toolbox and develop a repertoire of mental frameworks that can be applied to various problem-solving scenarios. These models will sharpen your analytical skills, foster creativity, and ultimately empower you to overcome obstacles and achieve success.

In the following sections, we will explore specific mental models that are particularly useful for problem-solving. These models include Occam’s Razor, the Pareto Principle, and the Circle of Influence. By understanding and applying these mental models to real-life scenarios, you will gain practical insights on how to approach problems with clarity and efficiency.

Understanding Mental Models

To become a more effective problem-solver, it’s important to understand the concept of mental models . Mental models are cognitive frameworks or representations that help you make sense of the world and navigate complex situations. They are the mental constructs you use to interpret information, analyze problems, and make decisions. By adopting and utilizing mental models, you can enhance your problem-solving abilities and improve your decision-making process.

What are Mental Models?

Mental models are like mental tools that you can use to understand, explain, and predict various phenomena. They are the lenses through which you perceive the world around you. These models are derived from your experiences, knowledge, beliefs, and cognitive processes. They provide you with a structured way of thinking and reasoning about problems and situations.

Mental models can take various forms, such as logical reasoning models, thinking frameworks, critical thinking models, or problem-solving models. They help you simplify complex problems, identify patterns, and make connections between different pieces of information. By internalizing these models, you develop a set of mental shortcuts or cognitive strategies that enable you to approach problems more efficiently and effectively.

The Power of Mental Models in Decision Making

The power of mental models lies in their ability to facilitate decision making. They provide you with a systematic approach to problem-solving, allowing you to evaluate options, anticipate consequences, and weigh potential outcomes. Mental models help you organize your thoughts, analyze information, and make informed choices.

By adopting mental models, you can overcome cognitive biases and avoid common pitfalls in decision making. These models enable you to consider multiple perspectives, challenge assumptions, and think critically. They also help you to identify relevant information, filter out noise, and focus on the key factors that influence the problem at hand.

Internalizing mental models is a continuous process that requires practice and exposure to diverse problem-solving scenarios. As you develop your mental model toolkit, you can explore additional models that align with your specific interests or areas of expertise. Check out our article on cognitive processes for more insights into the cognitive strategies involved in problem-solving.

In the next section, we will explore some popular mental models that can enhance your problem-solving abilities and provide practical applications in real-life scenarios. By incorporating these models into your decision-making process, you can unleash your inner problem-solver and achieve greater success in various aspects of your life.

Popular Mental Models for Problem-Solving

When it comes to problem-solving, mental models can serve as invaluable tools to help you navigate challenges and find effective solutions. In this section, we will explore three popular mental models that can enhance your problem-solving abilities: Occam’s Razor , the Pareto Principle , and the Circle of Influence .

Occam’s Razor

Occam’s Razor is a mental model that encourages simplicity in problem-solving. According to this principle, the simplest explanation is often the most likely one. When faced with a complex problem, Occam’s Razor suggests that you should prioritize explanations or solutions that require the fewest assumptions or elements.

By applying Occam’s Razor, you can streamline your problem-solving process and avoid unnecessary complexity. This mental model helps you focus on the key factors and underlying causes of a problem, enabling you to arrive at a more efficient solution. To learn more about critical thinking models and other cognitive tools, check out our article on cognitive tools .

The Pareto Principle

The Pareto Principle, also known as the 80/20 rule, states that approximately 80% of the effects come from 20% of the causes. This mental model suggests that a small number of factors or actions often have a disproportionately significant impact on the outcome of a situation.

By understanding and applying the Pareto Principle, you can prioritize your efforts and resources to focus on the most influential aspects of a problem. This allows you to maximize efficiency and achieve optimal results. To explore more mental models for decision-making and problem-solving, visit our article on mental models for decision-making .

The Circle of Influence

The Circle of Influence is a mental model introduced by Stephen Covey in his book “The 7 Habits of Highly Effective People.” This model encourages individuals to focus their time and energy on things they can control or influence, rather than wasting resources on things beyond their control.

When applying the Circle of Influence to problem-solving, it’s important to identify the aspects of a problem that you have the power to change or influence. By directing your efforts towards these areas, you can make meaningful progress and increase your chances of finding successful solutions. To explore more mental models for self-improvement and success, check out our article on mental models for self-improvement .

By incorporating these popular mental models into your problem-solving process, you can enhance your ability to tackle challenges effectively. Remember to apply Occam’s Razor to simplify complex problems, leverage the Pareto Principle to focus on the most impactful factors, and utilize the Circle of Influence to direct your efforts towards areas within your control. These mental models will empower you to approach problem-solving with a strategic mindset, leading to more efficient and successful outcomes.

Applying Mental Models to Real-Life Scenarios

Now that you have a good understanding of mental models and their significance in problem-solving, it’s time to explore how you can apply these models to real-life scenarios. Let’s dive into three popular mental models: Occam’s Razor , the Pareto Principle , and the Circle of Influence .

Problem-Solving with Occam’s Razor

Occam’s Razor is a useful mental model that suggests the simplest explanation is often the correct one. When faced with a problem, applying Occam’s Razor encourages you to prioritize the solution that requires the fewest assumptions or steps. By eliminating unnecessary complexity, you can streamline your problem-solving process and increase your chances of finding an effective solution.

To apply Occam’s Razor, start by identifying the core issue and focusing on the most straightforward explanation or solution. Avoid overcomplicating the problem by introducing unnecessary elements. This mental model helps you avoid getting lost in intricate details and guides you towards a more efficient problem-solving approach.

Maximizing Efficiency with the Pareto Principle

The Pareto Principle, also known as the 80/20 rule, is a mental model that suggests that approximately 80% of the effects come from 20% of the causes. Applied to problem-solving, the Pareto Principle reminds us to focus on the vital few factors that have the most significant impact.

When faced with a problem, consider identifying the key factors or causes that contribute to the issue. By focusing your efforts on addressing these critical elements, you can maximize efficiency and achieve more impactful results. This mental model helps you prioritize your resources and avoid wasting time and energy on less influential aspects of the problem.

Focusing on What You Can Control with the Circle of Influence

The Circle of Influence is a mental model that emphasizes focusing on what you can control rather than what you cannot. When encountering a problem, it is essential to distinguish between factors within your control and those outside of it. By directing your attention and efforts towards the aspects you can influence, you can increase your effectiveness in problem-solving.

To apply the Circle of Influence, evaluate the various elements of the problem and identify the ones you have the power to change or influence. By focusing on these areas, you can take proactive steps and make a meaningful impact. This mental model helps you avoid expending unnecessary energy on factors beyond your control and empowers you to take charge of the problem-solving process.

By integrating these mental models into your problem-solving approach, you can enhance your ability to tackle challenges effectively. Remember, mental models are tools that can help guide your thinking and decision-making, but they should be used in conjunction with your own judgment and experience. Explore additional mental models to expand your problem-solving toolkit and incorporate them into your daily life.

Developing Your Mental Model Toolkit

Now that you have a solid understanding of mental models and their application in problem-solving, it’s time to expand your toolkit by exploring additional mental models and incorporating them into your daily life.

Exploring Additional Mental Models

There are numerous mental models available, each with its own unique perspective and benefits. By exploring a variety of mental models, you can enhance your critical thinking skills and approach problem-solving from different angles. Here are a few additional mental models that you may find useful:

Cognitive Processes : Understanding the underlying cognitive processes involved in decision-making and problem-solving can provide valuable insights into how our minds work. Dive deeper into the intricacies of cognitive processes by exploring articles on cognitive processes .

Decision-Making Models : Decision-making models offer structured frameworks for making sound decisions. These models provide a systematic approach to evaluate options and consider various factors. Learn more about decision-making models by visiting our article on decision-making models .

Cognitive Strategies : Cognitive strategies help optimize our mental processes and improve our problem-solving abilities. They involve techniques such as goal setting, brainstorming, and organizing information. Discover effective cognitive strategies in our article on cognitive strategies .

By exploring these additional mental models, you can gain a broader perspective and develop a diverse set of tools to tackle various challenges.

Incorporating Mental Models into Your Daily Life

To make the most of mental models, it’s essential to incorporate them into your daily life. Here are some practical ways to do so:

Practice Awareness : Start by cultivating awareness of the mental models you have learned. Recognize when and how you can apply them in different situations.

Reflect and Evaluate : Take time to reflect on past experiences and evaluate how specific mental models could have been applied to improve your problem-solving outcomes.

Seek New Perspectives : Continuously seek out new mental models and perspectives. Explore articles and resources on topics such as cognitive frameworks and mental patterns to expand your understanding.

Experiment and Iterate : Apply mental models in various scenarios and assess their effectiveness. Adjust your approach as needed and refine your problem-solving skills over time.

Remember, the key to mastering mental models is consistent practice and application. The more you incorporate them into your thinking process, the more natural they will become. With time and experience, mental models will become an integral part of your problem-solving toolkit, empowering you to unleash your inner problem-solver.

Continue to explore different mental models and their applications in various aspects of life, such as self-improvement , productivity , creativity , leadership , and communication . The more you delve into these concepts, the more equipped you will be to tackle challenges and achieve success in your endeavors.

Here's How You Build a 6-Figure Business With the 3 Engine Framework

In today's world, it's not enough to simply know how to create a digital product.

You need to know the whole system to make your business flourish.

This is why you build a Full Stack Engine. Not only because you need a system that you can maximize, but also a system that allows you to walk away when you need.

Because while the money is great, freedom is better.

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Mental Models: Unlocking the power of effective thinking

Introduction.

As an ardent consumer of investment and decision-making content, I have come across numerous articles, books, and podcasts where the concept of mental models is hailed as a favorite among investors and experts in the field. Time and time again, the term would pique my interest, yet I never truly grasped the idea in its entirety. While I managed to piece together a vague understanding based on the contexts in which it was mentioned, I couldn't shake the nagging feeling that I was missing out on a powerful tool for enhancing my ability to think effectively and make good decisions.

Fueled by curiosity and a desire to broaden my thinking tools, I decided to do some research and reading on the world of mental models to arrive at a better understanding of what mental models are and why they are so important. In this article, I share my findings on the importance of mental models, discuss useful examples, and provide tips on how to apply them to improve your thinking and decision-making.

Where does the idea come from?

The investment community's fascination with mental models can be traced in part to Berkshire Hathaway's long-time Vice Chairman Charlie Munger's enthusiasm for them. Munger often attributes his extraordinary investment success to his ability to evaluate investments using multiple "mental models," a concept he famously discussed in a 1994 speech at the USC Business School .

Although Munger popularized the term, mental modeling theory dates back to the early 1940s and has been extensively studied across various fields, including psychology, cognitive science, and system dynamics, in the decades since it was first introduced.

What is a mental model?

The concept of mental models varies across disciplines, leading to a vast and intricate literature on their definition, presence, understanding, and application. An in-depth account of this vast literature is not possible within the space of this article, but worth acknowledging that it exists. Bearing that in mind, what follows a somewhat cursory definition which I've pieced together from a brief review of the literature. Fortunately, at least in my experience, a precise scientific definition isn't necessary for understanding the importance of mental models and how to use them to enhance your thinking.

A cursory definition

Mental models are cognitive constructs that help us understand the world around us and make effective decisions. They are frameworks that simplify complex systems, provide context for new information, and help us process, analyze, and interpret experiences. Mental models can be thought of as mental shortcuts or "rules of thumb" that help us navigate our everyday lives.

Why are they important?

A broad base of mental models improves your ability to think clearly, rationally, and effectively. Mental models do this by:

• Enhancing problem-solving skills: Mental models enable us to approach problems from different perspectives, identify patterns, and develop innovative solutions. They allow us to break down complex situations into smaller, more manageable parts, making it easier to evaluate options and determine the best course of action.
• Facilitating better decision-making: Mental models assist in filtering relevant information and minimizing cognitive biases. By incorporating diverse mental models, we can analyze situations more objectively and make informed decisions.
• Improving learning and retention: By providing a structure to organize and interpret new information, mental models promote better understanding and memory retention. They help us link new experiences to existing knowledge, facilitating faster and more effective learning.

What are some examples of useful mental models?

Some mental models I've found particularly useful include:

• The Pareto Principle (80/20 Rule): The Pareto Principle, also known as the 80/20 Rule, is a mental model that can be applied across various fields, including business, economics, and time management. The principle posits that 80% of the outcomes are derived from 20% of the causes. In a business context, this may mean that 80% of a company's profits come from 20% of its customers, or 80% of a project's progress can be attributed to 20% of the tasks. By identifying and concentrating on the critical 20% of tasks or inputs, individuals and organizations can maximize their productivity, effectiveness, and resource allocation. The Pareto Principle can also help in prioritizing and streamlining decision-making processes, allowing people to focus on the most significant factors that will yield the most considerable impact.
• First Principles Thinking: First Principles Thinking is a mental model that involves deconstructing complex problems into their most basic elements and questioning the underlying assumptions. By analyzing a problem from the ground up, individuals can gain a deeper understanding of the core principles and develop innovative, creative solutions that are not constrained by conventional wisdom or established practices. This approach can be applied in various fields, including science, technology, and business, to foster critical thinking and problem-solving skills. First Principles Thinking can help individuals overcome cognitive biases, question the status quo, and identify novel approaches or perspectives that may have been overlooked in traditional problem-solving methods.
• Circle of Competence: The Circle of Competence is a mental model that emphasizes the importance of focusing on areas where an individual possesses expertise or deep understanding. By acknowledging one's limitations and concentrating on domains within one's circle of competence, individuals can make more informed decisions, minimize the risk of costly mistakes, and increase the likelihood of success. This concept can be applied to investing, career development, and personal growth, among other areas. To expand one's circle of competence, it is essential to engage in continuous learning, skill development, and knowledge acquisition. By understanding and respecting the boundaries of one's circle of competence, individuals can develop self-awareness, avoid overconfidence, and ultimately make better decisions in both personal and professional settings.

How should you apply them?

• Develop a mental model toolbox: Expose yourself to various mental models across different disciplines to build a diverse cognitive toolkit. Reading books, articles, and attending workshops can help you acquire new models.
• Choose the right model for the situation: Different mental models are better suited for different situations. Assess the problem at hand and select the appropriate model to guide your thinking and decision-making.
• Practice using mental models: Apply mental models to everyday situations and challenges to strengthen your understanding and improve your ability to use them effectively.
• Challenge your assumptions: Regularly question your beliefs and assumptions, and consider alternative perspectives. This helps to reduce cognitive biases and improve the accuracy of your mental models.

Mental models are powerful cognitive tools that can help us make better decisions, solve complex problems, and enhance our learning capabilities. By developing a diverse set of mental models and applying them effectively, we can unlock the full potential of our minds and achieve greater success in both our personal and professional lives.

Publications

• Béland, F., & Cox, R. H. (2021). The Importance of Mental Models in Implementation Science. Frontiers in Public Health, 9, 680316. https://doi.org/10.3389/fpubh.2021.680316
• Jones, N. A., Ross, H., Lynam, T., Perez, P., & Leitch, A. (2011). Mental Models: An Interdisciplinary Synthesis of Theory and Methods. Ecology and Society, 16(1). https://doi.org/10.5751/es-03802-160146
• World Bank. (2015). Mind, Society, and Behavior. Retrieved from https://www.worldbank.org/content/dam/Worldbank/Publications/WDR/WDR%202015/Chapter-3.pdf
• Rook, L. (2013), Mental Models: A robust definition, The Learning Organization, Vol. 20 No. 1, pp. 38-47. https://doi.org/10.1108/09696471311288519
• Doyle, J.K. and Ford, D.N. (1998), Mental models concepts for system dynamics research. Syst. Dyn. Rev., 14: 3-29.  https://doi.org/10.1002/(SICI)1099-1727(199821)14:1<3::AID-SDR140>3.0.CO;2-K
• Groesser, S.N. (2012). Mental Model of Dynamic Systems. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_1838
• Clear, J. (n.d.). Mental Models: How to Train Your Brain to Think in New Ways. Retrieved from https://jamesclear.com/mental-models
• Farnam Street. (n.d.). Mental Models. Retrieved from https://fs.blog/mental-models/
• Ness Labs. (n.d.). Mental Models: A Beginner's Guide. Retrieved from https://nesslabs.com/mental-models
• Vikhornova, A. (2018, March 1). What We Can Learn from the History of Systems Thinking. Medium. Retrieved from https://medium.com/systems-thinking-for-non-systems-thinkers/what-we-can-learn-from-the-history-of-systems-thinking-79852d8955c4
• Egan, C. (2021). Mental Models: How Peter Senge's Theory Can Change Your Life. Shortform. Retrieved from https://www.shortform.com/blog/mental-models-peter-senge/
• Weiss, R. J. (2021). Charlie Munger's Mental Models Explained. The Ways to Wealth. Retrieved from https://www.thewaystowealth.com/investing/charlie-munger-mental-models-explained/
• Model Theory. (n.d.). What are Mental Models? Retrieved from https://www.modeltheory.org/about/what-are-mental-models/
• NDSU (2022). The Importance of Mental Models. NDSU Career and Advising Center. Retrieved from https://career-advising.ndsu.edu/blog/2022/11/01/the-importance-of-mental-models/
• McKinsey & Company. (n.d.). The beginning of system dynamics. Retrieved from https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/the-beginning-of-system-dynamics#/
• LessWrong. (n.d.). An overview of the mental model theory. Retrieved from https://www.lesswrong.com/posts/YKCoj7DxDMktr4qKP/an-overview-of-the-mental-model-theory
• The Systems Thinker. (n.d.). What are mental models? Retrieved from https://thesystemsthinker.com/what-are-mental-models/

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Mental Models

What are mental models.

Mental models are representations of the world that help us understand complex concepts and make better decisions.

They provide a framework for thinking and problem-solving, allow us to view problems from different angles and generate creative solutions, and help us become more effective thinkers and problem solvers. 

• Transcript loading…

We create mental models based on past experiences, beliefs, and assumptions to understand how the world works. Mental models can be conscious or unconscious, varying in accuracy and usefulness depending on the context. 

Mental models are essential for decision-making, problem-solving, and learning, as well as effective communication and collaboration in group settings. However, mental models can also lead to bias and errors if they are incomplete, inaccurate, inflexible, or resistant to change.

Mental Models in UX Design

“Users spend most of their time on other sites. This means that users prefer your site to work the same way as all the other sites they already know." — Jakob’s Law (Jakob Nielsen)

Mental models are important in creating user-friendly interfaces. Designers research users' mental models to create designs that align with their expectations and beliefs. Research takes various forms, such as ethnographic research through surveys or observation. If interfaces match users’ expectations, they do not have to learn new concepts or behaviors. For example, a shopping cart icon is a standard mental model for e-commerce websites. Skeuomorphic design elements, like virtual buttons that resemble real-world buttons, also help users. 

Mental models help people understand the world—they simplify complex concepts. Every individual forms their own mental model, and different people might form different models for the same interface. This is why we cannot rely on any one mental model to solve problems. Designers know this and have developed principles and methodologies like Jakob's Law and design thinking to understand their users' mental models better.

Jakob's Law emphasizes consistency in user experience design. Users may need support with unfamiliar design patterns, leading them to abandon tasks. Designs that align with users' mental models can address this issue. For example, if the designer places the navigation menu in an unexpected location, users may struggle to find it.

Design thinking is a problem-solving approach that aims to understand users' needs and preferences by involving them in every stage of the design process. Designers using this method often conduct user research, create personas, and then conduct user testing to identify potential problems with their designs.

How to Communicate Mental Models

As mental models are abstract, we can use different formats to communicate them. Each form has its unique advantages and applications: 

Conceptual Models: Conceptual models are used in HCI and interaction design as a way for designers to communicate how they interpret users' mental models to stakeholders, team members, and developers. Some examples of conceptual models are diagrams, flowcharts, or narratives. They are often used in science, engineering, and design to develop and test hypotheses, communicate complex ideas, and guide decision-making.

For example, a conceptual model of a forest could include wildlife, insects, trees, etc., their roles, how they interact and the different life stages they go through. This model can predict the effects of, say, introducing a new species or climate change.

Visual Models: Visual models describe data, concepts, or processes, such as diagrams, charts, graphs, maps, infographics, and animations. Visual models are often used in science, engineering, education, and business to simplify and make information more accessible . Compared to conceptual models, visual models provide more detailed and specific information.

A user flow is a visual representation of a user's path to accomplish a task on a website or app. It shows the steps involved in the process, including any user's decision points or actions.

© Interaction Design Foundation, CC BY-SA 4.0

Applications of Mental Models in Everyday Life

We can use mental models in everyday life to understand our environment better and make more informed decisions. 

Problem-solving: An example of problem-solving through mental models is the 5 Whys. The 5 Whys can help you understand how a user thinks and diagnose the cause of a problem with a series of "why" questions.

Decision-making: Mental models help us analyze the potential consequences of different decisions and identify which is most likely to lead to a desirable outcome. An example is the "cost-benefit analysis," which evaluates the costs and benefits of different options regarding financial, social, or environmental impacts.

Critical thinking: Methods like the scientific or Socratic methods help you question your assumptions and challenge commonly held beliefs.

The Five Whys method is a problem-solving technique that involves asking "Why?" five times to uncover the root cause of an issue. It helps to understand the underlying mental models that inform decision-making process

Learn More about Mental Models

Learn how to use Mental Models in Mobile UX .

Read more about the importance of mental models in decision-making and critical thinking, using Charlie Munger's approach as an example.

Discover how to create user-friendly designs that align with users' mental models by applying Jakob's Law . 

Don’t miss this excellent masterclass to learn How To Design For The Way Your Users Think .

Learn about mental models and their role in user experience design in this informative article.

Read more about transforming Mental Models into Conceptual Models for Mobile UX .

Literature on Mental Models

Here’s the entire UX literature on Mental Models by the Interaction Design Foundation, collated in one place:

Learn more about Mental Models

Take a deep dive into Mental Models with our course Mobile UX Strategy: How to Build Successful Products .

All open-source articles on Mental Models

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Mental Models: The Ultimate Guide

Updated: October 17, 2018

Published: September 13, 2018

Have you ever wondered why two heads are better than one -- and four heads are better than two? It's because we're all limited by our own experiences, biases, and areas of expertise.

These areas of expertise give rise to "mental models," assets we're using all the time but might not realize we have.

Your coworker in sales has a different background than you, which means she can bring different ideas to the table. You might spend most of your time creating content on a daily basis, giving you a unique understanding of your brand's voice and position in the industry. Meanwhile, your coworker might spend most of her time speaking to customers, giving her visibility into what your industry really wants right now.

Put you and your sales-minded coworker in a room together, and your combined insights can crack a challenging problem.

But it's just not feasible to host a roundtable discussion every time you need to make an important decision. As individuals, we need to be able to think about our business's problems differently every time we encounter a new one.

Luckily, there's a way to hack the decision-making process and reap the "two heads" effect by yourself: mental models.

What is a mental model?

A mental model is the specific thought process you use to examine a problem. There are many types of known mental models, and each one takes a unique view of a foreign concept in order to reduce its complexity. In short, it is the mind's way of making sense of something.

A mental model is a way of examining a problem. As James Clear explains , "Each mental model offers a different framework that you can use to look at life ... If you develop a bigger toolbox of mental models, you'll improve your ability to solve problems because you'll have more options for getting to the right answer. This is one of the primary ways that truly brilliant people separate themselves from the masses of smart individuals out there."

Charlie Munger's Latticework of Mental Models

To really understand mental models today, take a page from Charlie Munger , Vice Chairman of Berkshire Hathaway, Inc. -- a conglomerate holding company that owns such companies as Geico, Dairy Queen, and Helzberg Diamonds.

A close friend and colleague of Warren Buffet, Munger is known for his interest in psychology. On the subject of decision-making -- primarily in business -- he suggests there are numerous mental models that can help us dismantle and solve difficult problems. Says Munger:

"You can't really know anything if you just remember isolated facts and try and bang 'em back. If the facts don't hang together on a latticework of theory, you don't have them in usable form."

It's this latticework of mental models that allows us to adjust our view of a challenge if we need to. After all, not every dilemma is presented to us the same way, or can be decided on from the same vantage point. The more mental models you experiment with, the more adaptable you'll become to the challenges that come your way.

So, where do you even begin to build your own "latticework" of mental models? Take a look at some of the most common mental models below -- a few of them you might be practicing without realizing it.

14 Examples of Mental Models to Practice (and Avoid)

1. bayes' theorem.

This describes the probability of something happening based on potentially relevant factors. These factors include evidence from past results and current conditions that could affect a new outcome.

To give you an idea of how this theorem might look in the marketing industry, imagine you launched an email marketing campaign four months ago that had a 20% open rate. The following month, you launched a similar email marketing campaign with the goal of a 20% open rate, but instead received a 25% open rate. In the third month, your email campaign saw a 26% open rate. Then, last month, you purged your mailing list of contacts who haven't opened an email from your business in the last 60 days -- and subsequently launched another email campaign.

Given the steady increase in your open rate over the last four months, and the fact that you removed your most inactive emails from your contact list, a realistic open rate goal under Bayes' Theorem might be 30%.

2. Circle of Competence

We can thank Warren Buffett for this mental model . In 1996, Buffett told his shareholders, "You don't have to be an expert on every company, or even many. You only have to be able to evaluate companies within your circle of competence. The size of that circle is not very important; knowing its boundaries, however, is vital."

Concentrate on your area of expertise, and don't be afraid to say "I don't know" when you're dealing with someone else's circle of competence.

For example, a HubSpot content creator can write an article that teaches realtors how to use the inbound methodology to attract homebuyers, but she shouldn't try to write about the real estate industry itself. Realtors know far more about their customers and how the industry operates than HubSpot content creators do.

3. Confirmation Bias

This is a human tendency to look for and interpret information in a way that reinforces or confirms what you already believe.

For instance, if you're confident your website's organic traffic for December will exceed its traffic from November, you might focus too much on December's promising traffic level after just the first week, and not enough on the fact that the holidays later into December often cause B2B website traffic to decrease.

To protect yourself against confirmation bias, accept the idea that your perception doesn't always (or even frequently) equal reality. Challenge yourself to find different interpretations of what's happening.

In the above example, you might think, "Is there anything to suggest our organic traffic for December will drop before the month's over? What might stand in the way of our goal?"

Being more skeptical will lead you to probe more deeply for objections -- which, in turn, will help you set more realistic expectations before it's too late.

4. Inversion Mental Model

The inversion perspective is one of the most powerful mental models. Rather than thinking about your desired outcome, consider the outcome you'd like to avoid.

For example, say you want to be promoted to senior marketing manager. Instead of asking yourself, "What are the top five things I could do to get promoted?" ask yourself, "What are the top 10 things that would prevent my promotion?"

Then, you'd make sure to do none of those things.

As Shane Parrish says , "Avoiding stupidity is easier than seeking brilliance." While you won't always find the answer by inverting the problem, you'll definitely improve.

5. Fundamental Attribution Error

We're more likely to believe someone is acting a certain way because of their character than the situation.

In other words, if your social media strategist doesn't show up to a marketing team meeting, you'll probably think, "They're flakey," not "They must have gotten stuck in traffic."

Challenge yourself to give people the benefit of the doubt. Behavior is usually situational, so your predictions of how people will act will be more accurate if you don't chalk things up to "how they are."

6. Hanlon's Razor

If a marketing qualified lead (MQL) goes dark at a critical point in the acquisition process, you're probably going to assume they were "kicking tires" or decided the information they had wasn't good enough to continue the conversation. Hanlon's Razor, however, asks us to "Never attribute to malice what could be explained by carelessness." In other words, it's more realistic to assume the person is busy instead.

7. Jealousy Tendency

There are two types of envy. The productive type is "inferiority," or the desire to raise yourself up to another person's level. Do you want to become as successful as your team's marketing director? You're motivated by this kind of envy.

The unproductive type is malicious envy, or the desire to take something valuable away from someone else -- not for your own means, but so they don't have it.

These motivators are worth remembering when, for example, you're writing website copy for your online visitors. Your visitors might be personally invested in a particular goal because they want to do as well -- or better -- than another person at their company, or beat someone else's record. Identifying your visitors' desires will help you craft landing page copy that seeks to solve their personal goals.

You should also be conscious of the jealousy tendency in your own decision-making process. While a competitive streak (inferiority envy) might benefit you in a fast-moving startup, wanting other people to fail (malicious envy) will only distract you. Overcome envy by reminding yourself of your similarities to this person, which will trigger your empathy, and avoid the temptation to sabotage them. Turn those impulses into growth opportunities: What skill or habit can you improve to get their results?

8. Law of Diminishing Returns

At a certain point, the incremental benefits you get from an investment get increasingly smaller. The first month you go on a diet, for example, you might lose six pounds. The second month you might lose three. The third month you might lose two.

This concept applies to marketing in several ways. First, make sure you're focusing on the most valuable activities. Let's say you've spent a week researching your buyer persona before launching a blog dedicated to them. As crucial as a detailed buyer persona is to your business, know when to call it complete. You're probably not going to double your results by spending another week sizing up your ideal buyer, and the more trivial the details get, the less those details will actually benefit your content. Instead, use that time to research a different buyer and establish multiple audience segments.

To ensure you spend your time on the things that offer the biggest returns, recognize what you need to know to be successful. Developing a brand voice and a series of calls-to-actions for your blog might be more productive than mastering the entire AP stylebook cover to cover.

There are diminishing returns to memorizing obscure details, and the sooner you notice them, the sooner you can jump on the projects that are more valuable to your business's growth.

9. Margin of Safety

A bridge might theoretically handle up to 15,000 pounds, but it would be wise to cap the weight limit at 14,000. It would be a major disaster if the bridge wasn't actually that strong -- and the risk isn't worth it.

The margin of safety is the idea that we should leave ourselves room for mistakes or failures. For instance, when creating your website's conversion goals, you might not count a downloaded ebook as a lead until they've responded to a follow-up email or sought more information from you, just in case they change their mind.

Think of this model as a safety net. It's better to be pleasantly surprised than proven right.

10. Occam's Razor

This principle states the simplest explanation is usually the correct one. If you're trying to understand what happened, develop the most basic hypothesis possible.

11. Opportunity Costs

Every choice comes at the cost of another. If you decide to send emails after lunch, you can't use that time to write a blog post. If you pursue one large, unpredictable lead-generation campaign, you won't have the bandwidth or the risk tolerance to pursue another at the same time.

Keep this in the back of your mind every time you're deciding what to do. What's the alternative? Are you willing to give that up?

12. Pareto Principle

The Pareto Principle, also known as the 80/20 rule, means most results aren't distributed equally. In other words:

• 20% of the work generates 80% of the returns
• 20% of your traffic yields 80% of your leads
• 20% of features are responsible for 80% of your usage
• 20% of your time produces 80% of your results

If you can hone in on your top customers, selling activities, and so forth, you'll be dramatically more successful.

At my former company, for example, we analyzed our customers and found those who spent the most (i.e., the 20% who created 80% of our revenue) worked in HR. Once we knew that, our sales and marketing teams could target HR professionals. As a result, the company's revenue increased by 230%.

13. Preferential Attachment

Imagine two runners competing in a race. The first runner to pass the one-mile mark gets water and a protein bar. The slower one gets nothing.

This describes the preferential attachment, where the leader is given more resources than their competitors. Those resources give them an even greater advantage.

As a marketer, you see this effect in the lead-nurturing process. It can be tempting to spend all your time serving content to your most qualified leads. But in the process, you might be neglecting the people who are in the early stages of learning about your business, or take a bit more time to open their emails and download certain resources.

No matter how much you might "prefer" getting your furthest-along leads into the hands of a salesperson, it's important not to develop preferential attachment to these people at the expense of other website visitors.

14. Redundancy

Along similar lines, redundancy describes what good engineers do to put back-up systems in place to protect against failure. This drastically reduces your chances of total failure.

As a marketer, you can use this strategy to create a campaign that keeps your readers, subscribers, leads, and existing customers happy and educated while also making a bet on a brand new offering. Maybe you're promoting a huge product right now and have an ambitious lead-generation goal to hit next month. Pursue four or five smaller, low-risk content campaigns at the same time to ensure your lead-gen pipeline remains stable while also rolling out your new product.

With these mental models at your disposal, your analytical and decision making skills will exponentially improve.

Don't forget to share this post!

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What’s a mental model? Your shortcut to being more productive, more often

Contributing writer

Everyone has their own way of looking at the world.

The way you view your world and everything in it (yourself, your job, your relationships, your goals…literally everything) is unique—you’re experiencing it through a lens typically constructed from a mix of your beliefs, experiences, biases, and opinions.

But that lens? It’s not always the clearest way to look at things.

For example, when you encounter a problem you’ve never faced before, you’re not going to have the experience to solve it—so looking at the problem through your unique lens isn’t going to get you any closer to a solution.

And that’s where mental models come in.

Mental models allow you to view the world through more tried, tested, and unbiased lenses, and help find solutions to problems that might be out of your personal sphere of experience.

Not only can they be incredibly helpful in giving you deeper insights into the world around you, they can also be extremely useful at taking your career to the next level.

But what, exactly, are mental models? How do they work? And how can you use mental models to reexamine your own thoughts, beliefs, and behaviors, as well as improve your professional life in the process?

We talked to Thomas Oppong , founder of AllTopStartups and creator of Thinking in Models , a course on how to apply mental models for career growth, to get his expert insights on all things mental models—including which mental models you should adopt to thrive in your career.

What Are Mental Models?

First things first, before we jump into how mental models can completely change your career game, let’s quickly cover what, exactly, mental models are. Thomas Oppong explains:

“I think the basic definition of mental models would be just tools, ideas, principles, and perceptions that we consistently use to solve better decisions or to understand life. So every principle…that we use in our daily lives, either in business, career, or in life, [can be considered] mental models.”

So that lens we were talking about earlier, the unique one through which you view the world?

Think of that as your own, personal mental model. But there are other mental models (or other lenses through which you can view things) that you can apply to specific situations that will give you a deeper level of understanding and insight than you’d be able to pull from your own personal experience. This in turn, can help you make better decisions and easily solve a problem.

I’ll use myself as an example. I’m a writer, and I’m pretty good with words (if I do say so myself). But numbers? Not so much. So, if I was to try to figure out how much I need to save each month in order to comfortably retire 30+ years from now—strictly by using my own background and experience—I wouldn’t even know where to begin ($50 a month, cross my fingers, and hope for the best?).

But luckily, I don’t have to approach that problem through my own lens of experience; there’s a mental model for that!

Compounding is an existing mental model that, when applied to investment situations (like retirement), can be used to calculate how much interest will accrue over a specific period of time.

So instead of banging my head against a wall, trying to figure out how much to put aside each month, I can apply the compounding mental model and voila! Retirement planning problem solved.

Simply put, mental models are explanations of how things work.

They guide our thinking, giving us access to these “lenses” or formulas we might not otherwise have had access to, which we can use to supercharge our problem-solving abilities and make the decision-making process easier and faster. Oppong explains:

“Mental models have a lot to offer because…there are so many people who have experimented with different ways to make things better, different ways to make better decisions. And then, they’ve shared these principles [and] ideas with the rest of us. So, as people in their daily lives continue to find them and apply them, [mental models] shorten the decision process and make it easier for us to even do better or make better assumptions or make better decisions.”

In a nutshell, mental models can help us think beyond our own personal experience and provide a kind of mental “shortcut,” making it easier, faster, and more efficient to find solutions for problems.

What’s The Connection Between Mental Models, Productivity, And Success?

Alright, so now that you know what mental models are, let’s talk about how they work—and, in particular, how they’re connected to your career, success, and productivity

There are a few different ways mental models can help you level up your productivity, get more done , and take your career to the next level, including:

• Cutting down on problem-solving time. Imagine if you had to start from scratch to figure out every problem you encountered during the day? By applying mental models, you can more quickly and easily understand the problem at hand—and, more importantly, get to the best and more productive solution.
• Opening your mind to new and different ways of thinking. As mentioned, we all have our own unique way of looking at things—but sometimes, that unique way of looking at things can hold us back. By applying mental models, you can remove yourself from the equation and approach things from a more objective place. And that willingness to open your mind and see things through a new lens? It’s a key to growth—both personal and professional.“Once you’re open to learning [and using different mental models], you probably would be able to identify your own biases or your own traps or your own mental biases that are preventing you from growing,” says Oppong.
• Helping you become more adaptable. The most successful people are the ones who can roll with the punches and adapt to things as they come. The more mental models you understand, the more you’re able to look at things from a variety of different perspectives—and the more adaptable and flexible you’ll be when solving problems.

The Mental Models That Can Help You Take Your Career To The Next Level

You know what mental models are. You know how they can make you more productive. Now, let’s take a look at the specific mental models you need to adopt to thrive in your career.

Circle Of Competence

The circle of competence mental model was developed by Warren Buffet—and while it was originally used as a way to guide investment decisions, it’s also extremely relevant to business.

Your circle of competence is the areas in which you excel. You should always stick within that circle. If you try to move outside of that circle (and focus on tasks where you have a limited understanding or experience), you’re not going to be as effective—and your productivity will tank as a result.

Source: Farnam Street

So, for example, let’s say you’re the CEO of a startup, and your circle of competence includes pitching investors, mentoring your team, and coming up with big-picture strategies.

If you want your business to succeed, that’s where you need to spend your time; if your focus is on tasks outside of your circle of competence (so, for example, managing budgets or writing social media copy), your productivity is going to suffer—and so will your startup.

Take the time to identify your circle of competence. Ask yourself:

• What am I good at?
• What do I love doing?
• Where do I excel?

Then, spend your time and energy there—and figure out how to get rid of the tasks outside of your circle of competence (for example, by hiring an assistant).

Incentives (AKA Reward And Punishment)

The incentives mental model says that all living things (including humans) are inherently incentive-driven—when you understand this principle, you can apply it to your work and incentivize yourself to get things done.

For example, is there a task on your to-do list that you dread every day? If you respond to more positive incentives (or rewards), you could tell yourself: “If I get XYZ task done by 12pm, I can treat myself to an iced coffee at lunch.”

If you’re the kind of person who responds better to more negative incentives (AKA punishment), you could tell yourself “If I don’t get XYZ task done by 12pm, no iced coffee with lunch today.”

Either way, the reward or punishment is the incentive you need to tackle your dreaded work task, making it easier to get it done—increasing your productivity in the process.

Regret Minimization Framework

Sometimes, the decision that provides a sense of instant gratification isn’t the best or most productive—and that’s where the regret minimization framework comes in.

“One [mental model] that I’ve found to also be very helpful is… the Regret Minimization Framework ,” says Oppong.

Developed by Amazon CEO Jeff Bezos, the regret minimization framework allows you to think beyond the present moment and assists in making the best decision for your future.

Oppong explains:

“Whenever you’re taking an action, instead of just making a choice that would benefit you in the short term, you focus on the long term. Long term not just in the next five or ten years but…the next 15 or 20 years.”

This mental model gives you a framework for evaluating your decisions and how those decisions are going to impact your success and well-being in the long term.

So, for example,  you get offered a promotion at work, but it would require you to put in longer hours at the office. If you were only thinking about your short-term benefit, you might be tempted to say no (who wants to work late when there’s Netflix to watch?!). But by applying the regret minimization framework, you can assess how the decision is going to impact you in the long-term and come to the realization that, 10 years from now, you’d probably come to regret turning down a promotion for more time with your Netflix queue.

Law Of Diminishing Returns

The law of diminishing returns is defined as “the point at which the level of profits or benefits gained is less than the amount of money or energy invested”—you can use this mental model to maximize your efficiency and make sure you’re getting the most productivity bang for your buck.

The key to successfully using the law of diminishing returns?

Identifying the “point” where the energy outweighs the benefit and making sure to work up to that point, but never past it.

Here’s a great example of the law of diminishing returns: The number of hours you work every day.

Everyone hits a point where the juice is no longer worth the squeeze, where they’re technically “working,” but they’re so mentally exhausted, they’re not actually getting things done.

Identifying the time of day when you hit that point—and working until then, but not a minute after—will help you maximize your productivity every day (without veering into burnout territory ).

Fixed And Growth Mindset

According to this mental model, pioneered by Stanford psychologist and researcher Carol S. Dweck in her book, Mindset , there are two ways to view yourself: With a fixed mindset or a growth mindset.

If you have a fixed mindset, you believe who you are—including your skills, talents, and abilities—are set in stone. You are who you are, and really? There’s no changing.

A growth mindset is just the opposite, it’s the belief that your skills, talents, and abilities are constantly evolving. There’s always the possibility for change and, as the name implies, growth.

When you approach your career with a growth mindset, you’ll always leave the door open for new opportunities to learn, grow, and change—no matter how successful you are.

“If you have a growth mindset, then you’ll be able to [grow]…wherever you find yourself, even if you find yourself at the top of the corporate ladder, you will still be able to grow,” says Oppong.

Adopting a growth mindset will not only make for a more successful and productive career—but it will also make for a more exciting one When you’re open to growing, learning, changing, and evolving, who knows where you’ll end up?

Use Mental Models To Skyrocket Success

Everyone has their own way of looking at the world. But mental models allow you widen that perspective and find new, different, and more efficient ways to think and work—which is a key factor in skyrocketing your productivity and career and claiming the success you deserve!

Next: How To Beat Decision Fatigue With Better Brain Habits

Advice, stories, and expertise about work life today.

10 Mental Models Developers Can Use to Get Unstuck

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What Is a Mental Model?

How do mental models help developers think better.

• Mental Model 1: Rubber Ducking

Model 2: Circle of Competence

Model 3: feedback loops, model 4: mindmaps, model 5: hill charts.

• Model 6: Parkinson’s Law

Model 7: 5 Whys

Model 8: inversion.

• Model 9: Occam’s Razor

Model 10: Lean Startup

Pick the right mental model, frequently asked questions (faqs) about mental models for developers.

Start experimenting with ten mental models you can use to get unstuck, look at difficult problems from new angles, verify your assumptions, and understand systems more deeply.

How do you quickly recover when you’re stuck in a rut? 

Naturally, you could sit down and brainstorm solutions. Unfortunately, it may take time for inspiration to come when solving a complicated challenge with your code.

What can we do to think better and solve problems faster?

Whether you want to identify the root cause of a problem or understand the ideal way to prioritize, mental models could offer valuable insights. 

A mental model is a way for us to understand the world. Mental models are frameworks that help us understand how our minds work and why we think the way that we do. We can also use mental models to rationalize concepts.

Mental models are not always right. They are a simplified way of thinking that can help us understand things better. We can use these insights to take action.

Mental models are powerful because they’re flexible. Like metaphors, mental models let us understand things that we don’t know by comparing them to what we already know.

For example, game theory is a branch of mathematics focused on analyzing the actions and counteractions of individuals or groups. It’s a rigorous form of mental modeling that allows us to explore concepts such as decision-making, strategy, and even reciprocal relationships with others.

As human beings, it’s easy for us to underestimate the power of these tools. We often forget how much thinking goes into our daily routines. In fact, mental models can help us examine how we work and why we think the way we do.

Our brains’ mental models determine the quality of our thoughts. Understanding which mental model best fits a situation can help you work and think smarter. 

For developers, mental models can benefit your productivity and efficiency. It could enable you to understand the problem, correct high-level issues in the code, and avoid potential bugs.

Consider this scenario.

You’re in the zone and writing code at a fast pace when something goes wrong. You check the source code, iterate potential solutions, invoke a debugger, or analyze stack traces.

When done right, you can find the root cause of the issue. But this can take a lot of time and effort.

Now, consider an alternative scenario.

Let’s say you encountered a problem with a code. 

Instead of using a variety of random strategies, you can analyze the mental model of a system. Think about the conditions that led to the bug and find areas where the code isn’t aligned with the mental model. 

A developer could identify the solution even without a Google search with this approach. 

Now, what are the mental models that can help you get unstuck? Here are some notable mental models for developers that can help you get the job done. 

Mental Model 1 : Rubber Ducking

Rubber ducking is a shorter term for “rubber duck debugging.” 

The concept originated from a tale wherein a programmer described their code line-by-line to a rubber duck. 

While its original inspiration seems odd, the rationale is simple.

Explaining your code to another individual or an inanimate object lets you break down the problem and determine where you got stuck. You’re compelled to think outside the box.

Eventually, you’ll arrive at the point where you went wrong with your code.

Just to clarify, you don’t need to talk to an actual rubber duck or a toy plushie to get this done. You can also gain valuable insights by rubber ducking with a colleague or a friend. As you attempt to explain your code in-depth, they might brainstorm potential solutions.

The Circle of Competence is about differentiating “what you know” from “what you don’t know.”

To put it simply, this mental model helps you remain aware of your areas of expertise. At the same time, you can accept your weaknesses or sectors where you are at a disadvantage. 

No matter how long you’ve worked as a developer, you won’t be able to know everything. 

An example would be a gaming developer moving on as a developer in the finance industry. 

You need to be proficient in C# and C++, user interface design, and program terrains or AI for non-playable characters as a game developer. Some of these skills may be useful in your current role, but you later discover you need to understand bank laws or manage security services too. 

With the Circle of Competence, developers can predict the challenges they may encounter when starting a new project or moving on to a new job. Once you know what’s outside the circle, you can seek help or contact experts that could help you conquer the areas where you’re not confident. 

A feedback loop happens when an output of a system re-enters the system as inputs. 

It usually occurs in the “ plan-do-check-act (PDCA) cycle,” an iterative process for improving products and services. 

This process involves four steps:

• Plan : Determining what needs to be done
• Do : Following the initial plan
• Check : Assessing your plan’s execution and evaluating its effectiveness 
• Act : Putting the plan into action

In software development, feedback loops can occur during the development phase. 

This process may involve aggregating feedback from a sample group of customers to determine whether the output solves what it’s intended to. Otherwise, we may waste time and money in the development phase without satisfying customer expectations. 

Developers may apply feedback loops during pair programming or code reviews.

Imagine a junior developer writing the code while a senior developer reviews it. The process improves the skills of junior developers, helps identify bugs, and improves subsequent outputs of the team. 

A mindmap is a diagram that offers a visual representation of concepts or ideas. 

Try kicking off a project by making a mindmap. Begin with a central idea or concept. It might be the main problem or the project’s title. 

Next, you can add branches or subtopics related to the central concept. These could be the main tasks that need to be done by each team. 

You can then add more subtopics or branches. These could encompass tasks assigned to each member, contributing to the overarching goal. 

A mind map is also helpful in the testing process in software development. Testers could use it to explore an application and list passed or failed tests. 

Along the way, you could even include questions in the sub-branches. This way, the feedback and issues are organized in an easy-to-understand format. 

Hill charts are a mental model that can help you identify what’s in motion and what’s stuck. 

Like the shape of a hill, the chart is composed of two phases – an uphill slope and a downward slope.

The first phase is “Figuring Things Out,” situated on the uphill slope. At this stage, you have a basic understanding of the project, but you still need to settle some unknowns or finalize your overall strategy. 

As time goes by, you’ll eventually reach a point where you’re ready to put your strategy into action. Then, the downhill phase is about “Making it Happen” or implementation. 

Developers can utilize Hill charts by coming up with to-do lists for their projects. As you fulfill or add more items on the list, identify where they should be situated on the Hill chart. 

Senior developers working on multiple projects or managing several teams can use this to gauge where a team is focusing its efforts. It could also help identify stuck groups and what they need to move forward. 

Model 6: Parkinson’s Law

Parkinson’s law is a mental model which states that work expands to fill the time allotted.

Take, for instance, a developer team that’s given three weeks to add or tweak a specific feature in the product. The team is delighted to find that they have more than enough time to finish the project. They start slow and require three weeks to complete the task, but they discover more issues to finish after receiving feedback. 

Parkinson’s law states that teams should set deadlines for maximum efficiency, even if they’re imperfect. 

In the first example, the team seems too relaxed because of the illusion of time. Questions and minor tweaks could slow them down, but the output may still be imperfect.

However, if they were allotted a realistic two-week deadline, the same team could get more done in less time. They’ll even have sufficient time to work on feedback from testing, if necessary. 

The 5 Whys is a mental model which requires asking “Why” five times. 

The rationale is when you identify a problem, the most obvious solution may not address the root cause of the issue. 

Identifying the leading cause will enable developers to save time and effort. Otherwise, they would merely apply band-aid solutions while the real problem is left unaddressed. 

An example that seems relatable to developers could be the following:

Why couldn’t the user access the calendar feature in the app? There was a bug in the recent update.

What led to the bug in the recent update? The team was unable to test all the features. Why was the team unable to test all the features? New testers on the team were unable to test all the features properly.

Why did new testers fail to perform well? They were also not provided with resources and adequate training. Why were they not provided with proper training and resources? Most new testers worked remotely.

The team in charge of training them is having a hard time because there’s no tried and tested onboarding process yet for fully-remote workers.

During the problem-solving process, we often think forward. 

This may be effective when solving simple issues. However, it may be challenging to tackle a complicated issue that needs to be broken down. 

Inversion helps us break down problems and brainstorm solutions by thinking backward.

Let’s say your software product has launched a free trial to boost your customer base. Yet, the free trial conversion rate is only a dismal 2%. 

The standard thought process for brainstorming solutions would involve asking, “What can I do to get more people to use my product even after the free trial ends?”

Instead of thinking forward, invert the problem and ask, “Which features did users try the most during the free trial? How can we improve the user experience in our free plan?”

The solution to the first problem may solely involve improving your onboarding experience and creating tutorials. Yet, you may uncover underlying issues that significantly contribute to the low conversion rate by inverting the problem. 

Model 9: Occam’s Razor

Occam’s Razor, also known as the law of parsimony, is a mental model for problem-solving. To put it simply, the model states that when there are several ways to solve a problem, the simplest solution is likely more correct and ideal. 

Consider a developer that can write both simple and complex code to accomplish the same outcome. Even if two options exist, the most ideal would be the simpler code because it is faster to review and easier to update.

While the result is the same, the more straightforward solution is easier to execute and more advantageous in the long run.

Lean Startup involves the build-measure-learn feedback loop.

Most startups start with a great idea, but it can take weeks or months to realize this product. 

Lean Startup processes solve this problem by encouraging the development of a minimum viable product (MVP) that potential customers can test.

Once selected target customers try it, the startup will measure results and ask for feedback. The cycle continues until the startup has a high-quality product that they can confidently release en masse to target consumers.

The team can build the ideal product with continuous feedback from target consumers. Otherwise, it could take weeks or months for startups to get a product beta tested.

Worse, they may discover significant issues during the testing process. However, they’ve already invested thousands of dollars into building a product and can’t afford to stay in this stage for a more extended period. 

Understanding the right mental model for each situation helps us work smarter, not harder. 

Dealing with a complicated issue can cost us a lot of time and effort. Mental models help us break down the big problem into much smaller ones. This way, we can get to the heart of the matter and develop the most practical solutions. 

I know it may take time to ingrain these mental models in your daily life. But once you learn the process and actualize it, you can instantly get unstuck and steered in the right direction.

What are mental models and why are they important for developers?

Mental models are essentially frameworks or representations of how something works. They are crucial for developers as they help in understanding complex systems and processes, making problem-solving more efficient. By using mental models, developers can simplify complex problems, predict outcomes, and make better decisions.

How can mental models help me get unstuck in coding?

Mental models can provide a fresh perspective when you’re stuck in coding. They can help you break down the problem into smaller, manageable parts, and allow you to approach the problem from different angles. This can lead to new insights and solutions that you might not have considered before.

Can you give an example of a mental model that is useful for developers?

One example of a mental model that is useful for developers is the “First Principles Thinking”. This model encourages you to break down complex problems into their fundamental parts and then rebuild them from the ground up. It’s a powerful tool for understanding problems at a deeper level and coming up with innovative solutions.

How can I develop my own mental models?

Developing your own mental models involves a lot of reading, thinking, and practicing. Start by learning about existing mental models and applying them to your work. Over time, you’ll start to see patterns and develop your own models.

Are mental models only useful for coding?

No, mental models are not only useful for coding. They can be applied to any area of life where problem-solving and decision-making are required. This includes personal life, business, and even learning new skills.

How many mental models do I need to know?

There’s no set number of mental models you need to know. The more models you understand, the better equipped you’ll be to tackle different problems. However, it’s more important to understand and apply a few models well than to know many models superficially.

Can mental models be wrong or misleading?

Yes, mental models can be wrong or misleading if they’re based on incorrect assumptions or incomplete information. That’s why it’s important to constantly review and update your models based on new information or experiences.

What’s the difference between mental models and frameworks?

While both mental models and frameworks help us understand and navigate complex systems, they are not the same. A mental model is a representation of how something works, while a framework is a set of tools or methods used to solve a problem or achieve a goal.

How can I use mental models to improve my coding skills?

Mental models can help you understand complex coding concepts, break down problems, and come up with efficient solutions. They can also help you learn new programming languages or frameworks more quickly, as you can relate new information to what you already know.

Are there any resources where I can learn more about mental models?

Yes, there are many resources available online where you can learn more about mental models. Some recommended books include “Thinking in Systems” by Donella Meadows and “Super Thinking” by Gabriel Weinberg and Lauren McCann. There are also numerous articles and blogs on the topic.

Monique Danao is a contributing writer for Sitepoint. She writes about tech, social media, content marketing, and ecommerce.

Mental Models (Part 1)

Use these mental models to improve your problem-solving and decision-making skills and overcome common [smoov.bra.in2023] reasoning errors. 

As an economist by way of education (not profession), I was introduced to Charlie Munger’s now famous speech, The Psychology of Human Misjudgment (which is quite arguably the magnum opus on why we behave the way we do) in grad school, and this introduced me to behavioral economics, which then led me to discover mental models and how to use them to dominate the competition.

Or, in more formal (less hostile) terms: how to apply the models to business, investing, and personal growth 🙂

Its been a while since grad school, but the more I read and learn, the more I’ve come to realize that these models have near infinite possibilities and can be applied in a variety of areas, from basic life choices to solving the unanswered complex questions of the universe.

But before we get into the meat, what exactly is a mental model?

A mental model is just a fancy word for a principle that you can use to try to explain things and/or make better decisions.

It’s a simplified “cognitive framework” that can help you understand and interact with the world by providing a structured way to organize information and make good choices.

There are tens of thousands of mental models out there (every discipline has their own set), but you don’t need to know all of them.

You just need to know the basics.

As Munger says, “80 or 90 important models will carry about 90% of the freight in making you a worldly‑wise person.”

This post will discuss a few of the mental models that have been repeatedly useful to me over the years.

I use quite a few of them every day at the office (from spaceflight probability to hiring decisions to general strategy) and others outside the office (e.g. investing, personal life, and, of course fantasy football).

Note: as you read through this post you will come across various links to books that I personally recommend reading/studying to increase you knowledge of the topic currently in discussion.

Disclaimer: this is an incomplete list and biased from my own experience and knowledge, this is not financial or life advice, use at your own risk 🙂

Ok, let’s dive into it.

Mental Models to Help You Think Like A Genius:

1. Probabilistic Thinking

The modern world is facing a severe crisis of critical thinking and cognitive reasoning.

We exist in a culture of irrationality, fear, doubt, and uncertainty.

Nobody knows how to think anymore.

People are lost.

The confusion leads to anxiety and stress.

And stress leads to bad decisions.

Bad decisions are everywhere.

To make things worse, there are scammers, fraudsters, and hucksters lurking in the shadows.

Lurking and waiting to prey on the insecurity and fear that arises from a world full of uncertainty and unknowns.

Many times they come across as nice, helpful, and innocent.

They’ll offer you interesting remedies to “fix” your anxiety and stress.

They offer the answers to all of your problems and confusion.

Often by promoting courses or “mantras” or selling supplements, potions and elixirs.

Or by telling you to simply “think positive” and that everything will be OK.

“Have faith, little one.”

You can sell anything to fools if you market it properly.

They are wolves in sheep’s clothing.

And now “uncertainty” has become a dirty word.

We’re only allowed to think “positive”.

Everything else is “negative”.

So we default to the Binary.

What is the Binary, you ask?

Binary thinking is the tendency to see things in terms of absolutes (good versus bad or right versus wrong).

The Binary is simplified mindset where a person sees issues in terms of two options that are often mutually exclusive.

In the Binary, everything is either black or white, left or right, yes or no, on or off, hot or cold.

And using Binary Thinking to make sense of the world and solve hard problems often leads to bad outcomes.

Especially in a stressful world full of irrationality, anxiety, fear, doubt, and uncertainty.

Will you get struck by lightning today?

Hit by a car?

Robbed by bandits?

Win the Powerball lottery?

Maybe you will, maybe you won’t.

We’ll never totally know the outcome until the day is over, which is not particularly useful when we have to make our decisions in the morning.

Sure, Binary Thinking works in certain situations (sometimes), but for the most part, especially in the cold reality of our dynamic, complex, uncertain world, Binary Thinking is often subpar.

Using the Binary is a common [smoov.bra.in2023] runtime error. This happens when you use your brain on default settings.

Using default settings makes it easier to fall into the trap of optimism bias (more on this later), risk miscalculation, and self-destruction.

You see, in the real world, the future is inherently unpredictable because not all variables can be known — and even the tiniest error imaginable in the data can quickly throw off your predictions.

And because the future is not 100% deterministic, we need to figure out a way to navigate uncertainty and complexity so we can have a better understanding of the events that could impact us in a positive or negative way (and their likelihood).

This is where Probabilistic Thinking comes in.

In the office , we use probability theory just about every day to solve hard problems.

We’re a team of nerds who are deep in the mud with predictive modeling/analysis every day (building predictive algorithms, tinkering with AI and machine learning and the likes).

And from being knee deep in it on a day-to-day basis, I know firsthand just how difficult it is to predict the future .

Even if you’re a data science genius, predicting the future is incredibly difficult.

Most of the time the best you can do is just create “estimates” by coming up with simple, realistic, useful probabilities.

“So, how do I do that?”

And what is probabilistic thinking?

Probabilistic Thinking is when you try to estimate (using logic and math ) the likelihood of any specific outcome.

This can help improve decision making accuracy.

If you live on Earth (Solar System 1) and are not some alien reading this from some far away planet, you should be aware that every moment in our world is determined by an infinitely complex set of factors.

This complexity can lead to uncertainty, anxiety, and fear.

Feelings that most humans experience every day (to a certain degree).

Probabilistic thinking, on the other hand, can reduce uncertainty, anxiety, and fear, and help you solve some very hard problems — because probabilistic thinking can give you the ability to see through the dense fog of madness and uncertainty and show you the most likely outcomes.

When you know the outcomes (holy sh*t can you predict the future now?) not only will your decisions be more accurate and effective, but you’ll also be able to avoid pitfalls like recency bias and emotional decision-making suboptimality.

You see, the right answer is like two people. Quant is the nice one. Logic causes all the trouble. They fight.” —Jamin Thompson

Probabilistic Thinking is a much more scientific and quantitative approach (as opposed to Binary Thinking) when it comes to making difficult decisions in uncertain or unpredictable situations because it considers the odds, probabilities, and likelihoods of a multitude of various outcomes.

It acknowledges that many real-world situations are characterized by inherent variability and unknowns — and it seeks to quantify and manage this uncertainty through the use of probability theory.

Are you lost in the woods?

Decision forest too thick?

How do you get out of here?

Do you take this path or that one?

Each choice could lead to a different outcome.

How do you figure out the probability of each one?

Are there complex formulas?

What does probability even mean anyway?

That’s a great question. I’m glad you asked that question.

Probability means POSSIBILITY.

Probability is a measure of the likelihood that an event will occur in a random experiment.

The value is expressed as a number between 0 and 1, where 0 means the event is an impossible one and 1 indicates a certain event.

You see, many events cannot be predicted with absolute certainty. We can only predict the chance an event has to occur (or how likely they are going to happen).

The higher the probability of an event, the more likely it is that the event will occur.

Take a coin toss, for example. When you toss a coin, you will either get heads or tails. These are the only two possible outcomes (H, T). But, if you toss two coins, then there will be four possible outcomes [(H, H), (H, T), (T, H), (T, T)].

Note: this is basic probability theory (also used in the probability distribution) where you learn the possibility of outcomes for a random experiment. To find the probability of a single event, first we need to know the total number of possible outcomes. 

That said, when it comes to Probabilistic Thinking, there are two critical factors to consider: 

• The probability of a certain outcome.
• The magnitude of a certain outcome.

But, how do I calculate the probability of an outcome?

Without turning this into a very boring math essay full of complex formulas that make no sense, here is all you really need to know.

Probabilistic thinking pretty much boils down to the following three critical aspects:

• Bayesian thinking
• Fat-tailed curves
• Asymmetries

Let us begin with Bayesian Thinking.

(aka The Bayesian view of probability or Bayes’ Theorem)

Bayes’ Theorem is a fundamental concept in probability theory and statistics.

Named after 18th-century statistician, philosopher and minister Thomas Bayes, Bayes’ theorem describes the probability of an event, based on prior knowledge or conditions that may be related to the event.

It works by giving you a way to update and improve your probability estimates when new evidence becomes available.

In simple terms: Bayes Theorem measures the plausibility of an event when you have incomplete information.

It starts with a statement of knowledge prior (usually this comes in the form of a prediction).

*yes, this can really come in handy if you are trying to win your fantasy football league*

For example: to improve the state of knowledge, an experiment is designed and executed to “get new evidence”.

Both the prior and the experiment’s results have a joint distribution (the probability of two events happening together) that leads to a new and improved belief.

This is demonstrated in the following equation:

P(A|B) = [P(B|A) P(A)] / P(B)

Where P = probability, A = prior knowledge, and B = new evidence.

And where P(A) = probability the prior belief is true.

P(B) = probability that the new evidence is true.

P(A|B) = probability of the prior belief if the new evidence is true.

P(B|A) = probability of the evidence if the prior belief is true.

It’s a deceptively simple calculation, although it can be used to easily calculate the conditional probability of events where intuition often fails.

Unrelated, intuition usually fails.

Side note for the AI enthusiasts: although Bayes Theorem is widely used in the field of probability, it is also heavily used in machine learning as well.

The core concept of Bayes focuses on the fact that as we encounter new information, we should probably take into account what we already know when we learn something new.

This helps us use all relevant prior information as we make decisions.

It’s particularly useful when dealing with conditional probability, where the likelihood of an event is dependent on the occurrence of another event.

Poker players and entrepreneurs both embrace the probabilistic nature of decisions. When you make a decision, you’ve defined the set of possible outcomes, but you can’t guarantee that you’ll get a particular outcome. — Annie Duke (Poker Champion)

As mentioned previously, we live in a world that is full of uncertainty.

And uncertainty leads to stress.

Stress leads to bad results.

These are critical points to understand if you truly want to make better decisions.

A huge part of Probabilistic Thinking involves befriending uncertainty, which can be incredibly hard.

To overcome this, we need to acknowledge that uncertainty exists, that it is everywhere, and make it our friend.

To do this you must learn to live in “chaos” and embrace the unknown.

Let go of your ego and your biases and accept the conditions of the situation.

Let your mind be free.

Be ok with saying “I’m not sure”.

Accept that you will never (even if you are a data science genius or fantasy football expert) know all the facts in any given situation and that there are no guarantees of a specific outcome.

Next, after you figure out what you think may happen, ask yourself, “what else might happen?” and then decouple from notions of ‘good’ and ‘bad’ in decisions from those outcomes.

It’s also important to keep in mind that when you are swimming in the deep waters of uncertainty and complexity, there is also (very likely) a degree of luck involved, so it’s possible for you to make a ‘bad’ decision that leads to a positive outcome.

And that’s OK.

It’s MUCH easier to accept randomness when things don’t go our way.

So, with that in mind, instead of focusing on outcomes/results, learn to trust the process and try to reflect on your previous decisions from a probabilistic perspective.

Got all that?

Now, the next thing to remember is to never (for any reason) express 100% certainty.

This is the ultimate rookie move, and if you have made it this far, you definitely aren’t a rookie anymore.

So, start doing this today: if you want to make a prediction (fantasy football, the weather, stock market, economy, world war 3, whatever), get in the habit of assigning levels of certainty to your predictions, rather than boldly claiming something will just ‘simply happen’.

Always do your due diligence and estimate the percentage chance it will happen based on your available data/facts.

Note: it’s important that when you’re confronted with the possibility that you’re wrong that you don’t engage in any sort of cognitive gymnastics to try to hold onto that false belief (even though you want to). To become a true mental master, you’ll have to come to terms with the fact that there are countless things you’re not right about in our present moment in spacetime. 

If you’ve got all that, the next step is to update your probabilities.

You have been presented with new information. What should you do?

You must be open to it and consider any emerging facts.

Then, use the data to update your predictions.

Note: this process can be painful and difficult and involves challenging and disrupting your biases. 

And then, FINALLY, after you have made your predictions and updated them, THEN you must find the confidence to act (based on your current knowledge) to run the numbers and understand all the probable outcomes, while also accepting the fact that you could always be wrong.

Artist depiction of a Deimos-One Data Scientist Modeling Simplicity in a Stochastic Environment

And that’s (mostly) it in a nutshell.

Probabilistic Thinking (or Bayes’ theorem) is a powerful tool for making informed decisions and updating beliefs in the face of uncertainty and changing evidence.

It’s a pragmatic approach to decision-making that recognizes the limitations of certainty and embraces the tools of probability to navigate uncertainty.

Using it will help you avoid common [smoov.bra.in2023] reasoning errors and empower you to make more informed decisions.

As an added bonus, you’ll also get better at managing risk and developing strategies that can hedge against uncertainty and complexity.

If you master this style of analysis, you’ll have a new weapon in your war chest that will help you understand not only the world, but also yourself (decisions, thoughts, emotions, etc.).

That is true power.

Next up, fat-tailed curves.

So, what exactly is a “fat-tail”?

The statistical term ‘fat tails’ refers to probability distributions that have a relatively high probability of extreme outcomes.

Just think: your last relationship.

Or, that memecoin you put your whole savings in.

A fat-tailed distribution exhibits a large skewness, which consists of a thick end or “tail” toward the edges of the distribution curve.

Fat-tails also imply strong influence of extreme observations on expected future risk.

Fat-tails are similar (but slightly different) to its more traditional “do-gooder” older brother, the normal distribution curve.

The normal distribution curve is shaped like a bell (it’s also known as the bell curve), and it typically involves two basic terms: (1) the mean (the average) and; (2) the standard deviation (the amount of dispersion or variation).

Usually the values here cluster in the mean and the rest symmetrically taper off towards either extreme.

Kind of like ordering “hot wings” with the mildest sauce there is.

The fat-tail, on the other hand, is sort of like wild and crazy hot wings with Carolina reaper peppers and homemade hot sauce from the back of someone’s barn.

You know that sh*t is gonna be SPICY.

But hey, let’s compare wings, shall we?

At first glance both types of wings (mild/extreme hot) seem similar enough (common outcomes cluster together bla bla) but when you look closely (can you identify a Carolina reaper without Googling?) they always have very distinctive traits to tell them apart.

If you want to use comic books as a reference instead of hot wings, take Loki and Thor, for example. They are “brothers” but their difference is usually in the physical difference (Thor is the jacked one).

With these distribution curves, it is also appearance based — the difference is in the tails.

Here are some important distinctions: 

In a bell curve the extremes are predictable. There can only be so much deviation from the mean.

Fat tails have positive excess leptokurtosis (fatter tails and a higher peak at the mean), which means there is no real cap on extreme events (positive or negative).

In a normal distribution (bell curve), on the other hand, the extremes are more predictable.

The more extreme events that are possible, the longer the tails of the curve get.

Does that make sense?

You’re doing so great.

Sure, you could make the argument that any one extreme event is still unlikely, but due to the massive number of options, you probably won’t be able to confidently rely on the most common outcomes as a representation of the average.

And the more extreme events that are possible (think millions or even billions) the higher the probability that one of them will occur.

When it comes to fat-tails, you know crazy things are going to happen (with near certainty), like your fantasy football team having multiple starters sent to IR on the same day, you just have no idea of knowing when.

So, how can a common pleb like myself use this knowledge to my advantage?

That’s a great question, I’m glad you asked that question.

Suppose you hear on the news that you had a greater risk of falling out of bed and cracking your head open than being killed by war.

The stats, the priors (seem) to back it up: over 30,000 people died from falling injuries last year in your country and only 200 or so died from war.

Should you be more worried about falling out of bed or World War 3?

A lot of hucksters and actors who play economists on TV use examples like these to “prove” that the risk of war (World War 3 in this case) is low.

They say things like “there are very few deaths from this in the past and the numbers back this up so why even worry?”

Looking at it on the surface, it may appear (to the untrained eye) that the risk of war is low since death data shows recent deaths to be low, and that you have a greater risk of dying from falling, it all makes sense, right?

The problem is in the fat tails.

The shape of the curves often tell a very different story.

Think of it like this: the risk of war is more like your bank account, while falling deaths are more like weight and height. In the next ten or twenty years, how many outcomes/events are possible?

How fat is the tail?

Think about it.

If the risk of World War 3 is high, it would follow a more fat-tailed curve where a greater chance of extreme negative events exists.

On the other hand, dying from falling (like the time I fell out of the top bunk of my bunkbed as a kid) should follow more of a bell curve, where any outliers should have a well-defined scope.

It may take a bit of practice, thinking, and application, but trust me, it’s simple once you get it.

If you don’t understand it now, keep studying and you will.

Remember, the important thing to do when you are trying to solve a hard problem is not just sit around and try to imagine every possible scenario/outcome in the tail. This is an impossible task for any human running [smoov.bra.in2023] to figure out.

Instead, use the power of the fat-tail to your advantage.

Doing this will not only put you in a great position to survive and thrive in the wildly unpredictable future — but it will also help you think clearly and make good decisions — so you can always stay one step ahead in a world we don’t fully understand.

Finally, that leaves Asymmetries.

Now that you have basic probability under your belt, it’s time to embrace a more advanced concept that many experts call “metaprobability” — aka the probability that your probability estimates are any good.

Let’s be honest, your probability estimates probably suck (at least right now because you’re just starting out) but for the purposes of this exercise let’s pretend that they are brilliant.

For argument’s sake, let us consider the possibility.

You are a common plebeian genius who has access to brain tools and weapons only possessed by super advanced civilizations.

The internet, advanced machine learning, AI, GPT-4, Amazon Books , and many other tools from the wonderful world of advanced data science wizardry are all at your fingertips.

Look at us.

Who would have thought?

But with all these advanced tools is it now possible to accurately predict the future?

Let us see.

First, we evaluate the known params:

-I exist -The world exists -The world is chaos -Chaos leads to war -War is unpredictable -Unpredictability leads to uncertainty -Uncertainty leads to stress -Stress kills

bla bla bla

Now, let us consider a function of a complex variable f(z) = wut + tf where we assign falling out of bed, world war 3, nuclear attack, terrorism, disease, famine, global warming, AI, a specific weight to form a probabilistic argument to estimate the chance that a violent kinetic event will occur, and/or the event’s place in spacetime.

Let us import the vars into our shitty bathroom formula:

tf² [(x²) + (y²) + y(z²)] + ded P = —————————————— Σi (7x – war + wut²)

Solving for wut + tf we can conclude with a 69.699% probability that shit happens and that shit will continue to happen until shit happening ceases to occur.

We have discussed economic hucksters and Decepticon intellectuals in previous posts.

Bold predictions.

Fancy charts.

Long exposition.

Pseudo quant.

Devoid of logic.

Immovably committed to bold positions because it makes you sound smarter than being humbly realistic.

They make bold predictions but are wrong more often than they are right.

Bla bla bla.

The reason these guys are wrong so often has to do with asymmetries.

For example, if you observe a common huckster in action: the expensive suit, the nicely polished pitch deck, the fancy charts, the slick haircuts, etcetera…

These guys will look you right in the eye in a pitch meeting and tell you you can expect to “achieve a rate of return of 30% or more per year” or even more.

And most of them never hit their projections.

Not because they don’t have ANY of their projections correct, it’s because they got so many incorrect.

They overestimated their confidence in their probabilistic estimates — and they do this consistently, meeting after meeting, year after year.

A common [smoov.bra.in2023] runtime error.

Note: we all know the stock market usually only returns about 7% a year in the United States, but for some reason (unexplained by modern science) we continue to listen to and bet on the smooth talking 30% guy. 

With that in mind, here is what you need to do.

Write this down:   my probability estimates and predictions are more likely to be wrong when I am “over-optimistic” instead of “under-optimistic”.

And a lot more probability estimates are wrong on the “over-optimistic” side than the “under-optimistic” side.

Remember this fact.

Put it on the fridge so you never forget it.

Why is this so important, you ask?

The reason for is simple: a lot of uncertain outcomes are inherently asymmetric.

They have longer downside tails than upside.

And a common [smoov.bra.in2023] runtime error is to lock in and focus on the “obvious” or “most likely” outcome — and then forget to crunch the numbers to figure out the real expect impact of multiple asymmetries together.

Image: Sketchplanations.com

Things that never happen (or rarely happen): an investor who wanted to hit a 30% annual return but instead hit 45% over multiple years.

When you do the analysis (you can go crunch the numbers yourself) you’ll find that most guys end up closer to 9 or 10 percent.

Maybe 12 percent if they’re lucky.

Remember, your estimation errors are asymmetric, skewing in a single direction — this is often the case with probabilistic decision-making .

Whew, that was a lot.

You got all that?

To sum things up, just remember: when you get too over-confident and fall into the trap of blind “over-optimism” it often leads to errors and bad outcomes.

But, if you can build your data analysis skills up to a ’99 Madden awareness level’ you’ll be able to recognize overestimations in your probabilistic estimates so you can plan more carefully during high-stake situations and make rational (winning) decisions when you are faced with high levels of uncertainty, ambiguity, and incomplete knowledge.

You will be a god among men.

2. Second-Order Thinking 

These days, it can be tempting to make emotional decisions with small upside that provide instant gratification.

This is often referred to as first-level thinking, which is simplistic and superficial by nature.

First-level thinking does not consider the negative future consequences of a decision made today.

First-level thinking focuses on solving an immediate problem, with little or no consideration of the potential consequences.

But most decisions require a much deeper level of thought and mental exploration. They require you to look beyond the immediate and the obvious; to dig deeper.

For example:

An investor with first-level thinking may think that a crypto company with a rapidly growing online following will lead to an inevitable (somehow correlated) rise in share price.

On a similar note, a person on a diet may conclude that the best choice for a hungry stomach is a delicious bacon cheeseburger (I’ve done this).

In both cases, the potentially negative future consequences of each choice have not been fully thought out and evaluated.

It happens all the time: some decisions seem like dubs at first, but then turn out to be huge L’s as time goes on.

What seems like a good memecoin to buy today turns out to be a huge dud months later.

What looked like a good decision before is now a bad one.

Most people stop at first level thinking, but you don’t have to.

Instead, give second-order thinking a shot and see if your decision making can improve.

So, what exactly is Second Order Thinking?

In his exceptional book, The Most Important Thing , Howard Marks explains the concept of second-order thinking, which he calls second-level thinking:

“First-level thinking is simplistic and superficial, and just about everyone can do it (a bad sign for anything involving an attempt at superiority). All the first-level thinker needs is an opinion about the future, as in “The outlook for the company is favorable, meaning the stock will go up.”

First-level thinking is very linear.

It’s fast and easy.

Simplistic and superficial.

Just about anyone can do it.

It is based on default settings.

It is the result of looking for something that solves the problem at hand without considering the tradeoffs or consequences.

For example, you’re thirsty so let’s drink an ice cold beer .

First-level thinking is very basic, and just about everyone can do it (a bad sign for anything involving an attempt at superiority).

Second-order thinking is a lot deeper, more complex and convoluted.

More deliberate.

It requires a much deeper engagement of the mind.

Second-order thinkers analyze the statistics, facts, and information, but also question the reasoning behind them.

Second-order thinkers have a strong understanding of bias.

Second-order thinkers have strong metacognition.

Second-order thinkers think about their own thinking — and then think about thinking about the thinking they are thinking — often analyzing multiple variable at the same time.

Second-order thinking is a mental model that can help you properly assess the implications of your decisions by considering future possibilities/consequences. It encourages you to think outside the box and prepare for every eventuality.

So, how do I start thinking in the second order?

A good place to start would be asking yourself the following:

• What is the probability that I’m right?
• What is the range of likely future outcomes?
• Which outcome do I think will occur?
• What does the consensus think?
• How does my expectation differ from the consensus?
• And then what?
• What do the consequences look like in 10 minutes? 10 months? 10 Years?

Key Point: While first-level thinking evaluates the direct outcomes of a single decision, second-order thinking delves into the broader and deeper implications that can arise as a result of those outcomes.

Unlike first-level thinking, where everyone reaches the same conclusions, second-order thinking gives you the ability to navigate complex situations with greater perspective and insight.

It’s a model that that considers all future possibilities.

It can prepare you for any eventuality.

It goes beyond simple cause-and-effect reasoning.

It considers the entire chain of reactions and unintended consequences and shields you from the human default tendency to make the most obvious choice.

When you look beyond the immediate and obvious, you’ll be able to make better decisions that will lead to more positive outcomes, both now and in the future.

Looking back at some of your past decisions in a new light (from the lens of having just read through this) first-level thinking may sound really stupid, but our minds are programmed (aka [smoov.bra.in2023] default settings) to search for the easiest solution and/or the solution that usually appears obvious.

For this reason, most people really struggle to look beyond their initial assumptions to make the optimal decision.

This is especially true when we are stressed, in a time crunch, inexperienced in the field at hand, over-optimistic (see mental model #1 again), experiencing strong emotions, have psychological biases, or are isolated from other points of view.

At the end of the day, second-order thinking can help you step outside your comfort zone and objectively analyze and assess the implications of your decisions by considering the future consequences.

It will help you make sure you’re certain and comfortable with the long-term consequences of your decisions today.

This mental model is particularly useful in times of uncertainty and/or in rapidly changing environments, where the ability to foresee multiple potential outcomes can help you avoid disaster and lead to more successful outcomes.

Bonus Chance: second-order thinkers typically outperform first-level thinkers because they can see solutions to problems that others can’t.

3. Opportunity Cost

There is no such thing as absolute victory, there is no perfect solution, there are only trade-offs. All you can do is try to get the best trade-off you can get.

We live in a world of trade-offs.

There is no such thing as a perfect outcome.

And the concept of opportunity cost (e.g. there is no such thing as a free lunch) is the king of all trade-offs.

This is a fundamental concept that’s taught in Basic Economics .

So, what exactly is an opportunity cost?

In economics, opportunity cost refers to the loss of potential gain a person could have received but passed up in pursuit of another option.

Opportunity cost is a fundamental concept in economics and decision-making.

When making a choice between two or more alternatives, the cost of that decision is not just the immediate financial expense but also the potential value or benefits you lose by not choosing the next best alternative.

In other words, opportunity cost is the value of the next best option that you give up when you make a decision.

It helps you evaluate the trade-offs involved in your choices.

For the nerds, you can express opportunity cost conceptually as follows:

Opportunity Cost = FO – CO where: FO = Return on best forgone option CO = Return on chosen option

Let us consider an example of opportunity cost in the context of selling a stock now or waiting to sell it in three months.

Suppose you have 1,000 shares of Deimos-One stock, and the current market price per share is $100.

You have two options:

Option 1: Sell the stock today at $100 per share. Option 2: Wait and sell it in three months hoping that the stock price will go up.

To calculate the opportunity cost, we will need to compare the returns of both options.

Option 1: 1,000 shares x $100 per share = $100,000 (current value)

Option 2: 1,000 shares x $120 per share = $120,000 (future value) *let’s assume the stock price has increased*

Now, calculate the opportunity cost:

Opportunity Cost = Future Value (Option 2) – Current Value (Option 1) Opportunity Cost = $120,000 – $100,000 = $20,000

So, if you choose Option 1 and sell the stock today, your opportunity cost is $20,000.

This means that by selling now, you’re giving up the potential $20,000 in profit you could have earned if you had waited for three months and sold the stock at the higher price.

This opportunity cost scenario shows the trade-off between realizing immediate gains and waiting for potentially higher returns.

Opportunity cost is a powerful mental model to help you make more informed decisions as it encourages you to consider not only the benefits of your chosen option but also what you’re sacrificing by not choosing an alternative.

It’s always a wise move to think about the potential costs that arise because you chose in favor of one option and thus against every other option.

4. Randomness 

There aren’t always cause-effect relationships.

A lot of stuff is just random.

For some reason (unexplained by modern science), the human brain has a lot of trouble comprehending this fact (another common runtime error?) but the truth is: a lot of the world is made up of random, non-sequential, non-ordered events.

Humans are often “fooled” by randomness.

A function of [smoov.bra.in2023]  perchance?

A common plebian reasoning error?

This needs to be studied further.

We attribute causality to things we have zero control over.

And if you get fooled by randomness and get tricked into a false sense of pattern seeking — you will get finessed into seeing things as being more predictable than they actually are, and eventually make a critical error.

It happens all the time.

So, what exactly is “randomness”?

In previous blogs , we have discussed how hucksters and posers do not understand probabilistic processes, or how to model simplicity in stochastic environments.

Randomness is a mental model/cognitive framework that recognizes the inherent uncertainty and unpredictability present in various aspects of life and decision-making.

Here’s the thing: we live in an unpredictable world.

It’s a world with a dense decision forest.

There are many steps/variables/tricks/outcomes.

It can be thick and hard to see.

There is complexity.

Stochasticity.

Uncertainty.

But, the winners are often able to see 2-3 steps ahead.

They can see through the trees to infinity.

The losers can barely see what’s in front of them.

You see, many outcomes are not entirely deterministic — a lot of it is subject to chance and probability.

The winners just know how to calculate it.

Here are a few key principles to understand when it comes to randomness:

Probabilistic Thinking: Embracing randomness and uncertainty (see model #1 above) means thinking in terms of probabilities. Instead of expecting deterministic outcomes, we must consider the likelihood a situation can have multiple possible outcomes, each with its own chance of occurring. Understanding these probabilities is critical for understanding the distribution of possible outcomes and making informed decisions based on that information.

Statistical Reasoning: Randomness often follows statistical patterns. Finding these patterns can help successfully navigate through a dense decision forest. By applying statistical methods to analyze data, considering patterns and distributions rather than relying on intuition or a “gut feeling” can help you make informed judgments even when faced with uncertainty.

Stochastic Processes: One must recognize that many processes are driven by stochastic, or random, elements, and it helps to study, understand, and be able to model complex systems.

Irrationality Awareness: Randomness highlights human biases and irrational tendencies when dealing with uncertainty. In order to navigate through the decision forest, we must be aware of these biases and encourage ourselves to make rational decisions.

Uncertainty Awareness: Nobody knows it all. Many events are inherently uncertain and cannot be predicted with certainty. Recognizing this can help avoid overconfidence, not only in your predictions but also in your decisions.

Decision Under Uncertainty: As mentioned above, not only must we have uncertainty awareness, but we must be able to make decisions when outcomes are uncertain. It helps to make decisions based on expected value or risk-reward analysis.

Risk Assessment / Prediction Limits: Every decision you make can have a range of outcomes, and randomness often adds risk and uncertainty into decision-making processes. So, one must calculate risk probability. But not all events can be accurately predicted, some degree of uncertainty will always be present. Recognizing the limits of prediction is key.

We use Monte Carlo (MC) Simulation (a basic mathematical technique that predicts possible outcomes of uncertain events) in the office quite often to simplify randomness and predict future outcomes.

MC can be a very useful tool to identify all the possible outcomes of an event (e.g. spacecraft launch, payload drops, landing, etc.) making it a lot easier to measure risk so we can make good decisions under uncertain initial conditions.

We can run MC simulations millions of times (it generates a bunch of “what-if” test scenarios) by treating every input variable and its intrinsic uncertainty as a probability distribution function.

This allows us to get precise, quantitative risk analyses of incredibly complex projects (such as a payload drop over a distant planet) and mission critical risk assurance for the spacecraft.

What we usually end up with is a comprehensive and quantifiable statistical picture of what could happen, its likelihood of happening, and any errors associated with such an occurrence.

It is a great tool to hedge your bets against the cruel pimp slap of an unforeseen random event.

That said, MC also kicks ass due to the fact that unlike normal forecasting models (or traditional single-point risk estimates) MC is able to build a model of possible results by leveraging a probability distribution (like a uniform or normal distribution) for any variable that has inherent uncertainty.

Then, it recalculates the results over and over (thousands and thousands of times) each time using a different set of random numbers between the minimum and maximum values, generating a large number of likely outcomes.

And thanks to the internet and tools like YouTube and GPT4, you don’t have to be a master data scientist to set up and run basic MC simulations.

This is a very simple method/experiment that you can learn quickly and use to solve any problem that includes an element of uncertainty or randomness in its prediction.

Or, problems that may have a probabilistic interpretation or be deterministic in principle.

At the end of the day, we live in a confusing world with tons of uncertainty, and understanding randomness is essential if you want to manage risk and navigate uncertainty.

The basic framework is built on rational decision-making, statistical thinking, probability and risk analysis that you can use to make better decisions in all aspects of life, from everyday choices to complex financial and life decisions.

Put it in your war chest and use it to your advantage.

5. Occam’s Razor 

“Never multiply unnecessarily.”

Occam’s razor (also known as the “law of parsimony”) is a foundational mental model in reasoning and problem-solving. It underscores the principle of simplicity as a guiding criterion for choosing among competing hypotheses or explanations.

It states that one should not increase (beyond reason) the number of entities that are necessary to explain anything. All things being equal, the simplest solution is often the best one. The principle is attributed to 14th-century philosopher/theologian William of Ockham .

It can be summarized as follows:

Among competing hypotheses, the one with the fewest assumptions should be selected.

Simple explanations are preferrable to complex ones.

Simple theories are easier to verify.

Simple solutions are easier to execute.

Always choose the less complex explanation/option.

Occam’s razor mirrors the colloquial phrase “KISS” (Keep It Simple Stupid).

The general idea behind “KISS” is that if you have two or more explanations that work equally well (ceteris paribus) in answering a question, the simplest hypothesis is generally the most optimal.

As discussed in previous articles , we live in an era where we have more knowledge and technical capabilities to ask and answer more questions than ever before.

The more questions we ask, the more answers we get.

And, the more answers we get, the more questions arise.

Every day we are asking and answering more questions, in more fields, and arriving at even more questions.

And we’re doing it at unprecedented speeds.

We are, at an alarming rate, asking and answering more and more questions , which in turn, allows us to make more decisions and have more agency.

The problem is, you and I (and just about every human on this planet) is still running [smoov.bra.in2023] which severely limits our cognitive abilities and decision making skills.

Our core programming does not have the capacity to process information at this volume and scale.

Note: if this does not make sense to you, it’s likely a runtime error… [smoov.bra.in2023] needs more CPU and RAM to process fully but the sim params have prevented this.

Sure, we have a lot more options now (and that’s a good thing).

But, with more options come more choices.

And with more choices come more decisions to make.

And with more decisions to make, the more complex the solutions become.

So, paradoxically, in an attempt to gain more knowledge and understanding by building the technologies we thought would help make our lives easier, we seem to be doing the opposite of Occam’s Razor.

The question of utility seems to be getting farther and farther away.

We live in a world where we have unprecedented choice freedom and agency.

But with all of these options (multiplied unnecessarily) are we causing more work for our brains?

Are we wasting hours of our lives pondering over trivial decisions?

Just think about the amount of time it takes to find a TV show to watch on Netflix, or how long it takes to figure out what food you want to order on Uber Eats.

As a self-described deep thinker myself, I’m not going to sit here and chastise you for thinking but sometimes thinking can be suboptimal.

Alan Watts may have said it best when he said: “Don’t think too much.”

A person who thinks all the time has nothing to think about except thoughts. So, he loses touch with reality, and lives in a world of illusions.

By thoughts, I mean specifically, chatter in the skull. Perpetual and compulsive repetition of words, of reckoning and calculating.

I’m not saying that thinking is bad. Like everything else, it’s useful in moderation.

A good servant but a bad monster.

And all so-called civilized peoples have increasingly become crazy and self-destructive because, through excessive thinking, they have lost touch with reality.

We are wasting our brain power on trivial matters and useless activities.

This is causing decreased cognitive ability and mental exhaustion.

Occam’s Razor, on the other hand, encourages clarity, parsimony, and simplicity in reasoning.

It encourages you to choose straightforward, elegant explanations that minimize unnecessary complexities and assumptions.

It promotes rationality and economy in the formation of hypothesis.

It aids in prediction and empirical testing and contributes to the efficiency and progress of scientific inquiry.

This mental model’s power lies in its capacity to foster clarity of thought, drive scientific progress, and help you make informed and rational choices when faced with uncertainty and complexity.

Use it to your advantage.

Final Thoughts

Mental models (especially in the way presented here) can be a mouthful to take in at first, but if you spend some time with them, studying them, and using them in application (not just theory) you’ll eventually get the hang of them.

It’s all about the application and the context, you have to use them in the right way at the right time. It’s a science; you’ll have to understand them well and practice using them for them to have full effect.

Personally, I find mental models to be particularly useful when I’m trying to come up with cool ideas or trying to solve hard problems.

That said, I’ve rambled on enough for today.

I hope you gained some valuable insight from the words written here.

It’s my hope that you can leave the internet somehow smarter than you were when you first logged on.

So, what Mental Model has helped you the most?

Let me know on X/Twitter.

Follow me for more shitty analysis: twitter.com/jaminthompson.

Click Here to read Mental Models Part 2.

Best Mental Model Books for further study: 

Poor Charlie’s Almanac by Charles Munger

The Most Important Thing by Howard Marks

The Personal MBA by Josh Kaufmann

The Fifth Discipline by Peter Senge

Thinking in Bets by Annie Duke

Against the Gods by Peter Bernstein

Basic Economics by Thomas Sowell

Math for Machine Learning by Richard Han

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Overview of the Problem-Solving Mental Process

Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Rachel Goldman, PhD FTOS, is a licensed psychologist, clinical assistant professor, speaker, wellness expert specializing in eating behaviors, stress management, and health behavior change.

• Identify the Problem
• Define the Problem
• Form a Strategy
• Organize Information
• Allocate Resources
• Monitor Progress
• Evaluate the Results

Frequently Asked Questions

Problem-solving is a mental process that involves discovering, analyzing, and solving problems. The ultimate goal of problem-solving is to overcome obstacles and find a solution that best resolves the issue.

The best strategy for solving a problem depends largely on the unique situation. In some cases, people are better off learning everything they can about the issue and then using factual knowledge to come up with a solution. In other instances, creativity and insight are the best options.

It is not necessary to follow problem-solving steps sequentially, It is common to skip steps or even go back through steps multiple times until the desired solution is reached.

In order to correctly solve a problem, it is often important to follow a series of steps. Researchers sometimes refer to this as the problem-solving cycle. While this cycle is portrayed sequentially, people rarely follow a rigid series of steps to find a solution.

The following steps include developing strategies and organizing knowledge.

1. Identifying the Problem

While it may seem like an obvious step, identifying the problem is not always as simple as it sounds. In some cases, people might mistakenly identify the wrong source of a problem, which will make attempts to solve it inefficient or even useless.

Some strategies that you might use to figure out the source of a problem include :

• Asking questions about the problem
• Breaking the problem down into smaller pieces
• Looking at the problem from different perspectives
• Conducting research to figure out what relationships exist between different variables

2. Defining the Problem

After the problem has been identified, it is important to fully define the problem so that it can be solved. You can define a problem by operationally defining each aspect of the problem and setting goals for what aspects of the problem you will address

At this point, you should focus on figuring out which aspects of the problems are facts and which are opinions. State the problem clearly and identify the scope of the solution.

3. Forming a Strategy

After the problem has been identified, it is time to start brainstorming potential solutions. This step usually involves generating as many ideas as possible without judging their quality. Once several possibilities have been generated, they can be evaluated and narrowed down.

The next step is to develop a strategy to solve the problem. The approach used will vary depending upon the situation and the individual's unique preferences. Common problem-solving strategies include heuristics and algorithms.

• Heuristics are mental shortcuts that are often based on solutions that have worked in the past. They can work well if the problem is similar to something you have encountered before and are often the best choice if you need a fast solution.
• Algorithms are step-by-step strategies that are guaranteed to produce a correct result. While this approach is great for accuracy, it can also consume time and resources.

Heuristics are often best used when time is of the essence, while algorithms are a better choice when a decision needs to be as accurate as possible.

4. Organizing Information

Before coming up with a solution, you need to first organize the available information. What do you know about the problem? What do you not know? The more information that is available the better prepared you will be to come up with an accurate solution.

When approaching a problem, it is important to make sure that you have all the data you need. Making a decision without adequate information can lead to biased or inaccurate results.

5. Allocating Resources

Of course, we don't always have unlimited money, time, and other resources to solve a problem. Before you begin to solve a problem, you need to determine how high priority it is.

If it is an important problem, it is probably worth allocating more resources to solving it. If, however, it is a fairly unimportant problem, then you do not want to spend too much of your available resources on coming up with a solution.

At this stage, it is important to consider all of the factors that might affect the problem at hand. This includes looking at the available resources, deadlines that need to be met, and any possible risks involved in each solution. After careful evaluation, a decision can be made about which solution to pursue.

6. Monitoring Progress

After selecting a problem-solving strategy, it is time to put the plan into action and see if it works. This step might involve trying out different solutions to see which one is the most effective.

It is also important to monitor the situation after implementing a solution to ensure that the problem has been solved and that no new problems have arisen as a result of the proposed solution.

Effective problem-solvers tend to monitor their progress as they work towards a solution. If they are not making good progress toward reaching their goal, they will reevaluate their approach or look for new strategies .

7. Evaluating the Results

After a solution has been reached, it is important to evaluate the results to determine if it is the best possible solution to the problem. This evaluation might be immediate, such as checking the results of a math problem to ensure the answer is correct, or it can be delayed, such as evaluating the success of a therapy program after several months of treatment.

Once a problem has been solved, it is important to take some time to reflect on the process that was used and evaluate the results. This will help you to improve your problem-solving skills and become more efficient at solving future problems.

A Word From Verywell​

It is important to remember that there are many different problem-solving processes with different steps, and this is just one example. Problem-solving in real-world situations requires a great deal of resourcefulness, flexibility, resilience, and continuous interaction with the environment.

Get Advice From The Verywell Mind Podcast

Hosted by therapist Amy Morin, LCSW, this episode of The Verywell Mind Podcast shares how you can stop dwelling in a negative mindset.

Follow Now : Apple Podcasts / Spotify / Google Podcasts

You can become a better problem solving by:

• Practicing brainstorming and coming up with multiple potential solutions to problems
• Being open-minded and considering all possible options before making a decision
• Breaking down problems into smaller, more manageable pieces
• Asking for help when needed
• Researching different problem-solving techniques and trying out new ones
• Learning from mistakes and using them as opportunities to grow

It's important to communicate openly and honestly with your partner about what's going on. Try to see things from their perspective as well as your own. Work together to find a resolution that works for both of you. Be willing to compromise and accept that there may not be a perfect solution.

Take breaks if things are getting too heated, and come back to the problem when you feel calm and collected. Don't try to fix every problem on your own—consider asking a therapist or counselor for help and insight.

If you've tried everything and there doesn't seem to be a way to fix the problem, you may have to learn to accept it. This can be difficult, but try to focus on the positive aspects of your life and remember that every situation is temporary. Don't dwell on what's going wrong—instead, think about what's going right. Find support by talking to friends or family. Seek professional help if you're having trouble coping.

Davidson JE, Sternberg RJ, editors.  The Psychology of Problem Solving .  Cambridge University Press; 2003. doi:10.1017/CBO9780511615771

Sarathy V. Real world problem-solving .  Front Hum Neurosci . 2018;12:261. Published 2018 Jun 26. doi:10.3389/fnhum.2018.00261

By Kendra Cherry, MSEd Kendra Cherry, MS, is a psychosocial rehabilitation specialist, psychology educator, and author of the "Everything Psychology Book."

Mental Models and Lifelong Learning

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• Philip Barker 2 &
• Paul van Schaik 2  

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Cognitive structures ; Human development ; Learning ; Learning community ; Learning resources ; Problem solving

The development of mental models is an important aspect of living and learning. These complex cognitive structures capture records of human experience and store them in the mind. They can subsequently be used for problem solving and goal-seeking activity. An individual’s collection of mental models starts to develop at an early age and is continually modified during that person’s lifetime. The set of mental models that are developed in early life may not be fully applicable to situations that arise in later life. During a person’s life span, continual learning is therefore necessary in order to fine-tune these models – thereby ensuring their currency.

Theoretical Background

The theoretical background issues underlying the work described in this contribution falls within two distinct, but closely related and overlapping domains: mental models and lifelong...

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Hershey, D. A., & Walsh, D. A. (1993). In J. Cerella, J. M. Rybash, W. Hoyer, & M. Commons (Eds.), Mental models and the maintenance of complex problem-solving skills in old age (pp. 553–584). San Diego: Academic.

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Norman, D. (1988). The psychology of everyday things . New York: Basic Books.

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Novak, J. D., & Gowin, D. B. (1984). Learning how to learn . New York: Cambridge University Press.

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Web References

http://autocww.colorado.edu/HomePage.html

http://www2.parc.com/istl/groups/uir/projects/bloodhound/bloodhound.htm

http://staff.aist.go.jp/kitajima.muneo/CoLiDeS_Demo.html

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Barker, P., van Schaik, P. (2012). Mental Models and Lifelong Learning. In: Seel, N.M. (eds) Encyclopedia of the Sciences of Learning. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-1428-6_604

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Opportunities and risks of large language models in psychiatry

• Nick Obradovich 1 , 2   na1 ,
• Sahib S. Khalsa   ORCID: orcid.org/0000-0003-2124-8585 1 , 2   na1 ,
• Waqas U. Khan 3 ,
• Jina Suh   ORCID: orcid.org/0000-0002-7646-5563 4 ,
• Roy H. Perlis 5 , 6 ,
• Olusola Ajilore   ORCID: orcid.org/0000-0003-0737-0437 7 &
• Martin P. Paulus   ORCID: orcid.org/0000-0002-0825-3606 1 , 2  

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The integration of large language models (LLMs) into mental healthcare and research heralds a potentially transformative shift, one offering enhanced access to care, efficient data collection, and innovative therapeutic tools. This paper reviews the development, function, and burgeoning use of LLMs in psychiatry, highlighting their potential to enhance mental healthcare through improved diagnostic accuracy, personalized care, and streamlined administrative processes. It is also acknowledged that LLMs introduce challenges related to computational demands, potential for misinterpretation, and ethical concerns, necessitating the development of pragmatic frameworks to ensure their safe deployment. We explore both the promise of LLMs in enriching psychiatric care and research through examples such as predictive analytics and therapy chatbots and risks including labor substitution, privacy concerns, and the necessity for responsible AI practices. We conclude by advocating for processes to develop responsible guardrails, including red-teaming, multi-stakeholder-oriented safety, and ethical guidelines/frameworks, to mitigate risks and harness the full potential of LLMs for advancing mental health.

Lay Summary

Imagine a conversation with an advanced computer program that could help doctors understand and treat mental health better. In this paper, we discuss using such advanced programs, called Large Language Models, in psychiatry. While they might offer quicker, tailored help and improve how mental health services work, there are many big questions left to answer about how to use this technology safely and ethically, to benefit patients without causing unintended harm.

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Introduction.

New technologies can profoundly affect mental health research and psychiatric treatment [ 1 ]. For example, mobile computing devices and telehealth software offered unprecedented opportunities to improve access to care (e.g., interacting with a psychiatrist or psychotherapist via a smartphone), monitor clinical status (e.g., using a tablet to track personal outcomes), and collect clinically relevant data (e.g., allowing a smartwatch to record blood pressure to gauge stress levels). These tools offer advantages including convenience, anonymity, affordability, and the ability to reach underserved populations. These advantages also reduce the costs [ 2 ] and logistical challenges of traditional research methods. Currently, marked advances in the sophistication of large language models (LLMs) [ 3 ] may be poised to alter population mental health, mental health research, and mental healthcare practices. To understand the implications of this technological advance, we provide an overview of LLMs and describe some prominent applications for mental health. Additionally, we discuss the opportunities and risks LLMs might pose to the public’s mental well-being and to healthcare professionals’ efforts.

Function and use of LLMs

The term artificial intelligence (AI) denotes both (i) a scientific discipline studying how to build and understand computer systems that can complete endeavors deemed difficult because of the intelligence they seem to involve and (ii) those computer systems themselves [ 4 ]. As a scientific discipline, AI contains subfields that focus on the development of mechanical devices that can accomplish tasks for humans via movement, computing systems that can extract information from images, and software that can construe information in natural language for the purpose of devising further information for a user. As computer systems exhibit intelligence, AI possesses diverse abilities to solve problems, devise complex plans, integrate diverse technical information, and generate content (e.g., text, images, and audio). AI tools that generate content such as text and images have received attention over the past year due to the release of systems that make these tools easily accessible online (e.g., by February 2023, OpenAI’s suite of AI tools reached 100 million active users in 2 months) [ 5 ].

While the remarkable performance of the most advanced AI tools is quite novel, they rely on technical infrastructure and training processes—namely, artificial neural networks and machine learning—that have existed for decades [ 6 , 7 , 8 , 9 ] but rapidly progressed recently with the expansion of computing power that allows them to operate at substantially larger scale [ 10 ]. Artificial neural networks [ 8 ] are systems of weighted mathematical functions that slightly resemble neurons in the brain, ‘firing’—that is, producing an output—when the value of their inputs reaches some threshold value [ 8 , 11 , 12 ]. Machine learning constitutes a wide range of statistical methods [ 13 ], including those for estimating parameters and specifying connection structures in artificial neural networks [ 12 ]. Progress in these methods has occurred in recent decades [ 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ], particularly with the development of simplified network architecture (i.e., transformers) capable of rapidly and efficiently conducting parallel processing of sequential data such as text [ 15 ].

From these methods, models with impressive language capacities have emerged (e.g. ChatGPT-4 from OpenAI [ 16 ]). Humans can hold advanced conversations with LLM partners via a standard interchange: a user supplies a ‘prompt’ to the system in the form of a written statement and the LLM generates an output in the form of the most probable completion of the prompt. Prompts typically consist of instructions, context, input data, and output indicators, although these categories can be arbitrary (e.g. as in few-shot learning). Once a user supplies a prompt as input, the system breaks the prompt’s words into fragments that are passed through the model’s neural network to produce an output that it returns to the user [ 12 ]. This output is the model’s estimate of the most probable continuation of the input that the user supplied.

Because of their myriad cross-domain applications and public accessibility, LLMs have rapidly entered widespread use. Their abilities now figure in tools ranging from online search assistants to social bots, from essay-writing tools to clinical therapeutic assistants.

Potential implications of LLMs for mental health

The application of LLMs as general-use technological tools has already yielded anticipated and unanticipated consequences [ 17 ]. LLMs notably excel in efficiently acquiring information, condensing content, and tackling reasoning-intensive problems [ 3 ]. In the context of mental healthcare, LLMs could conceivably quickly assimilate patient data, summarize therapy sessions, and aid in complex diagnostic problem-solving, thus potentially saving users and health systems both significant time and effort. Furthermore, when integrated into roles as personal assistants, LLMs can help individuals keep better track of their priorities and tasks, thus helping them achieve objectives in their daily lives. In this section, we discuss implications that include equitable access to the tools, manners in which LLMs may reshape mental healthcare systems, certain population mental health risks posed by the tools, and a framework for considering and evaluating such risks.

Equitable access to tailored LLMs

While ‘out-of-the-box’ LLMs might perform adroitly at mental healthcare-related tasks, they can also be further aligned to such tasks via approaches such as fine-tuning (i.e., trained specifically using additional data after its initial training), in-context-learning, or retrieval augmented generation, among other techniques to enhance task-specific performance (i.e., allowing longer prompts). Such models when fine-tuned for mental healthcare could, for example, offer a detailed list of therapeutic resources when asked how to manage a particular phobia. However, while the capacity to process information is increasing, processing such information at scale can be quite costly in terms of computing power and development resources, and therefore not all providers or institutions may benefit equally from the ability to fine-tune LLMs. This could become particularly problematic in mental health contexts, where accuracy in understanding the nuanced expressions of symptoms, emotions, and self-reported experiences is paramount.

Managerial and process-related considerations

LLMs have the potential to reshape health system managerial activity and work processes [ 18 ]. In a recent example of this possibility, Jiang et al. [ 19 ] demonstrated how an LLM trained on clinical notes could successfully predict patients’ readmission, in-hospital mortality, comorbidity index, length of stay, and insurance denial in the NYU Langone Health System. On its face, the findings relate solely to prognosis, but viewed more broadly the results provide grounds for “systems solutions”—redesigns of the hospital’s management and processes [ 20 ] that enhance decision-making via acting on the implications of AI predictions [ 18 ]. Operational decisions about personnel schedules and equipment needs could draw on the readmission and length of stay predictions reported in the paper—that is, a prediction that a wave of patients might require readmission or longer stays could activate increases in staffing levels and hospital material orders. Likewise, financial and accounting decisions could follow from bulk predictions of insurance denial: predictions of denial could galvanize steps to redirect funds from other parts of the organization or gather charitable resources to cover care a patient cannot afford. From simple predictions made by those five outcomes and additional training relating to the administrative implications of those outcomes, the LLM could automate managerial decisions within the hospital system, as research has discussed in other contexts [ 18 ]. Such possibilities portend a future in which not only clerical tasks become automated but also certain administrative ones.

Population mental health risks of LLMs

One significant risk associated with the general capacities of LLMs is that they not only make human labor more efficient, they can substitute for that labor [ 21 ]. If such labor substitution occurs rapidly and en masse , population mental health could experience declines mirroring those from prior economic downturns; clinical communities should prepare for such downturns. Another potential impact of LLMs on population mental health relates to the ease of developing conversational applications via the models. Due, perhaps, to the development of these tools for chat applications that require a warm tone, such as customer service, production LLMs typically exhibit a distinctive attribute: they provide pleasant interactions. On its face, this pleasant tone appears beneficial. Having an approachable AI with which to converse might seem to add a friend, albeit simulated, to one’s life and it might make day-to-day tasks more pleasing. But a fluid conversation partner that recalls detailed aspects of your life, coupled with a warm tone, runs the risk of sparking a feedback loop whereby users substitute human-to-human socialization with human-to-machine interaction. Innovative research studying the iterative rollout of Facebook across U.S. college campuses now provides reason to believe that social media may have produced unintended and costly consequences for individual well-being [ 22 ], and LLMs raise similar—if not greater—potential for such outcomes [ 23 ]. Moreover, a subset of adults can experience the onset or worsening of depression with greater social media use [ 24 ]. This risk grows because, much like social media, some AI chat applications may be optimized for user engagement, thus encouraging prolonged interaction. However, unlike social media, LLMs interact in ways that involve instantaneous conversation, the provision of information users sought for its value in professional or leisure purposes, and extensive opportunities for customization that can further enhance engagement.

It is important to recognize that the impact of LLMs on individual well-being can differ from person to person or may depend on how they are used, much like the effects of social media. Previous researchers not only have produced AI models with the explicit goal of enhancing users’ peer-to-peer empathy, but they have also demonstrated that the use of such models via human-AI collaboration actually can increase user empathy over time [ 25 ]. Consequentially, a substantial outcome involves the possibility of augmenting real-world or online human interactions with LLM interactions due to feelings of isolation or loneliness, which are currently at an all-time high [ 26 ]. For example, LLMs can be helpful for people looking to increase their social activities locally (e.g. via aiding internet searching for such options). On the converse, those suffering from social anxiety, social disconnection, or loneliness might be more inclined to use LLMs as a substitute for human-to-human socialization. These risks and benefits are ultimately empirical questions that require further study and consideration of (1) the situations in which LLMs are used, (2) the alternatives available to any given individual, (3) the principles guiding LLMs’ responses to queries, and (4) the degree of monitoring of the consequences of interactions with LLMs.

A framework for risk assessment

Scholars [ 27 ] have proposed a framework to better examine the broad social impact of LLMs, which includes grievous problems such as embedding biases, reinforcing stereotypes, violating privacy, and exacerbating inequalities, among other issues. LLMs – similar to other artificial intelligence and machine learning tools [ 28 ] – are subject to bias [ 29 ], both stemming from the content on which they are initially trained and the subsequent reinforcement input designed to shape their behavior. As such, practitioners should be cognizant of potential biases in the models that may manifest in unexpected ways in practice. Added to this are concerns about the potential for nonsensical or fanciful responses by LLMs to produce misinformation or confusion among users, particularly those users with pre-existing thought disorders. Given the complex determinants of population mental health, such broad-scale social implications [ 17 ] require holistic evaluation.

In the medical realm, there are many existing and proposed frameworks/guidelines for developing AI systems that include LLMs. However, none to our knowledge provide a sufficiently clear understanding of what safety parameters should be assessed and whose safety should be considered when determining whether an AI medical application, tool, or device is safe to launch [ 30 , 31 , 32 , 33 ]. Moreover, current frameworks/guidelines often lack a surveillance component to monitor and identify the long-term risks and benefits of AI medical systems. One approach that mitigates these concerns is the Biological-Psychological, Economic, and Social (BPES) framework [ 34 ]. This innovative framework applies aspects of the biopsychosocial model used in psychiatry while providing a descriptive approach that guides developers and regulatory agencies on how to assess whether an AI medical system is safe to launch (Fig.  1 ). More specifically, it asks developers to demonstrate the safety of their AI technology for multiple stakeholders (patients, healthcare professionals, industry, and government healthcare agencies) while using the BPES domains as parameters [ 34 ]. Another contribution of the BPES framework is its compatibility with and ability to provide context for all existing and future AI medical regulatory and non-regulatory frameworks/guidelines, including red-teaming approaches, that are concerned with human safety and AI product/system efficacy [ 34 ]. Although potentially useful, one aspect this model does not comprehensively address is the need for human-centered, iterative, participatory design prior to randomized trial conductions; such considerations could be incorporated in future iterations.

Adapted from the pharmaceutical industry’s multi-phase clinical drug trial model, the BPES Framework is a translational scientific approach that can be applied to any regulatory or non-regulatory guideline that assesses an AI-based medical system’s safety and efficacy, before it is launched to the public. Figure created with Biorender.com.

The BPES framework, or others like it, could also be nested within broader efforts to evaluate “Software as a Medical Device” (SaMD). For example, the International Medical Device Regulators Forum (IMDRF [ 35 ]) is a global group of medical device regulators have begun expanding their harmonizing efforts to include SaMD and has established a working group on artificial intelligence/machine learning [ 36 ] focused on promoting the development of safe and effective practices. However, in current practice, effective regulation remains an aspiration rather than a reality.

Opportunities of LLMs for psychiatric care and research

LLMs [ 37 ] hold marked potential for mental healthcare and research [ 38 ], primarily due to the central role language plays in the description, manifestation of, and treatment for mental health disorders. [ 39 ] A straightforward application of LLMs in mental healthcare might focus on the status assessment of an individual’s mental health or the use of verbal/language-based interventions to change this state. LLMs could thus potentially help to assess mental illness severity, suggest possible diagnoses, generate treatment plans, monitor the effect of an intervention, provide risk assessment indicators (e.g., for recurrence or relapse of a condition), and offer evidence-based suggestions for when an intervention is no longer needed. For instance, Galatzer-Levy et al. [ 40 ] examined whether an LLM without finetuning, yet “explicitly trained on large corpuses of medical knowledge,” could project an individual’s psychiatric functioning accurately based on patient and clinical documentation. The team found that, indeed, the LLM succeeded in this task: they could not reject the null hypothesis that the LLM performed the same as human clinical assessors [ 40 ]. Such findings underscore the fact that LLMs have demonstrated potential in interpreting verbal information to infer underlying affect, cognition, and diagnosis, providing a novel approach to assessing an individual’s mental health [ 41 ].

In another investigation of the clinical utility of therapy chatbots, a recent study described the development of LUMEN, a problem-solving treatment therapy coach that utilized the natural language understanding and natural language processing features of the Amazon Alexa platform. Participants were randomized to 8 sessions with LUMEN or a waitlist control arm with functional neuroimaging, and clinical and behavioral assessments conducted before and after treatment. This pilot study found that compared to the waitlist control group, participants who received LUMEN showed increases in task-evoked dorsolateral prefrontal cortex (DLFPC) activity that correlated with improvements in problem-solving skills and reductions in the avoidant coping styles. Furthermore, there were modest effects for reductions in depression and anxiety symptoms in the LUMEN group compared to the control group [ 42 ]. Although this study demonstrated evidence that the use of therapy chatbots might reduce symptoms of internalizing disorders, it was conducted in a small sample of participants with mild-to-moderate depression and/or anxiety. Further work is needed to determine whether such digital mental health interventions might be appropriate or effective for patients with greater clinical symptom severity, or at population-level scales.

These results highlight just how quickly the technology surrounding language models is evolving. For example, with minimal instruction and data from a problem-solving treatment manual, a version of LUMEN was created with ChatGPT-4 in under an hour (by O.A. and colleagues on November 28, 2023). Beyond the advantage of speed, another benefit of using an LLM is that users can interact with the chatbot more naturally and can personalize the style and tone of the conversational interface, to accomplish tasks that include identifying potential mental health conditions, determining the causes of those conditions, recognizing emotions in conversations, and understanding the relationship between environmental events and emotional responses [ 43 ]. However, a major disadvantage – aside from the limitation that the chatbot is derived from a specific region and culture – is the lack of control and constraints on how the LLM-derived chatbot can and will respond. Notably, the performance of ChatGPT has been found to be sensitive to different prompting strategies and emotional cues [ 44 ]. Thus, current models have limitations [ 45 ], including unstable predictions and sensitivity to minor alterations in prompts [ 44 ]. Future development in this domain should consider approaches to allow for more freeform user input while restricting chatbot output to ensure treatment fidelity.

These applications of LLMs in clinical settings are a direct consequence of the models’ powerful capacity for natural language processing (NLP). For example, LLM-based NLP models can detect medication information from clinical notes, generate and process psychiatric clinical narratives, and extract clinical concepts and associated semantic relationships from text [ 46 , 47 ]. Such abilities to produce thematically accurate texts from automated transcriptions of clinical interactions may be particularly useful to the psychotherapeutic setting, potentially enabling clinicians to focus more attention on patients and less on real-time or post-hoc clinical note-taking. Further, LLM-driven evaluations of textual information are increasingly being applied to healthcare scenarios like perinatal self-harm, suicide attempts, HIV risk assessment, and delirium identification [ 43 ]. For example, LLMs have generated suicide risk predictions using health records data [ 48 ]. Others have demonstrated the cost-effectiveness and improved recovery rates associated with using a conversational AI solution for “referral, triage, and clinical assessment of mild-to-moderate adult mental illness” [ 49 ].

LLMs also have the potential to enhance psychotherapy by powering chatbots that provide evidence-based interventions. Such chatbots have demonstrated significant potential in the realm of mental health applications, providing a range of psychological interventions such as cognitive behavioral therapy [ 50 ], acceptance and commitment therapy [ 51 ], and positive psychology skills [ 52 ]. Clinicians have used them to assist patients with various conditions, including panic disorder [ 50 ], hypertension [ 53 ], and cancer [ 52 ]. They have even proven beneficial in postoperative recovery scenarios [ 51 ], and have also enhanced knowledge and skills related to health management [ 54 ]. Chatbots can deliver interventions at the user’s convenience, track progress, send reminders, and provide real-time feedback, perhaps enhancing adherence to treatment and self-management practices in the process. Moreover, they can potentially reduce healthcare costs by addressing minor health concerns that do not necessitate a doctor’s visit. However, research has raised concerns that these chatbots need better domain-specific training data, fine-tuning by expert clinicians, and targeting toward more “skilled” end-users [ 55 ]. Such chatbots also should be subject to efficacy trials to determine whether or not they actually improve clinical outcomes. Further, the degree to which both users and clinicians support the use of chatbots in clinical settings is worth additional scrutiny.

Another emerging application of LLM is clinical decision support, sometimes described more hyperbolically as “precision medicine”. Even LLMs trained on broad corpora (i.e., not specific to medicine) encode large amounts of medical knowledge, recognizing medications, indications, and adverse effects, and they can even demonstrate physician-level performance on board examinations, with the highest scores for psychiatry [ 56 ]. For example, one initial investigation applied ChatGPT-4 without fine-tuning or augmentation to suggest potential next-step antidepressant treatments [ 57 ] using a set of previously validated clinical vignettes. That study found that an optimal medication was identified ~3/4 of the time. On the other hand, in nearly half of the vignettes, the model also identified a potentially contraindicated or suboptimal treatment option.

More recently, a follow-up study examined the application of an LLM augmented with excerpts from clinical treatment guidelines for bipolar depression. Rather than applying a standard retrieval-augmented generation approach, this model simply incorporated key guideline principles in the prompt itself. This approach resulted in the selection of an expert-identified next-step treatment option twice as often as the unaugmented model and outperformed a small sample of community clinicians [ 58 ]. Notably, while the rate of selection of potentially contraindicated or less preferred options was diminished in the augmented model, such options appeared in approximately 1 in 10 vignettes, highlighting the need to apply such models with care. Prior work using clinical dashboards also underscored the capacity for psychiatric decision-support tools to lead clinicians astray [ 59 ].

LLMs may also prove to be valuable in formulating psychiatric research questions. Currently, researchers must analyze a growing volume of information to formulate hypotheses and discern the constellation of factors contributing to an individual’s mental health disorder for effective treatment research planning. In comparison, LLMs can rapidly and inexpensively generate—or provide feedback on—themes, questions, and proposed mechanisms at the level of disorders, while identifying possible factors underlying a particular individual’s mental health condition and formulating an individualized treatment plan [ 60 ]. Although this rapidly developing field is currently in its infancy, lessons learned from other areas of clinical science may be instructive. Thus, a phased approach for how to develop AI systems for use in mental health (e.g., one akin to the Phase I – IV process for evaluating new pharmacological agents) is likely to be of considerable value.

Risks of LLMs for psychiatric care and research

The application of LLMs in mental health research and care, while promising, is not without potential challenges [ 61 ]. First, the unpredictability and non-deterministic nature of some LLMs’ output causes concern in clinical settings. Like humans, these models can occasionally produce factually incorrect answers, make errors in simple arithmetic or logical problems, or generate responses that are seemingly obvious yet erroneous. And unlike humans, the models sometimes produce outputs that are outlandish and/or realistic yet fake (e.g., properly formatted citations to plausible-sounding journal articles that do not actually exist). The current state of research does not provide a clear methodology for guiding LLMs to minimize these inappropriate, false, or misleading outputs. Further compounding this challenge is the fact that LLMs by default do not have the capability to accurately attribute the sources underlying the information that they provide, and it depends on which layer of the foundation model [ 62 ] supply chain the clinical applications are built on top of. Such lack of attribution compounds the practical challenge of double-checking the information provided by any given LLM.

Second, scientific understanding of the mechanisms by which LLMs generate seemingly intelligent responses is still in its infancy [ 63 ]. For instance, if a user shares feelings of intense despair or mentions self-harm, an LLM that generates responses based on learned patterns rather than a real understanding of context or emotion might fail to accurately identify the urgency or respond appropriately. This lack of genuine empathy and understanding may perpetuate negative user experiences or intensify feelings of distress. Thus, while LLMs can aid mental health services, they must be used with robust monitoring systems and cannot replace professional intervention. Such monitoring systems may in turn require the oversight of clinical professionals, potentially providing additional roles and responsibilities for mental health clinicians.

Third, LLMs inherently express values in their communication, and those values might not comport with those of users. As a field, mental healthcare clinicians and researchers typically follow the principle of beneficence, which aims to promote human welfare and reduce suffering through knowledge and interventions. However, whether any particular LLM exhibits behaviors consistent with such values remains an empirical question [ 64 ].

Fourth, the potential for clinicians and researchers to become dependent on such systems is worth considering [ 65 ] as LLMs become widely adopted in the workplace. Even if such dependence does not materialize, it is likely that the requisite skills and practice of clinicians will change in the future as LLMs are integrated into clinical care settings. The manner and degree to which such change occurs is ultimately a question that will best be gauged via longitudinal study. As with any new technology or intervention, clinical trials will be invaluable in understanding where and how LLMs can be best deployed. The key elements of such trials include careful consideration of comparators, potential adverse effects, and sources of bias [ 66 ].

Finally, a suite of additional challenges – e.g., medical data imbalance and data shortage, potential data leakage and privacy implications, the need for carefully designed and evaluated prompts, the insufficient availability of open models trained across a variety of languages, the details of ethical implementation, and the difficulty of obtaining large, annotated corpora in specific medical fields – will need to be addressed in the process of incorporating LLMs into psychiatric care and research. Of specific concern are two additional facts. First, many of the health data that might be used to adapt LLMs to psychiatric contexts may fall under formal privacy protections (e.g. the Health Insurance Portability and Accountability Act, HIPAA), and ensuring such data are not subsequently revealed by the production LLM – a form of data leakage [ 67 ] – is an open problem. Second, those firms who already possess large stores of medical data such as large hospital systems and data brokerage firms that buy out data may have an inherent advantage in the use of these tools. Efforts to ensure equitable access are needed. Such challenges underscore the need for all parties engaged in mental healthcare to contemplate carefully how best to deploy LLMs to augment individual well-being.

Establishing responsible guardrails for the use LLMs in psychiatric care and research

In the context of AI deployment, the field of Responsible AI (RAI) has examined the ethical use of AI systems in various contexts, with many industry and academic researchers suggesting design principles and guidelines for others to follow. In practice, following these principles requires a significant amount of human oversight and investment to adhere to these principles. For instance, this includes impact assessments, a deep analysis of how AI affects stakeholders, and considering societal, cultural, and ethical implications, and it requires implementing human checks and controls to guide AI behavior, ensuring alignment with ethical standards and societal norms [ 68 ].

Perhaps the most critical activity of RAI human oversight is called ‘red-teaming’ [ 69 ]. The term, borrowed military strategy, describes the practice of adopting an adversarial approach to challenge and improve strategies, plans, and systems by identifying vulnerabilities and mitigating potential harms an opponent might exploit. This concept has been subsequently applied to technology, especially in cybersecurity. With the recent boom in generative AI technologies, AI red-teaming has gained considerable traction and attention as a method of ensuring AI safety, with AI companies jumping to hire professional and volunteer red-teamers (e.g., the most recent example being OpenAI’s announcement in February 2024 of Sora, a text-to-video engine that is currently available only to red-teamers).

The range of practices for AI red-teaming varies from open-ended to targeted and from manual to automated. For example, GPT-4 went through iterative rounds of red-teaming with the help of domain experts [ 70 ]. In another example, a Multi-round Automatic Red-Teaming (MART) method was used to enhance the scalability and safety of LLMs by iteratively fine-tuning a target LLM against automatically generated adversarial prompts, reducing safety violations by up to 85% without compromising performance on non-adversarial tasks [ 71 ].

Despite AI red-teaming efforts focused on computing and ethics, the omission of psychiatric-specific concerns represents a significant gap, hindering the responsible use of LLMs in psychiatric care and research. For psychiatric applications of red-teaming, key considerations include scoping intended application scenarios, selecting a diverse group for testing across various demographics and experiences, defining the technical scope of what will be tested (e.g., public vs. proprietary models, safety layers, or user interfaces), and deciding on the testing methodology, whether it be organized teams or scattered volunteers, and open or targeted tasks. In developing guardrails and testing the manners in which psychiatric applications of LLMs may present challenges, the role of red-teaming for LLM-based applications in psychiatry should not be overlooked. AI in mental health must ethically align with therapeutic goals while balancing efficiency and sensitivity, respecting patient autonomy and confidentiality, and avoiding the reinforcement of stigmas. AI testing in mental health should also evaluate its impact on psychological well-being and therapeutic outcomes, focusing on data sensitivity, patient privacy, cultural adaptability, and the potential for emotional harm. There are emerging examples of efforts to establish AI safety benchmarks [ 72 , 73 ], but it is worth emphasizing that implementing AI in mental health demands precision and human accountability and requires systems that adapt to individual needs and include robust mechanisms for clinician oversight to support, not replace, professional judgment.

The challenges we outline above thus necessitate the active engagement of domain experts in psychiatry to make the red-teaming of AI systems more robust, clinically sound, and safe for mental health. Such experts should include not only clinicians, but individuals with lived experience, family members, and policymakers – all of whom will be impacted by the success or failure of these models.

The use of LLMs in healthcare [ 19 ], mental health, and psychiatric research remains in its infancy. These models have shown promising potential for early detection, treatment, prediction, and scientific evaluation of mental health conditions. Increasing the reliability and predictability of LLMs can enhance clinician-researcher trust and understanding, thus facilitating the successful integration of risk models into clinical care and psychiatric research. The advent of LLMs will alter the landscape of public mental health, via direct social effects likely to stem from the technology as well as via the integration of LLMs into mental healthcare and research [ 60 ]. LLMs hold significant promise for mental healthcare and research, presenting opportunities to expedite diagnostic processes, enhance patient care, and contribute to research efforts in psychiatric science. Simultaneously, the use of LLMs for personal assistance can have far-reaching implications for population mental health, both positive and negative.

Nevertheless, the deployment of these advanced AI models will bring challenges, including ethical concerns [ 74 ]. Their limitations, such as the unpredictability of outputs in some models, lack of understanding of response generation mechanisms (i.e., lack of transparency), potential for inappropriate or false outputs, and possible mismatch of values and introduction of bias, present significant hurdles. Going forward, the next steps should involve rigorous interdisciplinary dialogue and research into understanding and overcoming these challenges via the establishment of responsible guardrails. Additionally, exploring ways to fine-tune LLMs with the guidance of clinicians and to expand their accessibility to a wider range of users is essential. Finally, the profound changes LLMs might bring justify efforts that study the system-wide effects of LLMs on population mental health, including socioeconomic consequences that might subsequently alter people’s well-being. It is through these concerted efforts that we can maximize the potential benefits of LLMs in mental healthcare and research while minimizing their associated risks.

Citation diversity statement

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Acknowledgements

N.O., M.P., and S.K. thank Tim Johnson for his extensive contributions to the initial draft of the manuscript and thank members of the Laureate Institute for Brain Research’s Large Language Models and Mental Health Discussion Group for useful conversations.

This work was partly funded by The William K. Warren Foundation, the National Institute of General Medical Sciences Center (Grant 2 P20 GM121312), the National Institute on Drug Abuse (U01DA050989), and the National Institute for Mental Health (R01MH127225; R01MH123804).

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These authors contributed equally: Nick Obradovich, Sahib S. Khalsa.

Authors and Affiliations

Laureate Institute for Brain Research, Tulsa, OK, USA

Nick Obradovich, Sahib S. Khalsa & Martin P. Paulus

Oxley College of Health and Natural Sciences, University of Tulsa, Tulsa, OK, USA

Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada

Waqas U. Khan

Microsoft Research, Redmond, WA, USA

Center for Quantitative Health, Massachusetts General Hospital, Boston, MA, USA

Roy H. Perlis

Department of Psychiatry, Harvard Medical School, Boston, MA, USA

Department of Psychiatry & Behavioral Health, University of Illinois Chicago, Chicago, IL, USA

Olusola Ajilore

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NO, SK, MP: Substantial contributions to the conception of the work. NO, SK, WK, JS, RP, OA, MP: (1) Drafting and revising the work critically for important intellectual content, (2) Final approval of the version to be published, and (3) Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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Correspondence to Sahib S. Khalsa .

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Competing interests.

Dr. Obradovich is part of OpenAI’s Researcher Access Program and has received resources from OpenAI to support research activities outside of this work. Dr. Khalsa provides uncompensated service on executive boards of the International Society for Contemplative Research and the Float Research Collective, serves on the editorial boards of Biological Psychology and JMIR Mental Health, and has served on a scientific advisory board for Janssen Pharmaceuticals. Dr. Khan has no conflicts of interest or disclosures to make that are relevant to the content. Dr. Suh is employed by Microsoft Research. Dr. Perlis has served on scientific advisory boards for Circular Genomics, Genomind, Belle Artificial Intelligence, Swan AI Studios, and Psy Therapeutics. Dr. Ajilore is the co-founder of KeyWise AI and has served as a consultant for Otsuka Pharmaceuticals on the advisory boards for Sage Therapeutics, Embodied Labs, and Blueprint Health. Dr. Paulus advises Spring Care, Inc., receives royalties from an article on methamphetamine in UpToDate, and has a compensated consulting agreement with Boehringer Ingelheim International GmbH.

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Obradovich, N., Khalsa, S.S., Khan, W.U. et al. Opportunities and risks of large language models in psychiatry. NPP—Digit Psychiatry Neurosci 2 , 8 (2024). https://doi.org/10.1038/s44277-024-00010-z

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Published : 24 May 2024

DOI : https://doi.org/10.1038/s44277-024-00010-z

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AI models can outperform humans in tests to identify mental states

Large language models don’t have a theory of mind the way humans do—but they’re getting better at tasks designed to measure it in humans.

• Rhiannon Williams archive page

Humans are complicated beings. The ways we communicate are multilayered, and psychologists have devised many kinds of tests to measure our ability to infer meaning and understanding from interactions with each other. 

AI models are getting better at these tests. New research published today in Nature Human Behavior found that some large language models (LLMs) perform as well as, and in some cases better than, humans when presented with tasks designed to test the ability to track people’s mental states, known as “theory of mind.” 

This doesn’t mean AI systems are actually able to work out how we’re feeling. But it does demonstrate that these models are performing better and better in experiments designed to assess abilities that psychologists believe are unique to humans. To learn more about the processes behind LLMs’ successes and failures in these tasks, the researchers wanted to apply the same systematic approach they use to test theory of mind in humans.

In theory, the better AI models are at mimicking humans, the more useful and empathetic they can seem in their interactions with us. Both OpenAI and Google announced supercharged AI assistants last week; GPT-4o and Astra are designed to deliver much smoother, more naturalistic responses than their predecessors. But we must avoid falling into the trap of believing that their abilities are humanlike, even if they appear that way. 

“We have a natural tendency to attribute mental states and mind and intentionality to entities that do not have a mind,” says Cristina Becchio, a professor of neuroscience at the University Medical Center Hamburg-Eppendorf, who worked on the research. “The risk of attributing a theory of mind to large language models is there.”

Theory of mind is a hallmark of emotional and social intelligence that allows us to infer people’s intentions and engage and empathize with one another. Most children pick up these kinds of skills between three and five years of age. 

The researchers tested two families of large language models, OpenAI’s GPT-3.5 and GPT-4 and three versions of Meta’s Llama , on tasks designed to test the theory of mind in humans, including identifying false beliefs, recognizing faux pas, and understanding what is being implied rather than said directly. They also tested 1,907 human participants in order to compare the sets of scores.

The team conducted five types of tests. The first, the hinting task, is designed to measure someone’s ability to infer someone else’s real intentions through indirect comments. The second, the false-belief task, assesses whether someone can infer that someone else might reasonably be expected to believe something they happen to know isn’t the case. Another test measured the ability to recognize when someone is making a faux pas, while a fourth test consisted of telling strange stories, in which a protagonist does something unusual, in order to assess whether someone can explain the contrast between what was said and what was meant. They also included a test of whether people can comprehend irony. 

The AI models were given each test 15 times in separate chats, so that they would treat each request independently, and their responses were scored in the same manner used for humans. The researchers then tested the human volunteers, and the two sets of scores were compared. 

Both versions of GPT performed at, or sometimes above, human averages in tasks that involved indirect requests, misdirection, and false beliefs, while GPT-4 outperformed humans in the irony, hinting, and strange stories tests. Llama 2’s three models performed below the human average. However, Llama 2, the biggest of the three Meta models tested, outperformed humans when it came to recognizing faux pas scenarios, whereas GPT consistently provided incorrect responses. The authors believe this is due to GPT’s general aversion to generating conclusions about opinions, because the models largely responded that there wasn’t enough information for them to answer one way or another.

“These models aren’t demonstrating the theory of mind of a human, for sure,” he says. “But what we do show is that there’s a competence here for arriving at mentalistic inferences and reasoning about characters’ or people’s minds.”

One reason the LLMs may have performed as well as they did was that these psychological tests are so well established, and were therefore likely to have been included in their training data, says Maarten Sap, an assistant professor at Carnegie Mellon University, who did not work on the research. “It’s really important to acknowledge that when you administer a false-belief test to a child, they have probably never seen that exact test before, but language models might,” he says.

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