Behavioral Economics: How People Actually Choose

You sign up for a gym at 8 a.m. in January, full of resolve, and pay monthly for a year. You go four times. A shop shows a jacket "was £200, now £120" and it suddenly feels like a bargain, though you had no idea what it was worth a minute ago. A company enrolls new staff in a pension plan automatically, and far more people end up saving, although nothing about their finances changed.

None of this is irrational in the sense of being random. It is irrational in a predictable way, and predictable is what an economist can use. Behavioral economics collects those regular departures from the textbook chooser and builds them into models. It is also a field that has been through a hard decade, in which many headline results were re-tested and some did not survive. This piece teaches both: the ideas, and the report card.

Bounded rationality: shortcuts that usually work

The textbook consumer weighs every option, knows her own tastes and never makes arithmetic mistakes. Herbert Simon argued in the 1950s that this ignores the obvious: minds are limited, time is short, information is incomplete. People do not optimise, they satisfice: search until something is good enough, then stop. He called this bounded rationality. Daniel Kahneman and Amos Tversky later catalogued the rules of thumb, or heuristics, that people use, and the systematic errors each one produces. Judging how likely something is by how easily examples come to mind (availability), by how much it resembles a stereotype (representativeness), or by starting from a number you were handed (anchoring), all work most of the time, and all fail in patterned ways. Kahneman received the 2002 economics Nobel prize for this work.

A worked example, from the "cognitive reflection" literature: a bat and a ball cost £1.10 together; the bat costs £1.00 more than the ball. How much is the ball? The quick answer is 10p. Check it: ball 10p, bat £1.10, total £1.20. If the ball costs x, the bat costs x + 1.00, so 2x + 1.00 = 1.10 and x = 5p. The wrong answer comes fast and feels right; the right one takes a second look. Biases are mostly this: a fast process that is usually fine, with no alarm bell when it is not. The rest of the piece is a tour of the best-studied ones, with a growing sense of how much to trust each.

Prospect theory: gains, losses and the kink

Kahneman and Tversky's 1979 paper in Econometrica proposed prospect theory as a description of how people value risky outcomes. Three ingredients. First, people judge outcomes as gains or losses from a reference point (usually where they are now), not as totals of wealth. Second, diminishing sensitivity: the difference between winning 0 and 100 feels larger than between winning 1,000 and 1,100. Third, loss aversion: a loss hurts more than the same gain pleases. Their 1992 estimates were roughly α ≈ 0.88 for curvature and λ ≈ 2.25 for loss aversion.

Without JavaScript: with λ = 2.25 and α = 0.88, a 50:50 chance to lose 100 is only worth taking if the win is at least about 251; with λ = 2 and α = 1 you need exactly 200.

At the default settings a coin flip that risks 100 and offers 150 has a positive average (+25 in money) but a negative felt value. Slide the win up to about 250 and it flips. People often refuse such bets, and prospect theory says why. How robust is loss aversion itself? That is an unfinished argument, taken up at the end.

Anchoring and framing: the same facts, different pull

In the best-known anchoring experiment, Tversky and Kahneman (1974) spun a wheel that stopped on 10 or 65, then asked people what percentage of United Nations members are African countries. People who saw 10 gave a typical answer near 25%; those who saw 65 answered near 45%. A random number pulled the estimate. The sliding tool below interpolates between those two reported medians (an illustration of the size of the pull, not data beyond them).

The anchoring index is the shift in estimates divided by the shift in anchors: (45 − 25) ÷ (65 − 10) ≈ 0.36. Anchoring is one of the sturdier findings in the field, though a few studies with obviously random anchors have not found it. Framing is the cousin: Tversky and Kahneman's 1981 "Asian disease" problem describes a programme for 600 people at risk either as "200 will be saved" or as "400 will die", and both describe the same outcome. In the original, a majority took the sure option when it was framed as lives saved and a majority took the gamble when framed as deaths. Framing effects have been re-tested widely, and the direction usually holds; the size depends heavily on the wording and the people asked.

Present bias: today versus later

Standard theory discounts the future at a steady rate, so your preference between "£100 on day d" and "£110 on day d + 7" stays the same as the days pass. Real people do not. Richard Thaler found in 1981 that people would be indifferent between $15 now and $30 in three months, $60 in a year or $100 in three years, implying discount rates that fall steeply with the delay. George Ainslie found many people preferred $50 now to $100 in six months but $100 in nine months to $50 in three: the same choice, six months further away, reverses. David Laibson (1997) captured this in the quasi-hyperbolic model: everything after today gets an extra one-off discount β below 1.

With β = 0.7 your plan today is to wait for the £110 whenever the smaller prize is a day or more away. When the day arrives and £100 is available now, you take it, against your own plan. That plan-then-break pattern is the gym membership, the unfinished diet, the unopened savings account. People who know they are like this buy commitment devices: a locked savings account, a bet with a friend, deleting the app. Set β = 1 and the reversal vanishes.

Defaults and nudges: design the easy path

If people are inattentive and slightly present-biased, whatever happens when they do nothing matters enormously. Madrian and Shea (2001) studied a large US company that switched its 401(k) pension plan to automatic enrolment, with the plan's economic terms unchanged: participation was significantly higher under automatic enrolment, and many employees stayed at the default contribution rate and fund. National organ-donation rates tell a similar story: about 12% consent in opt-in Germany against 99.98% in opt-out Austria is the oft-cited contrast, though it mixes in other causes, and Spain, with the highest donor rate, credits its transplant coordination more than its law.

The toy model shows the logic: the less attentive the population, the more the default decides, and the fewer people are choosing at all. This is the idea behind the nudge, a term Richard Thaler and Cass Sunstein made famous in their 2008 book: change the "choice architecture" without forbidding anything. It is also the cue for a warning. Whether nudges work at scale is disputed (report card below), and the ethics of steering people are argued too: critics worry about manipulation, supporters reply that every choice has some default, so someone has to set it.

Social preferences: the ultimatum game

Standard theory predicts people care only about their own payoff. In the ultimatum game (Güth, Schmittberger and Schwarze, 1982), a proposer splits 100 with a responder who can accept or reject; on rejection both get nothing. A pure money-maximiser would accept 1, so the proposer would offer 1. Real responders do not. Across 37 studies, proposers typically offered about 40%, and offers below about 30% were often rejected, so responders pay to punish what they see as unfair. Fifteen small-scale societies studied by Henrich and colleagues showed that what counts as fair differs across cultures, so the "typical" figures belong to the places where most experiments have been run.

Set the minimum and the spread to near zero (a textbook responder): the best offer collapses to about 3. Put them near what the studies describe and the best offer jumps to about 39, because a stingy offer risks everything. A proposer who offers fairly may be motivated by fairness or by fear of rejection, and the game alone cannot say which.

What held up, what shrank, what failed

Behavioral economics grew out of psychology, and psychology went through a replication crisis. In 2015 the Open Science Collaboration re-ran 100 published psychology studies and found about 36% replicated successfully by their main criterion. Economics did better but not perfectly: a 2016 project re-ran 18 laboratory experiments from top economics journals, and 11 (about 61%) replicated; a 2018 project re-ran 21 social-science papers from Nature and Science, and 13 (about 62%) replicated, typically with smaller effects. The report card below sorts the findings used in this piece and a few famous neighbours. Use the buttons to filter it.

A few lessons. Directions hold more often than sizes: defaults, anchors and present bias point the way the theory says, but their effects in the field are often smaller than the first lab study. Big, flashy, one-off findings are the riskiest: ego depletion and social priming were built on small samples and clever stories. Not every "bias" is one idea: loss aversion, the endowment effect and the status quo bias may be one force or three, and experts differ. And published averages exaggerate, for the reason shown in Econometrics (34/44): significant results survive the journal filter, and lucky overestimates are the ones that pass it.

What a graduate just sees

• People use shortcuts that usually work and sometimes fail in patterned ways; the pattern is what makes it a science.
• Outcomes are judged from a reference point, and losses weigh more than equal gains, probably about twice, but the figure depends on context.
• A number you are handed pulls your estimate toward it, even when it is irrelevant.
• The same facts framed as gains or losses can flip a choice.
• Present bias makes plans and actions disagree; commitment devices exist because people know it.
• The default is a choice someone made for you; when people are inattentive, it decides.
• People will pay to punish unfairness, and fairness norms differ across societies.
• Ask of any famous finding: replicated? how big? in whom? Direction survives more often than size.

Where it shows up next

• Consumer Behavior (34/58): reference prices, anchoring on list prices and "was/now" framing are everyday marketing.
• Pricing (34/61): decoys, round numbers and subscription defaults rely on these effects, and the replication record tells you which to trust.
• Public Economics (34/49): automatic enrolment and opt-out donation are policy tools, with the nudge debate attached.
• Strategy & Competition (34/54) and Game Theory (34/45): the ultimatum game is a game theory result that real people do not follow.
• In the world: pension design, fundraising, health messaging, app design and every "limited time offer".

If you want to go deeper

• 📘 Textbook. Nick Wilkinson and Matthias Klaes, An Introduction to Behavioral Economics (Palgrave). A standard undergraduate text.
• 📘 Reference. Colin Camerer, George Loewenstein and Matthew Rabin (eds.), Advances in Behavioral Economics (Princeton, 2004), for the research papers behind the field.
• 📘 Gentler. Richard Thaler, Misbehaving: The Making of Behavioral Economics (Norton, 2015), and Daniel Kahneman, Thinking, Fast and Slow (2011). Read both with the report card in mind, as some of the studies in the second have since been questioned.
• 🌐 Free course. MIT OpenCourseWare 14.13 Psychology and Economics (Spring 2020, Prof. Frank Schilbach).