Behavioral economics Study Guide

📖 Core Concepts Behavioral Economics – studies how cognition, affect, social influences, and other psychological factors shape decisions of individuals and institutions. Bounded Rationality – decision‑making is limited by information, cognitive capacity, and time; agents satisfice (choose “good enough”) instead of optimizing. Prospect Theory – a two‑stage model (editing → evaluation) where outcomes are judged relative to a reference point; gains and losses are weighted asymmetrically. Loss Aversion – losses loom ≈ 2.25× larger than equivalent gains, producing steeper value loss curve. Nudge / Choice Architecture – subtle changes in the decision environment that steer behavior while keeping all options available (libertarian paternalism). Heuristics & Biases – mental shortcuts (e.g., satisficing, mental accounting, herd behavior) that produce systematic deviations from rational choice. Behavioral Finance – applies these ideas to markets, explaining price distortions, excess volatility, and anomalies. --- 📌 Must Remember Reference Dependence: Outcomes are evaluated as gains above or losses below a reference point. Loss‑Aversion Coefficient: ≈ 2.25 (losses weigh >2× gains). Probability Weighting: Small probabilities are over‑weighted, large probabilities under‑weighted (inverse‑S shape). Satisficing Rule: Stop searching once a pre‑specified acceptable threshold is met. Nudge ≠ Sludge: Nudges facilitate desired actions; sludge adds unnecessary friction (e.g., hidden fees). Endowment Effect: Owners value a good more than non‑owners → linked to loss aversion & status‑quo bias. Present Bias: Preference for immediate reward over larger delayed reward (time‑inconsistent discounting). Framing Effect: Identical options elicit different choices when presented as gains vs. losses. --- 🔄 Key Processes Prospect Theory Evaluation Identify reference point → classify outcomes as gains/losses. Apply value function: concave for gains, convex for losses, steeper slope for losses. Compute decision weights using the probability‑weighting function (over‑weight low probs, under‑weight high probs). Satisficing Search Set acceptance threshold \(T\). Sequentially evaluate options; stop when an option \(xi\) satisfies \(xi \ge T\). Nudge Design (Choice Architecture) Determine desired behavior. Choose a default, placement, simplification, or social‑norm cue. Ensure transparency and no‑large‑cost impact on incentives. Behavioral Field Experiment Define treatment (e.g., nudge) & control groups. Randomly assign participants; use real monetary incentives. Measure outcome differences; test statistical significance. --- 🔍 Key Comparisons Bounded Rationality vs. Traditional Rationality – limited info & computation vs. full optimization. Nudge vs. Sludge – facilitates good choices vs. creates barriers. Prospect Theory vs. Expected Utility Theory – reference‑dependent, loss‑averse, non‑linear probabilities vs. linear probability weighting and outcome‑independent utility. Present Bias vs. Exponential Discounting – hyperbolic (steeper near‑term) discounting vs. constant discount rate. Endowment Effect vs. Market Value – owner’s valuation > market price vs. price based on willingness to pay. --- ⚠️ Common Misunderstandings “People are irrational” – they are systematically biased, not random; models predict direction of deviation. Loss aversion = “people hate loss” – it’s the relative weighting of losses vs. gains, not an absolute aversion to any loss. Nudges eliminate choice – they preserve freedom; they only alter the default or presentation. All heuristics are bad – heuristics reduce cognitive cost and are often adaptive; problems arise when context changes. Behavioral findings always generalize – many studies use WEIRD samples; external validity must be tested. --- 🧠 Mental Models / Intuition “Planner‑Doer” – your long‑run planner prefers optimal outcomes; the short‑run doer prefers immediate gratification → explains self‑control problems & commitment devices. “Reference Point Anchor” – think of a thermostat: the current temperature sets the baseline; any change is judged relative to that baseline. “Probability Distortion Lens” – treat rare events as “magnified” (lottery) and common events as “dampened” (insurance). “Choice Architecture as a Funnel” – the environment narrows the set of salient options, guiding flow without blocking alternatives. --- 🚩 Exceptions & Edge Cases Loss‑Aversion Coefficient Varies – cultural and contextual factors can shift the 2.25 figure (e.g., lower in some low‑income settings). Probability Weighting Reversals – for very high stakes, people may under‑weight small probabilities (e.g., disaster insurance). Status‑Quo Bias vs. Endowment Effect – when no ownership is involved, status‑quo bias can still dominate (e.g., default options). Nudge Effectiveness – can diminish if individuals become aware of manipulation (reactance) or if incentives are too weak. --- 📍 When to Use Which Use Prospect Theory when evaluating risky choices with clear gains/losses and where probability distortion matters (e.g., insurance, gambling). Apply Bounded Rationality / Satisficing in complex, time‑pressured decisions (e.g., consumer product search). Deploy Nudge when policy goals align with a modest change in behavior and the cost of altering incentives is high. Choose Field Experiments to test interventions in real‑world settings; use laboratory experiments for isolated mechanism testing. Select Behavioral Finance Models when explaining market anomalies like excess volatility, herding, or the equity‑premium puzzle. --- 👀 Patterns to Recognize Reference‑Dependent Framing → look for “gain vs. loss” wording in questions. Over‑Weighted Small Probabilities → lottery‑type payoffs often signal a bias. Default Options → high adoption rates usually indicate a successful nudge. Satisficing Signals – early‑stop search behavior, “good enough” thresholds in survey responses. Consistency Across Contexts – same bias (e.g., present bias) appearing in health, finance, and labor‑market questions. --- 🗂️ Exam Traps Confusing “Loss Aversion” with “Risk Aversion” – loss aversion is about asymmetry around a reference point; risk aversion is curvature of utility over wealth. Assuming All Probability Weighting is Over‑Weighting – large probabilities are under‑weighted; watch for “inverse‑S” description. Choosing “Rational Model” When Question Highlights Cognitive Limits – the correct answer often involves bounded rationality or heuristics. Mix‑up of Nudge vs. Sludge – answer choices that mention “removing options” are not nudges. Endowment Effect vs. Market Price – exam may ask which explains why sellers demand more than buyers pay; the correct term is endowment effect, not “high market price”. ---