By Jason Hreha

Glossary

Behavioral economics

Reviewed by Jason Hreha

Updated Published 13 min read

What is behavioral economics?

Behavioral economics studies how people make economic decisions by bringing psychological evidence into economics. It examines how attention, beliefs, emotions, concern for others and the way options are presented affect what people buy, how much they save, whether they give money away and which offers they accept. It also studies what those decisions mean for businesses, markets and public policy.

Consider someone choosing a monthly subscription. The price and the service matter. But so might whether renewal happens automatically, how clearly the future charge is displayed, and whether the person remembers to cancel. Behavioral economics asks how these features affect the decision, alongside income, prices and the available alternatives.

The central question is how people actually think and choose, and what that means for spending, saving and other economic activity. To find out, researchers study purchase records, run experiments, test different enrollment processes and build mathematical models to predict choices. The field is broader than a collection of thinking errors, known as cognitive biases, or a set of techniques for changing behavior.

Behavioral economics, economics and psychology

Economics studies how people and institutions use limited resources, respond to rewards and costs, and interact. An economic model is a simplified explanation of those decisions that researchers use to make predictions. For example, a model might assume that a shopper compares every available option, knows which they prefer, and chooses the one they value most within their budget.

Behavioral economists investigate what happens when those assumptions do not fit the decision. Perhaps the shopper overlooks a fee and compares only the advertised prices. A model that assumes they consider the full price may then predict the wrong purchase. Other models examine how choices change when a cost must be paid now rather than later, or when someone cares about whether a payment is fair as well as how much they receive.

This does not mean that conventional economics assumes everyone is selfish, knows everything or never makes a mistake. Economic models differ. Rational choice means choosing consistently according to what matters to you, given the options and limits you face. It can include concern for other people: someone who gives money away may be choosing what matters most to them, rather than making a mistake. Economists also study what happens when buyers lack information, must pay to find it, or cannot easily switch sellers. The useful comparison is between specific explanations of a decision, not between an imaginary perfect calculator and an equally imaginary irrational human.

Psychology studies a much wider range of mental processes and behavior, including memory, perception, development and personality. Behavioral economics draws on that work to address economic questions. Behavioral science covers the wider group of fields that study behavior. Behavioral finance applies related ideas to financial decisions and markets; experimental economics uses experiments to study economic questions. An experiment can test either a behavioral explanation or a conventional one.

How behavioral economics developed

Several lines of research shaped modern behavioral economics.

Herbert Simon and the limits of calculation

In the 1950s, Herbert Simon challenged models that required people to evaluate more information and alternatives than they could realistically handle. He called attention to bounded rationality: people make decisions with limited information, time and ability to compare options. Imagine looking for an apartment before your current lease expires. You can keep searching for the best possible place, or choose one that meets your needs and budget. Satisficing means using that second approach: looking for an acceptable option without first proving that no better one exists. Simon’s 1955 paper on rational choice helped establish this research direction.

Kahneman and Tversky: judgment and decisions under risk

In the 1970s, psychologists Daniel Kahneman and Amos Tversky studied how people judge uncertain events and choose between risky options. Their 1974 paper on heuristics and biases examined shortcuts in judgment. Their 1979 paper on prospect theory proposed an explanation for choices involving risk. It described how people judge a gain or loss against what they already have or expect, and how the chance of an outcome affects their choice. Under the theory, even a tiny chance of winning can have more influence on a decision than the numerical odds alone might suggest.

These were distinct contributions: one concerned judgments such as how likely an event is; the other concerned choices such as whether to take a guaranteed payment or a gamble. They helped bring psychological findings into economic debates about decision making. Kahneman received the 2002 economics prize for integrating psychological research into economics (Nobel Prize Outreach, n.d.).

Thaler and the move into economic applications

Richard Thaler connected psychological ideas with economic questions about spending, saving, fairness and self-control. His work included mental accounting, or the ways people organize and evaluate money in separate mental categories. The Nobel committee’s account of his work describes how limits on reasoning, concern for other people and problems of self-control affect economic decisions (Royal Swedish Academy of Sciences, 2017). He received the economics prize in 2017.

Thaler and Cass Sunstein’s 2008 book Nudge brought one applied approach to a much wider audience: changing how choices are arranged. Nudging became a prominent public face of behavioral economics, but it is only one part of the field.

Core ideas in behavioral economics

There is no single official list of principles. A useful way to organize the field is to ask three questions: What do people value? What do they believe? How do they reach a decision? DellaVigna’s (2009) review of field evidence uses the related categories of preferences, beliefs and decision making.

Bounded rationality and limited attention

People have limited time and mental capacity. They cannot investigate every alternative or keep every relevant detail in mind. That is the basic idea of bounded rationality. Limited attention is one part of it: even when information is available, someone may not notice it.

Imagine comparing internet plans. You might examine the monthly price closely while barely noticing the installation fee or the price after the introductory period. Those costs may be on the page, yet you could make your choice mainly from the advertised monthly price. Would making the later price easier to notice change which plan people choose?

Heuristics, biases and beliefs

A heuristic is a simplified way of making a judgment. Judging how common something is by how easily examples come to mind is called the availability heuristic. That shortcut can help when the examples you remember reflect how often something happens. It can mislead when an unusual event receives so much news coverage that it comes to mind more easily than everyday events.

A cognitive bias is a recurring pattern of error in judgment. To call an answer an error, researchers need a way to judge it: they might compare people’s estimates of how often an event occurs with records of its actual frequency. A heuristic is the shortcut someone uses; a bias is an error that may result. The two are not synonyms, because a shortcut can also produce a good answer.

Anchoring and overconfidence are two examples of bias. Anchoring means that a starting number influences a later estimate. For example, a car’s asking price can pull your estimate of its value toward that price. Overconfidence means being more certain of your answers than your record of correct answers supports. These ideas concern how people form beliefs and estimates, not just what they prefer.

Reference points, loss aversion and prospect theory

The same payment can seem different depending on what someone expected. Imagine receiving $100. If you expected nothing, it may feel like a gain. If you expected $150, it may feel like a shortfall. What you already have or expected to receive supplies a reference point: the starting point against which you judge the result.

Loss aversion means a loss can weigh more heavily than an equal-sized gain, measured from the same starting point. Losing $100 and gaining $100 need not carry equal weight in a decision. This differs from risk aversion, or preferring a more certain outcome over a risky one with the same average payoff. For example, someone might prefer a guaranteed $50 to an even chance of receiving $100 or nothing. Both options have the same average payoff, but only one guarantees a payment. Measured from zero, both options are gains, so choosing the certain $50 does not, by itself, show that the person gives more weight to losses than gains.

Prospect theory brings several ideas together. People judge gains and losses from a reference point, and another gain or loss may make less difference as the amounts grow. Imagine comparing a gain of $100 with $200, then $1,100 with $1,200. The extra $100 may feel less important in the second comparison. The theory also addresses how people respond to odds. Going from no chance of winning to a 1% chance, for example, can matter more to a choice than going from a 10% chance to an 11% chance. Both changes are one percentage point. This is one form of probability weighting: the odds can influence a decision in ways that do not simply track their numerical differences. A person can understand the odds and still respond differently to the two changes. For more detail, see loss aversion and the original prospect-theory paper.

Present bias and decisions over time

Intertemporal choice means choosing between outcomes at different times, such as a smaller payment today or a larger one next month. Present bias is more specific: a cost or benefit becomes especially important when it is immediate. That can make someone reverse a plan as the time to act arrives.

For example, someone might plan to do an unpleasant task on Saturday to enjoy Sunday free. On Saturday, avoiding the task now becomes more attractive, and they put it off until Sunday. The issue is the reversal as “later” becomes “now,” not simply that the person values leisure. Ericson and Laibson’s (2018) research on intertemporal choice examines these patterns and how people compare present and future outcomes.

Mental accounting

People sometimes treat money differently according to its source or assigned purpose. Imagine someone who puts regular income toward bills but treats a tax refund as money for a trip, even though either sum could pay the same bill. Mental accounting describes how people categorize and evaluate money in these ways. Thaler’s (1999) review of mental accounting develops the idea.

Social preferences and fairness

People can care about what happens to others, whether a payment is fair, and whether to repay a kindness or punish mistreatment. Social preferences describe these concerns about other people in economic decisions. Imagine accepting a smaller share of a payment so that someone else receives a fairer share. That can reflect a real preference for fairness, rather than a failure to understand which option pays you more.

Fehr and Schmidt’s (1999) model of fairness, for example, examines how concern about unequal payoffs can affect a decision. In that model, someone might give up income to avoid an unequal split. The choice need not be a mistake.

Framing and choice architecture

Framing concerns how a choice is described or presented. Imagine an annual subscription that costs $120, paid once a year. One description says “$120 per year”; another says “equivalent to $10 per month.” The price and payment schedule are unchanged, but the wording highlights different amounts. Choice architecture covers more than wording: it includes which options appear first, what information is shown and what happens if someone makes no active selection. That last feature is the default.

In Tversky and Kahneman’s (1981) work on framing, two descriptions of equivalent outcomes led people to make different choices. A default changes something else. Requiring a form to join a plan, or enrolling people automatically unless they opt out, changes what people must do, not how the choice is described. Neither framing nor a default is the same as loss aversion, which concerns how people value a gain or loss.

Three examples of the questions it asks

The concepts become easier to distinguish when you apply them to different questions. These are illustrations, not reports of experiments.

Situation A behavioral economics question Relevant ideas
A customer focuses on a sale price and gives little thought to later fees. Which parts of the total price do they notice? Limited attention, salience (what stands out)
Someone plans to complete a form next week, then postpones it again. Does avoiding the effort become more appealing when it is time to act? Present bias, intertemporal choice
A person accepts a smaller share so someone else can receive a fairer share. How much do they care about what each person receives? Social preferences

Each question suggests a comparison a researcher could investigate. But seeing the choice is not enough to know why someone made it. Someone may postpone a form because required information is missing, for instance, rather than because they give extra weight to avoiding an immediate effort. Researchers need to compare those explanations before deciding which fits.

How researchers study economic decisions

Behavioral economists use models to state their assumptions and predict what people will choose. In laboratory experiments, they can change one feature of a decision, such as the information shown, while keeping other features the same. Field experiments test changes in everyday settings, such as a shop, workplace or government service. Researchers also study purchase and enrollment records, ask people questions in surveys, and examine what happens when policies change.

The method needs to fit the question. Purchase records can show what people bought, but may not tell us why. An experiment could randomly assign customers to receive a reminder or no reminder, then compare how many complete a purchase. Random assignment helps separate the reminder’s effect from differences between the people receiving it. Even then, the result may not tell us how the reminder worked. Did it bring the purchase back to mind, make the instructions easier to find, or both? That is a question about the psychological mechanism: the process that produced the change.

Researchers also distinguish three kinds of question. A descriptive question asks what people do and how they decide: how many customers let a subscription renew automatically? A normative question asks what would make the outcome better: was keeping the subscription in the customer’s interest? An applied question asks whether a change helps achieve that aim: would a reminder before renewal help customers keep subscriptions they want and cancel those they do not? More renewals might benefit the seller without benefiting every customer. That is why describing a result and judging its value are separate tasks.

Where behavioral economics is used

Researchers use behavioral economics to study how people respond to prices and fees, why employees join retirement plans, how taxpayers respond to notices, and how people make decisions about education, health care and work. Practitioners might simplify a form, change the order of options, make a cost more visible, send a reminder, or change a default. Researchers also examine how firms respond to customers’ decisions.

A nudge, in the sense popularized by Thaler and Sunstein, changes how a choice is presented or arranged. It keeps options available, does not substantially change economic incentives (the financial costs and rewards of the choice) and is easy and cheap to avoid (Thaler & Sunstein, 2008; OECD, 2019). A reminder is one example. Offering a large subsidy is different because it makes an option financially more attractive; a ban removes an option altogether. A policy may combine these approaches, but calling them all nudges would hide what each one changes. The books guide provides a starting point for reading Nudge in that context.

Example: automatic retirement enrollment

In an opt-in retirement plan, an employee must sign up to join. Under automatic enrollment, the employer starts putting a specified share of the employee’s pay into the plan unless the employee chooses otherwise. Employees can participate or stay out under either system. What changes is whether they must act to join or to opt out.

Brigitte Madrian and Dennis Shea (2001) studied a large US employer that switched to automatic enrollment in its 401(k) plan. Among employees with three to fifteen months at the company, participation rose from about 37% to about 86%. Many employees also stayed with the plan’s default contribution rate and investment allocation: 3% of pay, invested in a money-market fund. The researchers compared employee groups hired before and after the policy change, rather than randomly assigning employees to the two systems. The study drew attention to defaults because so many employees kept the initial settings even though they could change them.

Enrollment is a meaningful outcome: people join a retirement plan, and money can go into their accounts. But that alone does not show that they actively chose to save or developed a savings habit. Enrollment alone also cannot show whether they became wealthier overall. Their net wealth (what they own minus what they owe) depends on their other savings and debts too. The guide to behavioral economics examples examines related retirement-enrollment research and these distinctions in more detail.

Criticisms and practical limits

Understanding the field does not require accepting its strongest practical promises. A study may explain why people made a particular choice. A practitioner needs to answer another question: will changing something about that choice reliably produce a useful result for the people they are trying to help?

Does the finding hold up?

Replication asks whether researchers can obtain a result again in a similar test. Generalization asks whether it also holds for different people, tasks or settings. A reminder could work again in the original workplace yet fail somewhere that employees have no time to act on it. So a result that replicates has not yet shown where else it applies. Even a famous study or influential theory needs both checks. And one failed result does not mean that every finding in behavioral economics is false.

Is the effect large enough to matter?

Stefano DellaVigna and Elizabeth Linos (2022) compared 126 trials run by two US government teams that test nudges with 26 trials published in academic journals. The studies counted actions such as enrolling in a program or making a payment. Across the government trials, the average increase in the share of people taking the measured action was 1.4 percentage points. In the academic sample, it was 8.7 points. A percentage point is the difference between two percentages: going from 20% to 21% is an increase of one point. These were different sets of trials, not the same interventions repeated at a larger scale. The comparison gives practitioners a reason to question plans built around impressive published academic results; it does not mean that every intervention has the same effect.

In Bad News for Nudges, Hreha argues that published nudge studies can create unrealistic expectations for real programs. He points to publication bias as one reason: studies with positive results are more likely to be published than studies with disappointing results. A plan based only on published successes may therefore expect more change than the program delivers.

None of this makes a small effect worthless. A small effect may be worth pursuing if the change is cheap and reaches many people. It may also be less useful than improving a product, lowering a price or changing a service. The question is whether the gain justifies the cost, including what else could have been achieved with the same time and money.

What actually changed?

A policy can produce a useful result without requiring someone to take a new action. Automatic enrollment, for example, can make someone a plan member and put money into their account without requiring them to fill in a form. That is different from someone completing a form or repeatedly deciding how much to save. This site’s critique of default nudges challenges the use of automatic enrollment as evidence that people have learned to act differently. The money transferred can still have value, but that value does not prove the stronger claim about learning.

A finding about choice is not a recipe for changing it

Suppose customers keep abandoning a form. Calling this “present bias” has not yet told you what to change. Perhaps the form asks for a document customers do not have. Perhaps the service costs too much. Perhaps they simply do not want it.

Behavioral economics can suggest possible explanations for a decision. Someone proposing a change still needs evidence that it addresses the actual obstacle and produces a worthwhile result. If customers cannot supply a document, making it easier to submit another form of proof may be more useful than a reminder. The name of a bias does not tell you which problem you have, or which solution will work.

Applying the evidence

Start with what people are trying to do and where they get stuck. If the obstacle might be a missing document or a high price, a reminder may not address it. Test a change that fits the problem, then measure what happened, what it cost and how long the effect lasted. The behavioral-design overview works through that process. The behavior-choice lesson addresses an earlier question: whether you have chosen a suitable behavior to pursue in the first place.

For Hreha’s broader argument about the field, read The Death of Behavioral Economics. The critical reading list collects further evidence and critiques. When reading them, keep asking what the idea explains, what the study actually found and what someone proposes doing with the result.

Where to go next

Use the behavioral economics field guide to explore individual concepts and the critical books guide to choose a longer introduction. The examples guide works through actual studies. The calculator for comparing two rates shows the size of a change in percentages and in people. Then try the three evidence exercises to practice deciding what a result does and does not show.

References