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Chris Starmer

Chris Starmer. The Behavioural Economics Revolution?. TSU Short course in Experimental and Behavioural Economics, 5-9 November 2012. Overview of Sessions. The Behavioural Revolution in Econ The Experimental Economist Individual Decisions Strategic Decisions 5. Markets.

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Chris Starmer

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  1. Chris Starmer The Behavioural Economics Revolution? TSU Short course in Experimental and Behavioural Economics, 5-9 November 2012

  2. Overview of Sessions • The Behavioural Revolution in Econ • The Experimental Economist • Individual Decisions • Strategic Decisions 5. Markets

  3. Rules of Engagement • Do ask questions if you want • Access to slides • References

  4. Behavioural Economics is Popular • Gaining momentum • Economic theory, Applied economics, Policy circles, Media discussions, Private enterprise • So what is it? • How does it differ from conventional Econ? • Is it the future of economics (or a fad)? • Should we welcome it?

  5. So What is BE? • No single definition • For e.g’s - Google: Colin Camerer, Richard Thaler, George Loewenstein • Common assertions – what it is: • More ‘realistic’ psychological foundations for economics • Bounded Rationality • Common claims – what it does: • More ‘realistic’ theories • Improved prediction of human behaviour • Useful policy tools (cheap/effective)

  6. Popular science summaries Great summary Implications for Policy

  7. BE - Scientific Revolution? • Changing character of economics • How, as economists, we think about: • Evidence • Theory • Rationality

  8. Evidence “Experimental Turn” in Economics • Explosion in Experimentation (since 1980s) • Fundamental to development of BE • “It is rarely, if ever, possible to conduct controlled experiments with the economy. Thus economics must be a non-laboratory science.” • Richard Lipsey (1979) An Introduction to Positive Economics

  9. Two classic experiments • Individual preferences • The endowment effect • Social Preferences • The pull of the crowd • Both very simple • Maybe you could have done it if you’d thought of it first!

  10. Give up mug to get chocolate? (89% prefer mugs, n=76) Give up chocolate to get mug? (10% prefer mugs, n=87) Jack Knetsch (American Economic Review, 1989) What proportion of people prefer mugs to chocolate bars? “Endowment Effect”

  11. Pull of the crowd • Bryan, J.H. and Test, M.A. (1967) “Models and helping: naturalistic studies in aiding”, Journal of Personality and Social Psychology, 6, 400-7.

  12. Impact of Experimentation • Produced Many ‘ANOMALIES’ • Patterns of behaviour • Influences on behaviour • Surprising (relative to std Econ Theory) • Examples • Time and Risk Responses • Social Preferences (altruism, reciprocity) • Myopia, Status quo bias • Partial information • Context sensitivity of choice • Experience matters

  13. From anomaly to (behavioural) theory Anomalies New Theories (many) Experiments testing new theories Some successes e.g. “Prospect Theory” Kahneman/Tversky

  14. Leading to New Breed(s) of Theory • Empirically Grounded (vs axiomatic) • More “realistic” psychological foundations • Bounded Rationality vs Full Rationality

  15. More ‘realistic’ assumptions Realistic about what? Two key dimensions – Preferences and Reasoning

  16. Preferences • Individual preferences • Risk • Time e.g. ‘prospect theory’ Kahneman & Tevrsky (Econometrica,1979) • Social preferences • Egoism • Fairness • Reciprocity e.g. ‘Theory of fairness, competition and cooperation’ Fehr & Schhmidt (QJE, 1999)

  17. Reasoning • Cognitive limitations • Calculating ability • Myopia • Memory • Abilities • Speed • Adaptability Bounded Rationality: Herbert Simon , ‘How to decide what to do’, Bell Journal, 1978. Giggerenzer, Tod, ABC, Simple Heuristics That Make us Smart, Oxford, UP 2000.

  18. Bounded Rationality in a Nutshell • Because of: • Limits of computational capacity (e.g. Memory) • Costs of deliberation (e.g. time) • Agents develop/use: • decision heuristics, rules of thumb • Rules of thumb, help agents navigate complex world • Sometimes • Rules of thumb lead to suboptimal decisions • But also support fast effective decisions

  19. Who’s the best driver: economicus or heuristicus? Simple Heuristics That Make Us Smart Gigerenzer et al, OUP, 1999

  20. Session 1 - Part II Behavioural Economics in Action (i) A classic Theory (ii) Some Applications

  21. (i) A classic model in Behavioural Economics Prospect Theory, Kahneman and Tversky, Econometrica, 1979

  22. Prospect theory - Background • Theory of Decision Under Risk • Competitor to Expected Utility Theory • One of most highly cited papers in economics • (Google Scholar: 24k+ Cites, Nov 2012) • Often cited as key development in ‘behavioural economics” • Kahneman shared Nobel prize, 2002 • Aspects of prospect theory becoming ‘mainstream’? (e.g. loss aversion)

  23. A thoroughly behavioural theory • Built from experimental evidence • Anomalies relative to standard theory • Informed by psychological theory • E.g. Psychophysics of perception • Features assumptions about both • (limitations of) Reasoning • Non-standard preferences • Claims improved predictive power • Relative to expected utility theory

  24. Structure of PT • Theory of choice among risks or “prospects” • Prospect is prob. Dist. over consequences (p1, x1; p2, x2.........pn, xn) • “Two Step” Theory • Step 1: ‘editing’ • Step 2: ‘evaluation’

  25. Editing Step • Before evaluating prospects individuals ‘edit’ choice set • Editing involves (simplification) heuristics: • e.g.1. rounding outcomes/probs • e.g.2 ignoring v. small prob events • e.g.3 elimination of (transparent) dominance

  26. Example: Choose A or B Option A 90% white 6% red 1% green 3% yellow $0 win $45 win $30 lose $15 Option B 90% white 7% red 1% green 2% yellow $0 win $45 lose $10 lose $15 Tversky and Kahneman, 1986: 58% chose A Rearrange the information 90% white 6% red 1% green 1% blue 2% yell A: $0 win $45 win $30 lose $15 lose $15 B: $0 win $45 win $45 lose $10 lose $15 Easy to see that B dominates A Using second ‘framing’ everyone chose B (n=88)

  27. Evaluation Step Uses (Non-standard) preference function applied to edited choice set (Roughly) Max V(q) =  (pi)v(xi) (pi) is a “probability weighting” function v(xi) a utility function on outcomes EUT is Special case where (pi)=p and V(.) is vNM utility function

  28. Evaluation is…. Model of maximisation, but….. Incorporating (empirically grounded) assumptions about human perception: 1) probability ‘distortion’ 2) loss aversion Common interpretation is that these are ‘biases’ relative to optimal decisions

  29. PT assumes Overweigthing ‘small’ p Underweighting ‘large’ p Support Psychophysics Field evidence Gambling Risk assessment Provides fit to ‘anomaly evidence’ Probability distortion p

  30. The Value Function • Built on three main ‘psychological’ assumptions: • Carriers of value are changes relative to a reference point • Gains and losses evaluated separately • “Loss aversion” • losses loom larger than gains

  31. Value of Δx on gain scale -Δx +Δx Value of Δx on loss scale

  32. Assessment of Prospect theory • More ‘realistic’ decision model • surely, people do simplify complex decisions • Considerable evidence of loss aversion, probability distortion • Some additional predictive content • More complex model • Spawned a large research programme • Developing and testing PT • Using it to explain field phenomena

  33. Applications Behavioural Economics in the wild

  34. The Equity Premium Puzzle Excess return of stocks over bonds (long run) Why do people invest so much in safer assets? Benartzi/Thaler, Quart. J. Econ, 1995. Loss Aversion Myopia Prospect theory in the Wild:Predicting Investment

  35. BE and Public Policy Policy makers have become interested in BE Because maybe it helps explain why people do ‘suboptimal things’ • Not save enough, drink too much, drive too fast, waste things (Energy, water, food) • Might help identify (cheap but effective) new tools for policy intervention • The ‘Nudge’ agenda

  36. “Our government will find intelligent ways to encourage, support and enable people to make better choices for themselves.”

  37. Benartzi and Thaler, JPE, 2004 • Companies concerned re low level of employee savings • not increasing in line with income growth • Prescriptive savings program (Smart) • based on findings of behavioural economics • “Status quo bias” • pre-commit to savings increases out of future income growth • Opt out facility (instead of opt in) • Increased savings rate from 4% to 12% over 2 year period

  38. Encouraging repayment of court fines Source: Behavioural Insights Team & HMCTS, 2012

  39. Encouraging repayment of court fines Source: Behavioural Insights Team & HMCTS, 2012

  40. That’s it Tomorrow – the experimental economist

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