The Great Mental Models

The problem it deals with

Most business decisions are made quickly, with partial information, in a field you know only in part. Habit takes over, and habit fails when the situation is new. Parrish's answer, drawn from the work of Charlie Munger and others, is to hold a range of models from different fields so you have more than one lens on a problem. He says the person with the fewest blind spots wins, and that having only one model makes you use it where it does not fit.

What changes after you read it

You collect a short list of questions. Am I looking at the thing or at a summary of it? Is this inside what I properly understand? What happens after the first effect? What would make this fail? What is the simplest explanation? Do I have evidence of bad intent, or just a mistake? None of the questions gives the answer, but each one stops a specific kind of error.

Why it suits a business owner

Owners face pricing, hiring, product and cash problems with no time to become expert in each. General models carry across all of them. The chapter on second-order thinking is useful for pricing and incentives, because it includes the story of the bounty on cobras that produced more cobras. The chapter on the circle of competence reminds you to check the incentives of any adviser you rely on when you are outside your own field.

Where it falls short

The book is an introduction. Each model gets a chapter of a few pages, many examples are famous stories retold, and some stories, such as the lab-grown meat and the tiger masks, take space that a practical reader might prefer to use on method. It does not tell you how to combine the models in a live decision. If you already use mental models, you will meet mostly familiar ideas, and the authors' website covers many of them.

When to read it

Read it before a decision that is hard to reverse, or after one that went wrong in a way you did not expect. The chapters are short, and you can read one and use it on the same day. It is also useful if you manage people, because naming a model gives a team a shared way to challenge a decision.

How it connects to decisions you can log

Models become habits when you use them on purpose. Next to each decision in your log, note which model you applied and what it showed, then check the result later. The book makes a related point about second-order effects: the consequences you did not consider are the ones you will have to deal with anyway. Over time the log shows which models help in which kinds of decision, and those can become a standard checklist for the team.

Shane Parrish, founder of the website Farnam Street, argues that the quality of your thinking depends on the models in your head and that a wide set of models drawn from many fields beats deep knowledge of one. This is the first volume of the Great Mental Models project and presents nine general thinking concepts, followed by three supporting ideas. He says that none of the ideas is his own and that they come from people such as Charlie Munger, Nassim Taleb, Peter Bevelin, Richard Feynman and Charles Darwin.

Preface

Parrish explains how he came to the subject. He was promoted early at an intelligence agency after 11 September 2001, with no training in making decisions. His MBA did not help, but it led him to Charlie Munger and the idea of a latticework of mental models. He started the website Farnam Street, and the book is the one he wished had existed when he began.

Introduction: Acquiring Wisdom

He defines a mental model as a simple representation of how something works and says the person with the fewest blind spots wins. The introduction uses the myth of Antaeus, who lost his strength when lifted from the ground, to argue that understanding must keep touching reality. Three things get in the way: a limited vantage point, ego and distance from the consequences of our decisions. A sidebar on the three buckets of knowledge, physics, biology and human history, explains why larger and more relevant samples make for better models. The introduction ends by saying that understanding means little unless you change what you do.

The Map is not the Territory

A map is a reduction of what it describes, and that is why it is useful. Korzybski's phrase reminds us that the description is not the thing, and the chapter lists what to watch for. Maps can be out of date, they reflect the purpose and values of their maker and they leave out risks that exist on the ground. Sidebars cover the tragedy of the commons, Taylor's model of management and a map scaled one mile to one mile. The habit is to understand a model's limits before you rely on it.

Circle of Competence

Using a story of a town's lifer and a visiting stranger, Parrish says true knowledge of a complex territory cannot be faked. Inside your circle you know what you do not know and can decide fast. You build one through curiosity, monitoring and feedback, over years. When you must act outside it, learn the basics, talk to someone with a strong circle and use broad models, and weigh their incentives. The sidebars cover the problem of incentives and Buffett's remark about Rose Blumkin staying within what she knew.

First Principles Thinking

First principles thinking separates the underlying facts from the assumptions built on them, so you can reason from what is non-reducible in your situation. The chapter explains Socratic questioning, asking why and being sceptical of your own assumptions. A long example is the discovery that bacteria cause most stomach ulcers, which overturned the belief that stomachs were sterile. Another is the effort to grow meat in a laboratory by reproducing what makes meat taste like meat.

Thought Experiment

A thought experiment is a device of the imagination for investigating the nature of things. It lets you test ideas that cannot be tested physically, explore consequences and learn from mistakes without paying for them. Examples include betting on a basketball match, re-imagining history with counterfactuals, which the authors warn against over-trusting, and the trolley problem. Another sidebar, Reduce the Role of Chance, runs a stock purchase made partly with borrowed money through many imagined outcomes to separate luck from skill.

Second-Order Thinking

First-order thinking looks at immediate results, and second-order thinking asks what happens next. Parrish gives the bounty on cobras in colonial Delhi, which led people to breed snakes, and the use of antibiotics in livestock. Buffett's parade of people on tiptoes shows how second-order effects can leave everyone worse off. The chapter says to use the model to weigh long-term interests against short-term gains and to build stronger arguments, with Cleopatra and Mary Wollstonecraft as examples.

Probabilistic Thinking

Probabilistic thinking estimates the likelihood of outcomes using some maths and logic. Three ideas are central: Bayesian thinking, which updates an estimate as new evidence arrives, fat-tailed curves, where extreme events have no real cap, and asymmetries in odds and payoffs. Sidebars cover conditional probability, orders of magnitude and Taleb's anti-fragility, which prefers to prepare for volatility than to predict it. The intelligence officer Vera Atkins and insurance companies are the examples of probability used well.

Inversion

Inversion means thinking backwards from the end, or asking what you want to avoid. As Parrish puts it, "Avoiding stupidity is easier than seeking brilliance." The chapter credits the mathematician Jacobi with the advice to invert, always invert, and uses Sherlock Holmes recovering a photograph. It goes on to personal finance and Kurt Lewin's force field analysis, which asks about the forces that block change as well as those that support it. The CCTV system invented by Marie Van Brittan Brown is the example of innovation by working backwards.

Occam's Razor

Simpler explanations are more likely to be true than complicated ones. The chapter says to prefer the explanation with the fewest moving parts when two have equal power, and it describes the habit of leaping to the worst case when a partner is late or a child's height is off. Examples cover Hume on miracles, the evidence for dark matter, doctors who think of horses and not zebras, and Louis Gerstner's refusal to give IBM a grand vision. It also warns that some things are not simple, and that the razor is a tendency and not a law.

Hanlon's Razor

Do not attribute to malice what is better explained by stupidity or error. Parrish uses road rage and the Linda problem to show how our minds prefer vivid stories, and tells how the Roman emperor Honorius read treachery into his general's actions, which helped end the western empire. He also tells of the Soviet officer Vasili Arkhipov, who assumed no hostile intent during the Cuban missile crisis. A sidebar names the devil fallacy, the habit of putting conditions down to villainy, and he reminds the reader that the razor is not an excuse to ignore real bad actors.

Supporting Ideas: Falsifiability, Necessity and Sufficiency, Causation vs. Correlation

Three shorter pieces sit between chapters. Falsifiability, after Karl Popper, says a good theory must be able to be proven wrong. Necessity and sufficiency separates the conditions needed for success from those that guarantee it, and notes that the gap is often luck. The last piece separates things that happen together from things that cause each other, using correlation coefficients as a guide.

What to do with it

• Pick one model a week and apply it to a live decision, writing down what it showed.
• Next time you read a report or dashboard, list two things it leaves out about the real situation.
• Write the edges of your circle of competence and who covers what lies outside it.
• For a pending decision, list second-order effects and run an inversion by asking how it could fail.
• When a problem looks like someone's fault, name two non-malicious explanations before you act.