What is behavioral economics (short definition)
– Behavioral economics studies how psychological factors—like emotions, mental shortcuts, social pressures, and limited information—influence the economic choices people and institutions make. It explains why real decisions sometimes deviate from the “rational choice” model that classical economics assumes.
Why this matters (key point)
– Standard economics assumes agents consistently maximize utility by weighing costs and benefits. Behavioral economics documents systematic departures from that ideal and shows how those departures affect markets, product design, public policy, and personal finance.
Core concepts (definitions)
– Rational choice theory: the classical assumption that people pick the option that gives the highest expected benefit given their constraints.
– Bounded rationality: decision-making that is rational within limits—people use limited information, simplified strategies, and satisficing (choosing good-enough options) rather than optimizing.
– Heuristics: mental shortcuts used to simplify complex decisions (e.g., availability heuristic: estimating probabilities based on how easily examples come to mind).
– Framing: changing how choices are presented alters decisions (the same outcome framed as a “gain” versus a “loss” can produce different preferences).
– Loss aversion: losses tend to hurt more psychologically than equal-sized gains please, making people avoid losses even when a tradeoff would be rational.
– Mental accounting: people mentally segregate money into buckets (e.g., “vacation fund” vs “retirement”) and treat them differently.
– Sunk-cost fallacy: continuing an endeavor because past resources (time, money) have been spent, rather than because future expected returns justify it.
– Choice architecture and nudging: designing how options appear to steer people toward particular decisions without removing freedom of choice (for example, default pension enrollment).
Brief history and notable contributors
– Adam Smith recognized limits to perfect rationality long before modern behavioral research. Key modern milestones include Herbert A. Simon’s bounded rationality, Amos Tversky and Daniel Kahneman’s work on heuristics and prospect theory, and Richard Thaler’s contributions on nudges and self-control. These ideas gained broad influence across economics and policy in the late 20th and early 21st centuries.
Factors that commonly influence decisions
– Limited information and processing ability.
– Emotions and mood.
– Social context and herd behavior (doing what others do).
– Presentation and defaults.
– Incentives shaped to exploit heuristics (e.g., product placement or pricing that nudges choices).
Applications (concise examples)
– Financial markets: investor overconfidence, herding, and bias-driven mispricings.
– Product pricing and packaging: using anchoring (a high “list” price) to make discounts feel larger.
– Public policy: default opt-in for retirement plans raises savings rates.
– Game theory and strategy: anticipating biased behavior can change optimal moves in negotiations or auctions.
Practical checklist: how to use behavioral insights (for traders, students, and practitioners)
1. Identify the decision frame: restate the choice in neutral terms (avoid emotionally loaded language).
2. List potential cognitive biases that might apply (e.g., loss aversion, anchoring, confirmation bias).
3. Quantify outcomes where possible (expected values, probabilities). If numbers aren’t available, estimate ranges.
4. Apply simple rules or pre-commitments (stop-loss rules, automatic savings, checklists).
5. Diversify information sources to reduce availability and confirmation biases.
6. Use choice architecture intentionally: set defaults that reflect long-term goals, but preserve freedom.
7. Test and record results; iterate using data rather than intuition.
Worked numeric example — loss aversion and choice framing
Scenario: Two options are offered.
– Option A: Receive $100 for sure.
– Option B: 50% chance to receive $240, 50% chance to receive -$10 (a loss of $10).
Calculate expected values (EV):
– EV(A) = $100.
– EV(B) = 0.5 * $240 + 0.5 * (-$10) = $120 – $5 = $115.
Observation:
– EV(B) > EV(A) by $15, so a risk-neutral decision-maker would prefer B. In practice, many people prefer A because the possibility of a loss (even small) and the certainty of A interact with loss aversion and risk preferences to favor the sure gain. Framing the same payoffs as avoiding losses rather than achieving gains would change the
preferred choice: when the same outcomes are presented as avoiding losses rather than securing gains, people often switch toward the risky option. That pattern is a core prediction of prospect theory: people are risk-averse over gains and risk-seeking over losses.
Worked numeric demonstration using a prospect-theory value function
Assumptions and formula
– Value function v(x):
– For gains x ≥ 0: v(x) = x^α
– For losses x < 0: v(x) = −λ (−x)^α
– Use standard calibration from Kahnem