Behavioral economics

Intuition [Beginner]

Standard economics assumes people are rational: they have consistent preferences, weigh all available information, and always choose the option that maximises their well-being. Behavioral economics asks a simple question: what if they do not?

Consider these observations. People drive across town to save $5 on a $15 item but will not drive the same distance to save $5 on a $500 item. The saving is $5 either way -- a rational agent should make the same choice. People keep subscriptions they never use because cancelling feels like losing something. People overestimate the likelihood of dramatic events (plane crashes) and underestimate common ones (car accidents). People will walk past a $20 bill on the ground if picking it up feels socially awkward.

These are not random errors. They are systematic -- predictable, repeatable patterns that violate the predictions of rational choice theory. Behavioral economics studies these patterns and asks what they mean for how markets work, how policy should be designed, and how people actually make decisions.

The field does not reject rationality entirely. Instead, it proposes that human decision-making is boundedly rational: people try to make good choices but face cognitive limits, time pressure, and emotional influences that lead to predictable deviations from the rational ideal.

Visual [Beginner]

Prospect Theory: Value Function

  Value
    |
    |    *
    |   *       Gains (concave --
    |  *         diminishing sensitivity)
    | *
  0 +---+---+---+--- Reference point
    |    *
    |     *   Losses (convex, and steeper
    |      *  than gains -- loss aversion)
    |       *
    |
       Losses     Gains

  Key features:
  1. Reference dependence: value measured from a reference point,
     not from absolute wealth.
  2. Loss aversion: losses feel roughly 2x worse than equivalent
     gains feel good.
  3. Diminishing sensitivity: the difference between $100 and $200
     feels larger than between $1,100 and $1,200.


Cognitive Bias Map

  Anchoring          Availability        Representativeness
  (first number      (vivid events       (stereotypes
  biases judgment)   seem more likely)   override statistics)

  All lead to systematic departures
  from rational prediction.

Worked Example [Beginner]

A store sells a jacket for $125 and a tablet for $25. A second store sells the same jacket for $120 and the same tablet for $30. Which store offers the better deal?

Rational answer: Both stores charge $150 total. They are equally good deals.

Behavioral reality: Many people prefer the first store. Why? Framing effects and the principle of diminishing sensitivity. A $5 saving on a $25 item (20% discount) feels more significant than a $5 saving on a $125 item (4% discount), even though the absolute saving is identical.

This is an example of proportional thinking -- people evaluate changes relative to a reference point rather than in absolute terms. Standard economics predicts that only the absolute saving ($5) matters; behavioral economics predicts, correctly, that the proportional saving also influences decisions.

Another example: a restaurant menu lists a $100 steak next to a $50 chicken dish. The chicken seems reasonably priced. Remove the steak and the chicken suddenly seems expensive. The steak serves as an anchor -- a reference point that shifts perceptions of value. This is not rational (the chicken's quality and cost have not changed) but it is a well-documented and exploitable pattern.

Check Your Understanding [Beginner]

Formal Definition [Intermediate+]

Bounded rationality

Herbert Simon (1955) proposed that decision-makers are satisficers rather than maximisers. Rather than evaluating all possible alternatives, they search until they find an option that exceeds a threshold (aspiration level):

$$\text{Choose }a\text{ such that }u(a)\ge \bar{u}$$

where $\bar{u}$ is the aspiration level. The aspiration level adjusts based on experience: if options above $\bar{u}$ are easily found, $\bar{u}$ rises; if not, $\bar{u}$ falls.

Prospect theory

Kahneman and Tversky (1979) proposed prospect theory as a descriptive alternative to expected utility theory. Decisions are evaluated relative to a reference point $r$, using a value function $v(\cdot )$ and a probability weighting function $\pi (\cdot )$.

The value function has three properties:

1. Reference dependence: $v(x)$ is defined on gains and losses relative to $r$, not on absolute wealth.
2. Loss aversion: $v(-x)=-\lambda v(x)$ where $\lambda \approx 2.25$ (losses loom larger than gains).
3. Diminishing sensitivity: $v^{\prime \prime }(x)<0$ for $x>0$ (concave for gains), $v^{\prime \prime }(x)<0$ for $x<0$ (convex for losses, since $v$ is steeper for losses).

A common parametric form is:

$$v(x)=\{\begin{array}{ll}{x}^{\alpha }& \text{if }x\ge 0\\ -\lambda (-x{)}^{\beta }& \text{if }x<0\end{array}$$

with $\alpha ,\beta \approx 0.88$ and $\lambda \approx 2.25$ (Tversky and Kahneman, 1992).

The probability weighting function $\pi (p)$ overweights small probabilities and underweights moderate-to-large probabilities:

$$\pi (p)=\frac{p^{\gamma }}{(p^{\gamma }+(1-p{)}^{\gamma }{)}^{1/\gamma }}$$

with $\gamma \approx 0.61$. This explains why people both buy lottery tickets (overweighting tiny probabilities of winning) and buy insurance (overweighting tiny probabilities of loss).

Nudge theory

Thaler and Sunstein (2008) define a nudge as any aspect of the choice architecture that predictably alters behaviour without forbidding options or significantly changing economic incentives. Formally, a nudge changes the default or the framing of a choice while preserving freedom of choice.

Examples include automatic enrolment in retirement savings plans (opt-out rather than opt-in), placing healthy food at eye level in cafeterias, and pre-filling tax forms with known information.

Key Concepts [Intermediate+]

• Bounded rationality: People have limited cognitive resources and time, so they use heuristics (mental shortcuts) rather than exhaustive optimisation.
• Heuristics: Fast, frugal decision rules (e.g., "choose the brand you recognise") that work well in most situations but can lead to systematic errors.
• Anchoring: The tendency for an initial number or piece of information to influence subsequent judgments, even when the anchor is irrelevant.
• Availability heuristic: Estimating the probability of an event based on how easily examples come to mind. Leads to overestimation of vivid, recent, or media-covered events.
• Representativeness heuristic: Judging probability based on how similar an event is to a prototype or stereotype, ignoring base rates.
• Loss aversion: Losses feel roughly twice as painful as equivalent gains feel pleasurable. People reject fair gambles that offer equal expected gains and losses.
• Prospect theory: A descriptive theory of choice under risk that replaces expected utility with a reference-dependent value function and probability weighting.
• Framing effects: The same objective choice presented differently (as a gain vs. a loss, or with different defaults) leads to different decisions.
• Nudge: A change in choice architecture that steers behaviour without restricting options. Relies on defaults, framing, and social norms.
• Present bias: Overweighting immediate costs and benefits relative to future ones. Explains procrastination and undersaving for retirement.

Exercise 1. A doctor tells patients that a surgery has a "90% survival rate" versus telling them it has a "10% mortality rate." The procedures are identical, but patients choose the surgery more often in the first case. Identify the behavioral mechanism and explain why standard economic theory predicts no difference.

Exercise 2. A person is given a mug and then asked the minimum price at which they would sell it. Another person is asked the maximum price they would pay for the same mug. Standard theory predicts these prices should be approximately equal. In experiments, selling prices are roughly twice buying prices. Explain this result using behavioral concepts.

Academic Perspectives [Master]

Behavioral economics (mainstream)

The behavioral economics program, led by Kahneman, Tversky, Thaler, and others, has been incorporated into mainstream economics over the past four decades. The approach is primarily descriptive: it documents systematic deviations from rational choice and develops models (prospect theory, quasi-hyperbolic discounting, models of fairness) that better predict observed behaviour.

Behavioural insights have been applied to policy through nudge units (the UK's Behavioural Insights Team, the US Social and Behavioral Sciences Team) and to finance (behavioral finance explains asset bubbles, momentum, and the equity premium puzzle). The 2017 Nobel Prize to Richard Thaler signalled the field's full acceptance.

Neoclassical response

Neoclassical economists offer several defences of the rational agent model:

1. Market discipline: In competitive markets, irrational agents are eliminated (they lose money to rational arbitrageurs). Individual irrationality does not necessarily imply market-level irrationality.
2. As-if rationality: Friedman (1953) argued that agents behave as if they were rational because those who consistently deviate from rationality are driven out of the market. The realism of assumptions does not matter; only predictive accuracy matters.
3. Learning: People may make systematic errors initially but learn over time, converging to rational behaviour with experience.
4. Revealed preference: If people choose something, they must (by definition) prefer it. Behavioural economists' claim that people make "mistakes" imposes the researcher's preferences on the decision-maker.

Austrian school

Austrian economists share behavioral economics' scepticism of the rational agent model but reject the behavioral alternative for different reasons. Kirzner and others argue that the rational/irrational dichotomy is itself misleading. Real economic decisions are made under genuine uncertainty (not merely risk with known probabilities), and the relevant question is not whether decisions are "rational" but whether the market process discovers and corrects errors.

Austrians also criticise nudge theory as paternalistic. If individuals are irrational, who is rational enough to design the nudges? The nudge designer suffers from the same cognitive biases as everyone else. Hayek's knowledge problem applies: central planners (including choice architects) cannot possess the dispersed, tacit knowledge that individuals hold about their own circumstances and preferences.

Marxian and critical perspectives

Some scholars argue that behavioral economics individualises what are structural problems. People do not undersave because of present bias; they undersave because wages have stagnated, housing costs have risen, and the financial system extracts wealth. People do not make "irrational" health choices because of cognitive biases; they make constrained choices shaped by poverty, food deserts, and occupational hazards.

From this perspective, the nudge approach depoliticises inequality and exploitation by framing them as problems of individual cognition rather than structural power. Changing the default on a retirement savings form does not address the underlying question of why workers bear all the investment risk in a defined-contribution system.

Ecological rationality

Gerd Gigerenzer and colleagues propose an alternative framework: ecological rationality. Heuristics are not "biases" or "errors" but adaptive tools that evolved to work well in specific environments. The availability heuristic, for instance, is an effective guide to frequency in environments where memorable events are actually more common. Errors arise only when the heuristic is applied in environments that differ from those in which it evolved.

This reframing challenges the behavioral economics narrative. Rather than cataloguing human irrationality, ecological rationality asks: in what environments do simple heuristics outperform complex optimisation? The answer, surprisingly often, is that heuristics are more robust, faster, and nearly as accurate as "rational" models, especially under conditions of uncertainty and limited data.

Historical Context [Master]

• Simon (1955): Herbert Simon introduced bounded rationality and satisficing, challenging the assumption of unlimited computational capacity. Nobel Prize, 1978.
• Allais (1953): Maurice Allais demonstrated systematic violations of expected utility theory (the Allais paradox), showing that people's choices under risk were inconsistent with the independence axiom. This was the first major empirical challenge to rational choice under risk.
• Tversky and Kahneman (1974): "Judgment under Uncertainty: Heuristics and Biases" introduced the three classic heuristics (availability, representativeness, anchoring) and launched the heuristics-and-biases research program.
• Kahneman and Tversky (1979): "Prospect Theory: An Analysis of Decision under Risk" provided the first comprehensive descriptive alternative to expected utility theory. The most cited paper in the history of economics.
• Thaler (1980, 1985): Richard Thaler documented mental accounting, the endowment effect, and other anomalies that challenged standard economic assumptions. His work built the bridge between psychology and economics.
• Laibson (1997): David Laibson formalised quasi-hyperbolic discounting ($\beta$-$\delta$ preferences), modelling present bias and its implications for savings behaviour.
• Thaler and Sunstein (2008): Nudge popularised libertarian paternalism and choice architecture, influencing governments worldwide to create behavioural insights teams.
• Nobel Prizes: Herbert Simon (1978), Daniel Kahneman (2002), Richard Thaler (2017). The progression of prizes tracks the field's growing acceptance within mainstream economics.

Bibliography [Master]

• Allais, M. (1953). Le comportement de l'homme rationnel devant le risque. Econometrica, 21(4), 503-546.
• Friedman, M. (1953). The methodology of positive economics. In Essays in Positive Economics. University of Chicago Press.
• Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. Viking.
• Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
• Laibson, D. (1997). Golden eggs and hyperbolic discounting. Quarterly Journal of Economics, 112(2), 443-478.
• Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics, 69(1), 99-118.
• Thaler, R. H. (2015). Misbehaving: The Making of Behavioral Economics. W. W. Norton.
• Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. Yale University Press.
• Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. Science, 185(4157), 1124-1131.