Module 9 — Behavioural and Experimental Microeconomics

Reference dependence, probability weighting, present bias, social preferences, limited attention, experiments, nudges, and welfare.

Module 9 — Behavioural and Experimental Microeconomics

Core question

Which departures from the standard choice model are stable enough to predict, test, and use in policy without explaining every surprise after it occurs?

Learning outcomes

You will be able to:

distinguish preference, belief, attention, and optimisation errors;
use reference dependence, present bias, and social-preference models;
design and critique a behavioural experiment;
interpret evidence on nudges at scale;
identify the welfare and autonomy problem in behavioural policy.

1. Diagnose the benchmark before replacing it

An observed choice can differ from a simple model because of:

Channel	Example	Evidence needed
preferences	dislike of losses or inequality	stable choice pattern across equivalent tasks
beliefs	overestimated rare risk	elicited expectations or information treatment
attention	fee or attribute not noticed	salience/eye-tracking/information intervention
computation	choice set too complex	simplification or decision-time evidence
constraint	liquidity or time prevents action	budget, eligibility, or access data
social meaning	action signals identity or norm	variation in observability/context

Calling all six “bias” prevents a causal diagnosis.

2. Reference dependence and prospect theory

Outcomes are evaluated as gains or losses relative to reference point r:

v(x-r)= \begin{cases} (x-r)^\alpha,&x\ge r,\\ -\lambda(r-x)^\beta,&x<r, \end{cases}

typically with λ>1. The model combines:

reference dependence;
greater sensitivity to losses than equal gains;
diminishing sensitivity away from the reference;
nonlinear decision weights on probabilities.

These are separate empirical components. Loss aversion alone does not imply overweighting rare probabilities.

Worked framing example

Gain frame: receive £10 for sure or take a 50–50 gamble paying £20 or £0.
Loss frame: first receive a £20 reference endowment, then lose £10 for sure or take a 50–50 gamble that loses £0 or £20.

Final payoffs are identical, yet the salient £20 reference can make the second description feel like losses and increase risk seeking. A valid test randomises framing while holding payoffs, information, and comprehension fixed.

3. Present bias and commitment

Quasi-hyperbolic preferences at time t are:

U_t=u_t+\beta\sum_{k=1}^{T-t}\delta^ku_{t+k}, \qquad 0<\beta\le1.

δ captures ordinary patience; β<1 adds extra discounting of every future period relative to now.

Worked procrastination

Studying now costs 6 units; tomorrow's exam benefit is 8. Let β=0.6 and δ=1:

-6+0.6(8)=-1.2.

The student postpones. Tomorrow, the same present-bias calculation can repeat. A commitment device—scheduled study group, website block, or deadline—can improve the earlier self's welfare if the person is sophisticated about future inconsistency.

The device can harm a time-consistent or liquidity-constrained person. Observed delay alone does not identify present bias.

In a two-person Fehr–Schmidt representation:

u_i=x_i -\alpha_i\max\{x_j-x_i,0\} -\beta_i\max\{x_i-x_j,0\},

with disadvantageous inequality often weighted more than advantageous inequality.

This can rationalise rejection of a low offer in an ultimatum game: the responder sacrifices money to avoid or punish an unequal outcome. Alternatives include reciprocity, norm enforcement, experimenter demand, reputation, and confusion. Different games or treatments are needed to separate them.

5. Defaults, salience, and friction

A default can change participation through:

effort saved by accepting it;
implied recommendation;
loss aversion around the status quo;
procrastination;
inattention.

The mechanism determines design. If effort is the problem, simplify active choice. If comprehension is the problem, improve explanation. If the default conveys endorsement, disclose how it was selected.

Sludge adds friction that benefits the designer at the chooser's expense—for example, one-click enrolment but a multi-page cancellation path.

6. Experimental design is part of the theory

A credible experiment specifies:

population and recruitment;
randomisation unit and treatment contrast;
incentive-compatible outcome;
manipulation/comprehension checks;
primary outcome and analysis plan;
attrition, interference, and multiple-testing handling;
treatment effect and uncertainty;
transport from experiment to policy setting.

Laboratory control strengthens mechanism tests; field scale strengthens realism. Neither automatically supplies the other.

7. Evidence case: nudges at scale

DellaVigna and Linos assemble 126 randomised trials run by two large US nudge units, covering more than 23 million individuals. Average take-up effects were about 1.4 percentage points, compared with 8.7 points in a sample of published academic nudge studies; the scaled effects remained statistically meaningful but were much smaller (2022).

The result teaches three lessons:

publication selection can favour large effects;
academics may optimise interventions more intensively;
scaled programmes cover broader populations and outcomes.

It does not show that every nudge is weak. Effects vary by behaviour and implementation, and a small percentage-point change can be cost-effective at low marginal cost.

8. Welfare when choices are inconsistent

Standard revealed-preference welfare says a chosen option is at least as good as an available alternative. With inattention, unstable reference points, or present bias, which choice reveals welfare—the immediate choice, informed choice, planned choice, or experienced outcome?

A behavioural policy appraisal should report:

whose preferences define welfare and at what time;
whether information or choice is restricted;
heterogeneity and opt-out cost;
distribution of errors and benefits;
manipulation risk and designer incentives;
learning, persistence, and long-run response.

An intervention that helps distinguish mechanisms is more valuable than one that merely moves an outcome. Active choice versus default, information versus simplification, and immediate versus delayed effects can reveal why behaviour changed.

9. From anomaly to cumulative evidence

One surprising choice is not a new theory. A useful behavioural model should:

state a restricted departure from a benchmark;
predict a new pattern before seeing it;
survive incentive, framing, and comprehension checks;
replicate across samples or explain heterogeneity;
improve prediction or policy relative to a simpler model.

Practice

Separate loss aversion and probability weighting in a 2×2 experimental design.
Solve the procrastination example for β=0.9 and δ=0.95.
Use the Fehr–Schmidt model to find when a responder rejects a 9/1 split.
Design a default experiment with an active-choice comparison arm.
Explain why a 1.4-point effect can be valuable yet fail a strong cost-benefit test.

Quick check

An anomaly must be defined relative to a clear benchmark.
Reference dependence, probability weighting, and loss aversion are distinct.
Present bias creates preference reversal and possible demand for commitment.
Experimental control does not guarantee policy-scale effect size.
Behavioural welfare requires an explicit standard and easy opt-out.

Next: internalise external effects and provide public goods.

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