constructed preferences ss200 colin camerer n.
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  1. “Constructed” preferencesSS200 Colin Camerer • Preferences: “complete, transitive” u(x), tradeoffs among goods • Historical note: Axioms not empirically well-founded. They were designed to provide simple mathematical framework for aggregation (utility demand) and because Pareto won the “what is utility?” battle • “Constructed” suggests expression of preference is like problem-solving: • Will you vote for John Kerry? • Answered by rapid intuition (tall, good hair) and/or deliberate logic (positions on issues) • Alternative views of preference: • Learned (reinforcement, “locked in a closet” story) • “Discovered” (Plott, implies path-independence) • Hybrid view: Combination of predisposition (e.g., language, “preparedness”), learning and logic

  2. “Constructed” preference: effects • Context-dependence (comparative) • Description-dependent “framing (descriptions guide attention) • Reference-dependence (changes, not levels; anchoring) • Some values “protected”/sacred (health, environment) • Is too much choice bad? • Open questions: • Are effects smaller with familiar choices? • Experts? • Markets? • New predictions (e.g. “big tip” labor supply experiment) • Cross-species (pigeons, rats, capuchins)

  3. 1/n heuristic & partition dependence in the lab (cf. “corporate socialism”, Scharfstein & Stein, at corporate level)

  4. Context-dependence (comparative) • Objects judged relative to others in a choice set • Asymmetric dominance • Compromise effects • Economic question: What is seller’s optimal choice set given context-dependent preferences?

  5. Description-dependent “framing” (descriptions guide attention) • Analogy to figure-ground in perception • Actual study with n=792 docs (Harvard Med, Brigham &Women’s, Hebrew U; McNeil et al JAMA ’80s) • treatment 1 yr 5 yrs choice • Surgery 10% 32% 66% 53% • Radiation 0% 23% 78% 47% • treatment 1 yr 5 yrs choice both frames • Surgery 90% 68% 34% 82% 60% • Radiation 100% 77% 22% 18% 40% • Asian disease problem (-200 vs (1/3) of -600 / +400 vs (2/3) 600 • Pro-choice vs pro-life • Politics: “spin” (Lakoff) • e.g. aren’t we better off w/ Hussein gone? • Liberation vs. occupation • …other examples? • Supply-side response: Competitive framing; which frame “wins”?

  6. Reference-dependence • Sensations depend on reference points r • E.g. put two hands in separate hot and cold water, then in one large warm bath • Hot hand feels colder and the cold hand feels hotter • Loss-aversion ≡ -v(-x) > v(x) for x>0 (KT 79) • Or v’(x)|+ < v’(x) |- …a “kink” at 0; “first-order risk-aversion” aka focussing illusion? • Requires theory of “mental accounting” • What gains/losses are grouped together? • When are mental accounts closed/opened? • Conjecture: time, space, cognitive boundaries matter • Example: Last-race-of-the-day effect (bets switch to longshots to “break even”, McGlothlin 1956)

  7. Reference-dependence modelling • Where does r come from? MORE TBA HERE • Default: Usually status quo or pre-experiment condition • Koszegi-Rabin ’05: Reference point r is based on “recent” expectations…“personal equilibrium” in which choices optimize ref-dependent utility given r, and r is fulfilled • Utility from u(c)+µ(c-r)|+ λµ(r-c)|+ • Exhibits multiple equilibria,Giffen good effects, endowment effect (sensitive to “how much” one owns an object, e.g. PZ end-of-trial or J List effects)

  8. Prospect theory value function: Note kink at zero and diminishing marginal sensitivity (concave for x>0, convex for x<0)

  9. Endowment effects (KKT JPE ’90)

  10. KKT “mugs” experiment (JPE ‘90)

  11. Plott-Zeiler review

  12. Data from young (PCC) and old (80 yr olds) using PZ instructions (Kovalchik et al JEBO in press 04)

  13. Plott-Zeiler (AER 05) results: replication (top) vs mugs-first (bottom)

  14. “Status quo bias” and defaults in organ donation (Johnson-Goldstein Sci 03)

  15. Loss-aversion in savings decisions (note few points with actual utility <0) from Chua & Camerer 03 (slopes .86 +, .33 - ratio 2.63)

  16. Disposition effects in housing (Genesove and Mayer, 2001) • Why is housing important? • It's big: • Residential real estate $ value is close to stock market value. • It’s likely that limited rationality persists • most people buy houses rarely (don't learn from experience). • Very emotional ("I fell in love with that house"). • House purchases are "big, rare" decisions -- mating, kids, education, jobs • Advice market may not correct errors • buyer and seller agents typically paid a fixed % of $ price (Steve Levitt study shows agents sell their own houses more slowly and get more $). • Claim: • People hate selling their houses at a "loss" from nominal [not inflation-adjusted!] original purchase price.

  17. Boston condo slump in nominal prices

  18. G-M econometric model Model: Listing price L_ist depends on “hedonic terms” and m*Loss_ist (m=0 is no disposition effect) …but *measured* LOSS_ist excludes unobserved quality v_i …so the error term η_it contains true error and unobserved quality v_i …causes upward bias in measurement of m Intuitively: If a house has a great unobserved quality v_i, the purchase price P^0_is will be too high relative to the regression. The model will think that somebody who refused to cut their price is being loss-averse whereas they are really just pricing to capture the unobserved component of value.

  19. Results: m is significant, smaller for investors (not owner-occupants; less “attachment”?)

  20. Cab driver “income targeting” (Camerer et al QJE 97)

  21. Cab driver instrumental variables (IV) showing experience effect

  22. Anchored valuation: Valuations for listening to poetry framed as labor (top) or leisure (bottom)(Ariely, Loewenstein, Prelec QJE 03 and working paperhttp://sds.hss.cmu.edu/faculty/Loewenstein/downloads/Sawyersubmitted.pdf

  23. “Arbitrary” valuations • Stock prices? • Wages (what are different jobs really worth?) • Depends on value to firm (hard to measure) • & “compensating differentials/disutility (hard to measure) • Exotic new products • Housing (SF Pittsburgh tend to buy “too much house”; Simonsohn and Loewenstein 03) • Exec comp'n (govt e.g. $150k for senator, vs CEO's, $38.5 million Britney Spears)

  24. What econ. would happen if valuations are arbitrary? • Perfect competition price=marginal cost…anchoring influences quantity, not price; expect large Q variations for similar products • Attempts to influence the anchor (QVC home shopping, etc., "for you just $59.95”). • Advertising!!! • If social comparison/imitation is an anchor, expect geographical, temporal, social clustering (see this in law & medical practice) • E.g., CEO pay linked to pay of Directors on Board's comp'n committee. Geographical differences in housing prices, London,Tokyo, NYC, SF. • Interindustry wage differentials for the same work (Stanford contracts out janitorial service so it doesn't have to pay as much; cf. airline security personnel??) • Sports salaries: $100k/yr Miami Dolphins 1972 vs $10million/yr modern football • Huge rise in CEO comp'n from 1990 (42 times worker wage) to 2000 (531 times); big differentials between US and Europe • Consumers who are most anchorable or influenceable will be most faddish -- children and toys!!? (McDonald's happy meal etc)

  25. Is too much choice bad? • Jams study (Iyengar-Lepper): • 6 jams 40% stopped, 30% purchased • 24 jams 60% stopped, 3% purchased • Assignment study: • Short list 74% did the extra credit assignment • Long list 60% did the extra credit assignment • Participation in 401(k) goes down 2% for every 10 extra funds • Shoe salesman: Never show more than 3 pairs of shoes… • Medical • 65% of nonpatients said they would want to be in charge of medical treatment…but only12% of ex-cancer patients said they would • Camerer conjecture: The curse of the composite • Paraphrased personals ad: “I want a man with the good looks of Brad Pitt, the compassion of Denzel Washington…” • Is there “too much” mate choice in big cities?

  26. Choice-aversion • How to model “too much choice”? • Anticipated regret from making a mistake • “grass is greener”/buyer’s remorse • Direct disutility for too-large choice set (e.g. too complex) • Policy question: • Markets are good at expanding choice…what is a good institution for limiting choice? • Example: Bottled water in supermarkets • Limit “useless” substitution? What is the right amount? • Pro-govt example: Swedish privatized social security • Offered hundreds of funds • Default fund is low-fee global index (not too popular) • Most popular fund is local tech, down 80% 1st yr

  27. Capuchins obey law of demand (K. Chen et al 05)

  28. (a,b,c) means display a, then pay b or c One: stochastic dominance Two: reference-dependence (risky) Three: reference-dependence (riskless) Monkey loss-aversion

  29. Experimental markets & prob judgment • 1. Abstract stimuli vs natural events?? • pro: can precisely control information of individuals • can conpute a Bayesian prediction • con: maybe be fundamentally different mechanisms than for concrete events... • 2. Do markets eliminate biases? • Yes: specialization • Market is a dollar-weighted average opinion • Uninformed traders follow informed ones • Bankruptcy • No: Short-selling constraints • Confidence (and trade size) uncorrelated with information • Camerer (1987): Experience reduces pricing biases but *increases* allocation biases • Contingent claims markets: • Markets enforce correct prices..BUT probability judgment influences allocations and volume of trade (example: Iowa political markets)

  30. IIlusions of transparency • “Curse of knowledge” • Difficult to recover coarse partition from fine-grained one • Piaget example: New PhD’s teaching • EA Poe, “telltale heart” • Computer manuals • “ The tapper” study (tapping out songs with a pencil) • Hindsight bias • Recollection of P_t(X) at t+1 biased by whether X occurred • “I should have known!” • “You should have known” (“ignored warning signs”) • --> juries in legal cases (securities cases) • implications for principal-agent relations? • Spotlight effect (Tom Gilovich et al) • Eating/movies alone • Wearing a Barry Manilow t-shirt •  psychology: Shows how much we think others are attending when they’re not