Economic Theory and Experimental Economics: Confronting Theory with Experimental Data and vice versa. Lecture I & II Risk preferences

Transcription

1 Economic Theory and Experimental Economics: Confronting Theory with Experimental Data and vice versa Lecture I & II Risk preferences Bar Ilan University Mar 2012

2 Preferences Let X be some set of alternatives (consumption set). Formally, we represent the consumer s preferences by a binary relation % defined on the consumption set X. For any pair of baskets (or bundles) x and y, if the consumer says that x is at least as good as y, wewritex % y and say that x is weakly preferred to y. Bear in mind: economic theory often seeks to convince you with simple examples and then gets you to extrapolate. This simple construction works in wider (and wilder circumstances).

3 The basic assumptions about preferences The theory begins with two (not three!) assumptions about preferences [1] Completeness x % y or y % x for any pair of bundles x and y. [2] Transitivity if x % y and y % z then x % z or any three bundles x, y and z.

4 Together, completeness and transitivity constitute the formal definition of rationality as the term is used in economics. Rational economic agents are ones who [1] have the ability to make choices, and [2] whose choices display a logical consistency. The preferences of a rational agent can be represented, or summarized, by a utility function (more later).

5 The paternity of decision theory and game theory (1944)

6 Preferences toward risk The standard model of decisions under risk (known probabilities) is based on von Neumann and Morgenstern Expected Utility Theory. Let X be a set of lotteries, or gambles, (outcomes and probabilities). A fundamental assumption about preferences toward risk is independence: For any lotteries x, y, z and 0 <α<1 x  y implies αx +(1 α)r  αy +(1 α)r.

7 Experiments à la Allais Allais (1953) I Choose between the two gambles:.33 % A :=.66 $25, 000 $24, 000 B := 1 $24, 000 &.01 $0

8 Allais (1953) II Choose between the two gambles: 33 $ $ % := := & % & $0 66 $0

9 The Marschak-Machina probability triangle 1 P H Increasing preference 0 P L 1 H, M, and L are three degenerate gambles with certain outcomes H>M>L

10 A test of Expected Utility Theory (EUT) 1 P H B A D 0 P L C 1 EUT requires that indifference lines are parallel so one must choose either A and C, or B and D.

11 Contributions Results have generated the most impressive dialogue between observation and theorizing (Camerer, 1995): Violations of EUT raise criticisms about the status of the Savage axioms as the touchstone of rationality. These criticisms have generated the development of various alternatives to EUT, such as Prospect Theory.

12 Limitations Choice scenarios narrowly tailored to reveal anomalies limits the usefulness of data for other purposes: Subjects face extreme rather than typical decision problems designed to encourage violations of specific axioms. Small data sets force experimenters to pool data and to ignore individual heterogeneity.

13 Research questions Consistency Is behavior under uncertainty consistent with the utility maximization model? Structure Is behavior consistent with a utility function with some special structural properties?

14 Recoverability Can the underlying utility function be recovered from observed choices? Extrapolation Given behavior in the laboratory, can we forecast behavior in other environments?

15 A new experimental design An experimental design that has a couple of fundamental innovations over previous work: A selection of a bundle of contingent commodities from a budget set (a portfolio choice problem). A graphical experimental interface that allows for the collection of a rich individual-level data set.

16 The experimental computer program dialog windows

17

18 Rationality Let {(p i,x i )} 50 i=1 be some observed individual data (pi denotes the i-th observation of the price vector and x i denotes the associated portfolio). A utility function u(x) rationalizes the observed behavior if it achieves the maximum on the budget set at the chosen portfolio u(x i ) u(x) for all x s.t. p i x i p i x.

19 Foundations of Economic Analysis (1947) Paul A. Samuelson ( ) the first American Nobel laureate in economics and the foremost (academic) economist of the 20th century (and the uncle of Larry Summers ).

20 Revealed preference A portfolio x i is directly revealed preferred to a portfolio x j if p i x i p i x j,andx i is strictly directly revealed preferred to x j if the inequality is strict. The relation indirectly revealed preferred is the transitive closure of the directly revealed preferred relation.

21 Generalized Axiom of Revealed Preference (GARP) If x i is indirectly revealed preferred to x j,thenx j is not strictly directly revealed preferred (i.e. p j x j p j x i )tox i. GARP is tied to utility representation through a theorem, which was first proved by Afriat (1967).

22 Afriat s Theorem The following conditions are equivalent: The data satisfy GARP. There exists a non-satiated utility function that rationalizes the data. There exists a concave, monotonic, continuous, non-satiated utility function that rationalizes the data.

23 Afriat s critical cost efficiency index (CCEI) The amount by which each budget constraint must be relaxed in order to remove all violations of GARP. The CCEI is bounded between zero and one. The closer it is to one, the smaller the perturbation required to remove all violations and thus the closer the data are to satisfying GARP.

24 The construction of the CCEI for a simple violation of GARP x 2 D C B A y x The agent is wasting' as much as A/B<C/D of his income by making inefficient choices. x 1

25 A benchmark level of consistency A random sample of hypothetical subjects who implement the power utility function ( ) = 1 1 commonly employed in the empirical analysis of choice under uncertainty, with error. The likelihood of error is assumed to be a decreasing function of the utility cost of an error.

26 More precisely, we assume an idiosyncratic preference shock that has a logistic distribution Pr( )= ( ) R ( ) : =1 where the precision parameter reflects sensitivity to differences in utility. If utility maximization is not the correct model, is our experiment sufficiently powerful to detect it?

27 The distributions of GARP violations ρ=1/2 and different γ γ=5 γ=10 γ=1 γ=1/2 γ=1/ CCEI

28 Bronnars (1987) test (γ=0) n=50 n= CCEI n=15 n=5 n=

29 Recoverability Revealed preference relations in the data contain the information that is necessary for recovering preferences. Varian (ECMA, 1982) uses GARP to generate an algorithm that can recover preferences from choices. This approach is purely nonparametric making no assumptions about the parametric form of the underlying utility function.

30 Risk neutrality

31 Infinite risk aversion

32 Loss / disappointment aversion

33 Decision-making quality

34 Decision-making quality In economics, heterogeneity in choices is attributed to heterogeneity in preferences, information, beliefs, orconstraints. Several strands of empirical research consider heterogeneity in choices driven, instead, by differences in the quality of decision-making. The revealed preference criterion: if decisions are high quality then there exists a utility function that the choices maximize.

35 Judgment about the quality of decision-making is generally made difficult by twin problems of identification and measurement: Theidentification problem Distinguishing differences in decisionmaking quality from unobserved differences in preferences, information, beliefs or constraints. Identification is important because welfare conclusions and thus (constrained) optimal policy will depend on the sources of any systematic differences in choices. The measurement problem Defining (and implementing) a portable, practical, autonomous, quantifiable,andeconomically interpretable measure of decision-making quality.

36 The CentERpanel A representative sample of over 2,000 Dutch-speaking households (5,000 individual members) in the Netherlands. A wide range of individual socio-demographic and economic information for the panel members. The subjects in the experiment were randomly recruited from the entire CentERpanel body.

37 Participants Dropouts Nonparticipants Female Age Education Low Medium High Household monthly income Occupation Paid work House work Retired Others Household composition Partner # of kids # of obs

38 Types of analysis [1] A descriptive overview of the average quality of decisions (consistency with economic rationality). [2] The relationship between decision-making quality and demographic and economic characteristics. [3] The correspondence between wealth accumulation and behavior in the laboratory.

39 Mean CCEI scores

40 CCEI scores Combined CCEI CCEI Constant Female.887*** -.024***.735*** (.022) (.009) (.037) (.015) Age *** -.051** *** -.081** (.011) (.011) (.020) (.020) (.020) (.032) Education Medium High ** *** (.011) (.011) (.017) (.018) Income ** ** (.012) (.013) (.014) (.019) (.020) (.022) Occupation Paid work House work Others **.037* (.018) (.021) (.019) (.026) (.030) (.030) Household composition Partner # of children -.026** (.011) (.004) (.018) (.007) R # of obs

41 Dominance Violations of monotonicity with respect to first-order stochastic dominance (FOSD) are errors, regardless of risk attitudes. A decision to allocate less tokens to the cheaper account violates dominance but need not involve a violation of GARP. We use expected payoff calculations (largest upward probabilistic shift) to assess how nearly choice behavior complies with dominance.

42 A scatterplot of CCEI and FOSD scores

43 Wealth differentials = The heterogeneity in wealth is not well-explained either by standard observables (income, education, family structure) or by standard unobservables (intertemporal substitution, risk tolerance). = If consistency with utility maximization in the experiment were a good proxy for (financial) decision-making quality then the degree to which consistency differ across subjects should help explain wealth differentials.

44 Wealth regressions Ln(hhld wealth) CCEI Combined CCEI Risk attitude Conscientiousness Ln(hhld income '08) Female Age Age 2 Age 3 Partner # of children (0.072) 1.425** *** *** * *** *** ** *** *** ** 0.514*** *** (0.565) (0.381) (0.838) (0.127) (0.164) (0.316) (0.005) (0.000) (0.183) (0.092) (0.714) (0.381) (0.840) (0.127) (0.164) (0.316) (0.005) (0.000) (0.183) (0.093) (0.746) (0.384) (0.121) (0.168) (0.319) (0.005) (0.000) (0.191) (0.095) (0.750) (0.121) (0.169) (0.316) (0.005) (0.000) (0.191) (0.095) Education controls Y Y Y Y Constant (5.862) (5.879) (5.880) (5.841) R # of obs

45 Sources of correlation Fraction of hhld wealth Ln(hhld wealth) Checking Saving Stocks House CCEI Ln(hhld income '08) Ln(hhld income '06) Ln(hhld income '04) Female partner # of children Constant 0.467* (0.262) 0.312* (0.176) 1.907** 0.686*** *** ** *** * ** * *** * *** *** 0.098*** *** 0.069*** (0.751) (0.133) (0.188) (0.222) (0.107) (5.947) (0.705) (0.184) (0.184) (0.222) (0.101) (6.032) (0.057) (0.013) (0.018) (0.021) (0.009) (0.750) (0.096) (0.021) (0.029) (0.033) (0.013) (1.197) (0.050) (0.010) (0.012) (0.014) (0.007) (0.388) (0.129) (0.023) (0.039) (0.044) (0.019) (1.289) R # of obs

46 Is there a rationality gap in development? CCEI # of Mean SD obs. Berkeley Low High HM # of Mean SD obs. Berkeley Low High

47 FOSD # of Mean SD obs. Berkeley Low High Time # of Mean SD obs. Berkeley Low High

48 Risk aversion and loss aversion

49 Risk aversion the fraction of tokens allocated to the cheaper asset

50 Risk aversion All sample CCEI 0.8 CCEI 0.9 Female *** *** *** (0.007) (0.008) (0.010) Age *** *** *** *** *** *** (0.011) (0.010) (0.014) (0.012) (0.011) (0.018) (0.014) (0.013) (0.021) Education Medium High 0.018** 0.015** 0.018* ** 0.026** (0.008) (0.008) (0.009) (0.010) (0.012) (0.012) Income (0.008) (0.009) (0.011) (0.011) (0.011) (0.013) (0.013) (0.014) (0.016) Occupation Paid work House work Others (0.011) (0.013) (0.012) (0.014) (0.017) (0.016) (0.017) (0.020) (0.020) Household composition Partner # of children Constant ** 0.632*** *** *** (0.009) (0.003) (0.015) (0.010) (0.004) (0.019) (0.013) (0.005) (0.023) R # of obs. 1,

51 Loss/disappointment aversion The theory of Gul (1991) implies that the utility function over portfolios takes the form min {αu (x 1 )+u (x 2 ),u(x 1 )+αu (x 2 )}, where α 1 measures loss/disappointment aversion and u( ) is the utility ofconsumptionineachstate. If α>1 thereisakinkatthepointwherex 1 = x 2 and if α =1we have the standard EUT representation.

52 An illustration of the derived demand ln x x 1 2 x ln x 1 = 2 0 p ln p 1 = 2 0 ln p p 1 2

53 Scatterplot of the CARA NLLS estimates Risk Loss

54 Risk aversion (CARA) All sample CCEI 0.8 CCEI 0.9 Female 0.060*** 0.086*** 0.110*** (0.021) (0.026) (0.032) Age * ** *** (0.030) (0.029) (0.045) (0.036) (0.035) (0.058) (0.043) (0.042) (0.071) Education Medium High * (0.024) (0.024) (0.031) (0.030) (0.038) (0.038) Income (0.027) (0.027) (0.031) (0.035) (0.035) (0.039) (0.044) (0.044) (0.048) Occupation Paid work House work Others 0.098*** ** 0.141*** ** 0.146** (0.038) (0.042) (0.043) (0.050) (0.057) (0.056) (0.065) (0.072) (0.072) Household composition Partner # of children Constant ** (0.026) (0.010) (0.054) (0.032) (0.013) (0.068) (0.039) (0.015) (0.088) R # of obs. 1,

55 Loss aversion (CARA) All sample CCEI 0.8 CCEI 0.9 Female (0.166) (0.203) (0.247) Age * ** 0.543* *** 0.706** ** (0.241) (0.236) (0.363) (0.280) (0.277) (0.455) (0.333) (0.331) (0.560) Education Medium High ** ** (0.192) (0.192) (0.239) (0.235) (0.296) (0.291) Income * ** ** (0.216) (0.217) (0.244) (0.270) (0.272) (0.299) (0.337) (0.339) (0.369) Occupation Paid work House work Others ** 0.752* 0.752* 1.253*** ** (0.306) (0.340) (0.347) (0.394) (0.447) (0.440) (0.508) (0.561) (0.563) Household composition Partner # of children Constant *** 0.180** 1.663*** *** 0.204** 1.316** *** 0.215* 1.432** (0.207) (0.083) (0.436) (0.249) (0.100) (0.538) (0.305) (0.120) (0.692) R # of obs. 1,

56 Risk # of Mean SD obs. Berkeley Low High CARA loss # of Mean SD obs. Berkeley Low High CARA risk # of Mean SD obs. Berkeley Low High

57 Ambiguity aversion

58 Ambiguity aversion The distinction between settings with risk and ambiguity dates back to at least the work of Knight (1921). Ellsberg (1961) countered the reduction of subjective uncertainty to risk with several thought experiments. A large theoretical literature (axioms over preferences) has developed models to accommodate this behavior.

59 Experiments à la Ellsberg Consider the following four two-color Ellsberg-type urns (Halevy, 2007): I. 5 red balls and 5 black balls II. an unknown number of red and black balls III. a bag containing 11 tickets with the numbers 0-10; the number written on the drawn ticket determines the number of red balls IV. a bag containing 2 tickets with the numbers 0 and 10; the number writtenonthedrawnticketdeterminesthenumberofredballs

60 A cleverly designed experiment that allows distinguishing between four models of ambiguity aversion SEU, MEU, REU and RNEU. The different models of ambiguity aversion generate different predictions about how the urns will be ordered. For each subject, there will be a unique model that predicts (is consistent with) the subject s reservation values. No single model predicts all the observed behaviors, and all models are represented in the pool of subjects.

61 The distribution of relative demands Kernel density x /( x ) 2 x /( x ) 3 x 1 1 x 1 1 x /( x ) x Relative demands

62 The number of diagonal portfolios by subject (ambiguity - vertical axis / loss - horizontal axis) Y X

63 Parametric analysis There is a variety of theoretical models of attitudes toward risk and ambiguity, but they all give rise to one of two main specifications: [1] kinked specification rationalized by -Maxmin Expected Utility ( - MEU), Choquet Expected Utility, or Contraction Expected Utility. [2] smooth specification, based on the class of Recursive Expected Utility (REU) models.

64 The kinked specification has the form h 2 3 (min{ 1 3 })+ 1 3 ( 2) i +(1 ) h 2 3 (max{ 1 3 })+ 1 3 ( 2) i where is the ambiguity parameter. The indifference curves have a kink at all unambiguous portfolios where 1 = 3.

65 The general form of the smooth specification is Z µz ( ) ( ) ( ) where ( ( )) is a (second-order) distribution over possible priors on and : (R + ) R is a possibly nonlinear transformation over expected utility levels. The REU models are based a cardinal utility indicator the preferences generated are not independent of a change in the scale of utility.

66 To clarify, suppose risk preferences are represented by a von Neumann- Morgenstern utility function ( ) with constant absolute risk aversion (CARA) ( ) = where isthenumberoftokens, a scale parameter, and is the coefficient of absolute risk aversion. The concavity of the transformation implies that the ranking of uncertain prospects will not be invariant to changes in (cannot identify and separately).

67 Smooth (left) and kinked (right) specifications x 1 /(x 1 +x 3 ) The Simulated x 1 /(x 1 +x 3 ) and log(p 1 /p 3 ) in the REU Model when alpha = rho = rho = 0.10 rho = rho = 0.5 rho = rho = log(p 1 /p 3 ) x 1 /(x 1 +x 3 ) The Simulated x 1 /(x 1 +x 3 ) and log(p 1 /p 3 ) in the Alpha-MEU Model when alpha = rho = 0.05 rho = 0.10 rho = 0.25 rho = 0.5 rho = 1 rho = log(p 1 /p 3 ) The Simulated x 1 /(x 1 +x 3 ) and log(p 1 /p 3 ) in the REU Model when alpha = 1 rho = 0.05 rho = 0.10 rho = 0.25 rho = 0.5 rho = 1 rho = 2 The Simulated x 1 /(x 1 +x 3 ) and log(p 1 /p 3 ) in the Alpha-MEU Model when alpha = rho = 0.05 rho = 0.10 rho = 0.25 rho = 0.5 rho = 1 rho = x 1 /(x 1 +x 3 ) x 1 /(x 1 +x 3 ) log(p 1 /p 3 ) log(p 1 /p 3 )

68 Estimation Our first parametric assumption is that risk preferences are represented by a von Neumann-Morgenstern utility function with CARA, ( ) = We restrict, WLOG, the parameters so that preferences are always risk and ambiguity averse 0 in both specifications, in the kinked specification and 0 in the smooth specification. For each subject and for each specification, we generate estimates of the ambiguity and risk aversion parameters, ˆ and ˆ, using nonlinear least squares (NLLS).

69 Econometric results Kinked specification 56 subjects (38.9%) have non-kinky preferences ˆ 1 2. We cannot reject the hypothesis that =1 2 for a total of 109 subjects (75.7%) at the 5% significance level. Smooth specification 44 subjects (30.6%) have ˆ 0, indicating ambiguity neutrality. We cannot reject the hypothesis that =0for a total of 97 subjects (67.4%) at the 5% significance level.

70 Scatterplot of the estimated parameters kinked specification Rho Alpha

71 Scatterplot of the estimated parameters smooth specification Rho Alpha

72 There is the strong tendency for subjects to equate the demand for the securities that pay off in the ambiguous states, 1 and 3. This feature of the data is consistent with the kinked specification but not with the smooth specification. But the tendency to equate 1 and 3 cannot be attributed solely to ambiguity aversion. In the first place, there is also strong tendency to equate the demand for the securities that pay off in any pair of states.

73 A model of ambiguity aversion and loss/disappointment aversion If both loss aversion and ambiguity aversion are present in the data, we need a structural model in order to disentangle the two effects. In order to allow for kinks at portfolios where = 0 for any 6= 0,we make use of the rank-dependent utility (RDU) model of Quiggin (1982). This is a generalization of the SEU model that replaces probabilities with decision weights when calculating the value of expected utility. In Quiggin (1982), the decision weight of each payout depends only on its (known) probability and its ranking position.

74 Following -MEU, we assume that the unknown probabilities 1 and 3 areskewedusingtheweights and 1 : is given a probability weight 2 3 and min =min{ 1 3 } max =max{ 1 3 } is given probability weight 2 3 (1 ) where the parameter measures the degree of ambiguity aversion.

75 The utility of a portfolio x =( ) takes the form I. 2 min 1 ( 2 )+ 2 ( min )+(1 1 2 ) ( max ) II. min 2 max 3 ( min )+( ) ( 2 )+(1 1 2 ) ( max ) III. max 2 3 ( min )+ 4 ( max )+(1 3 4 ) ( 2 )

76 where 1 = ³ = ³ ³ = ³ = ³ ³ and the mapping from the four parameters 1 4 to two parameters and is as follows: 1 = = = = 1 3

77 The parameter measures the degree of ambiguity aversion and the parameter measures the degree of loss aversion: 0 and =0 kinked specification =0and 0 loss/disappointment aversion (Gul, 1991) =0and =0 standard SEU representation. The indifference curves will have kinks where = 0 and agents will choose portfolios that satisfy = 0 for a non-negligible set of prices.

78 Econometric results The vast majority of the subjects are well described by the loss- and ambiguity-neutral SEU model. The remainder appear to have a significant degree of loss and/or ambiguity aversion Ambiguity Neutral Averse Total Loss Neutral Averse Total There is considerable heterogeneity in both ˆ or ˆ and that their values are not correlated ( 2 =0 029).

79 Scatterplot of the estimated parameters generalized kinked specification (ambiguity - horizontal axis / loss - vertical axis) Y X

80 Procedural rationality

81 Procedural rationality How subjects come to make decisions that are consistent with an underlying preference ordering? Boundedly rational individuals use heuristics in their attempt to maximize an underlying preference ordering. There is a distinction between true underlying preferences and revealed preferences. Preferences have an EU representation, even though revealed preferences appear to be non-eu.

82 Archetypes and polytypes We identify a finite number of stylized behaviors, which collectively pose a challenge to decision theory. We call these basic behaviors archetypes. We also find mixtures of archetypal behaviors, which we call polytypes. The archetypes account for a large proportion of the data set and play a role in the behavior of most subjects. The combinations of types defy any of the standard models of risk aversion.

83 Center Vertex

84 Centroid (budget shares) Edge

85 Bisector Center and bisector

86 Edge and bisector Center, vertex, and edge

87 Vertex and edge Center and bisector

88 The aggregate distribution of archetypes for different token confidence intervals Center Vertex Centroid Edge Bisector All

89 The distribution of archetypes, by subject (half token confidence interval) Fraction of decisions Center Vertex Centroid Edge Bisector

90 The distribution of archetypes, by subject (one token confidence interval) Fraction of decisions Center Vertex Centroid Edge Bisector

91 A two- and three-asset experiment Token Shares in 3-asset experiment for Subject ID 3 TS 2 = 1 TS 1 = 1 TS 3 = The relation of x 1 and x 2 in 2-asset experiment for ID x x 1

92 Token Shares in 3-asset experiment for Subject ID 47 TS 2 = 1 TS 1 = 1 TS 3 = The relation of x 1 and x 2 in 2-asset experiment for ID x x 1

93 Token Shares in 3-asset experiment for Subject ID 25 TS 2 = 1 TS 1 = 1 TS 3 = The relation of x 1 and x 2 in 2-asset experiment for ID x x 1

94 Token Shares in 3-asset experiment for Subject ID 61 TS 2 = 1 TS 1 = 1 TS 3 = The relation of x 1 and x 2 in 2-asset experiment for ID x x 1

95 Token Shares in 3-asset experiment for Subject ID 65 TS 2 = 1 TS 1 = 1 TS 3 = The relation of x 1 and x 2 in 2-asset experiment for ID x x 1

96 Type-mixture model (TMM) A unified account of both procedural rationality and substantive rationality. Allow EU maximization to play the role of the underlying preference ordering. Account for subjects underlying preferences and their choice of decision rules/heuristics.

97 Ingredients The true underlying preferences are represented by a power utility function. A discrete choice among the fixed set of prototypical heuristics, D, S and B(ω). The probability of choosing each particular heuristic is a function of the budget set:

98 Subjects could make mistakes when trying to maximize EU by employing heuristic S. In contrast, when following heuristic D or B(ω) subjects hands do not tremble. A subject may prefer to choose heuristic B(ω) or D instead of the noisy version of heuristic S.

99 Specification The underlying preferences of each subject are assumed to be represented by u (x) = x1 ρ (1 ρ) (power utility function as long as consumption in each state meets the secure level ω). Let ϕ(p) be the portfolio which gives the subject the maximum (expected) utility achievable at given prices p =(p 1,p 2 ).

100 The ex ante expected payoff from attempting to maximize EU by employing heuristic S is given by U S (p) =E[πu ( ϕ 1 (p)) + (1 π)u ( ϕ 2 (p))] ϕ(p) is a random portfolio such that p ϕ(p) =1for every p =(p 1,p 2 ), and and i n N(0,σ 2 n). p 1 [ ϕ 1 (p) ϕ 1 (p)] = ε

101 When following heuristic D or B subjects hands do not tremble. therefore write We U D (p) =u(1/(p 1 + p 2 )) and U B (p) =max{πu(ω)+(1 π)u((1 p 1 ω)/p 2 )), πu(1 p 2 ω)/p 1 )) + (1 π)u(0)}

102 Estimation The probability of choosing heuristic k = D, S, B(ω) is given by a standard logistic discrete choice model: Pr(heuristic τ p; β, ρ, σ) = e βu τ P e βu k k=d,s,b where U D, U S and U B is the payoff specification for heuristic D, S and B(ω), respectively.

103 The ˆβ estimates are significantly positive, implying that the TMM has some predictive power. Distinguish systematic behavior from what appear to be mistakes and identify heuristics when they occur. There is a strong correlation between the estimated risk parameters from the EU, loss/disappointment and TMM estimations.

104 The distribution of the individual Arrow-Pratt measures (TMM) TMM Fraction of subjects

105 The distribution of the individual Arrow-Pratt measures (OLS) OLS Fraction of subjects

106 Goodness-of-fit Compare the choice probabilities predicted by the TMM and empirical choice probabilities. Nadaraya-Watson nonparametric estimator with a Gaussian kernel function. The empirical data are supportive of the TMM model (fits best in the symmetric treatment).

107 Discussion Suppose there are states of nature and associated Arrow securities and that the agent s behavior is represented by the decision problem max s.t. u (x) x B (p) A where B (p) is the budget set and A is the set of portfolios corresponding to the various archetypes the agent uses to simplify his choice problem. The only restriction we have to impose is that A is a pointed cone (closed under multiplication by positive scalars), which is satisfied if A is composed of any selection of archetypes except the Centroid.

108 We can derive the following properties of the agent s demand: 1. Let p k denotes the k-th observation of the price vector and x k arg max n u (x) :x B ³ p k A o denotes the associated portfolio. GARP. Then the data n p k, x ko satisfy 2. There exists a utility function u (x) such that for any price vector p, x arg max {u (x) :x B (p) A} x arg max {u (x) :x B (p)}.

109 x 2 A x 1 = x 2 Reveled preferences True preferences x 1

110 Takeaways [1] Classical economics assumes that decisions are based on substantive rationality, and has little to say about the procedures by which decisions are reached. [2] Rather than focusing on the consistency of behavior with non-eut theories, we study the fine-grained details of individual behaviors in search of clues to procedural rationality. [3] The switching behavior that is evident in the data leads us to prefer an alternative approach one that emphasizes standard preferences and procedural rationality.

111 Individual-level data

112 Individual-level data A review of the full data set reveals striking regularities within and marked heterogeneity across subjects. A quick look at the individual-level data indicates that whatever is going on is not consistent with the standard interpretation of EUT. Examples that reveal some of the unexpected features of the data and illustrate the role of heuristics.

113 Diagonal

114 Boundary

115 Smooth

116 Diagonal and boundary

117 Diagonal, boundary and smooth

118 Diagonal, boundary and smooth

119 Center Vertex

120 Centroid (budget shares) Edge

121 Bisector Center and bisector

122 Edge and bisector Center, vertex, and edge

123 Vertex and edge Center and bisector