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Familiarity and Choice

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Francesco Cerigioni (UPF and Barcelona GSE)

Publié dans : Économie & finance
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Familiarity and Choice

  1. 1. Familiarity and Choice Francesco Cerigioni Universitat Pompeu Fabra - Barcelona GSE October 14, 2016 1 / 27
  2. 2. Familiarity and Choice Familiarity and Automatic Psychological Processes? Implications for Markets? 2 / 27
  3. 3. Familiarity and Choice Familiarity and Automatic Psychological Processes? Implications for Markets? Part 1: Familiarity and Analogical Thinking. (Dual Process Theory) 2 / 27
  4. 4. Familiarity and Choice Familiarity and Automatic Psychological Processes? Implications for Markets? Part 1: Familiarity and Analogical Thinking. (Dual Process Theory) Part 2: Familiarity, Analogies and Noise Trading. 2 / 27
  5. 5. Familiarity and Choice Familiarity and Automatic Psychological Processes? Implications for Markets? Part 1: Familiarity and Analogical Thinking. (Dual Process Theory) Part 2: Familiarity, Analogies and Noise Trading. Part 3: Familiarity and Endogenous Preferences. (Mere Exposure Effect) 2 / 27
  6. 6. Dual Process Theory The individual as interaction of two systems. 3 / 27
  7. 7. Dual Process Theory The individual as interaction of two systems. Associative System (System 1): Effortless, Automatic and always running, i.e. Parallel. Analogical reasoning. Fast thinking, intuition. 3 / 27
  8. 8. Dual Process Theory The individual as interaction of two systems. Associative System (System 1): Effortless, Automatic and always running, i.e. Parallel. Analogical reasoning. Fast thinking, intuition. Analytical System (System 2): Effortful, Controlled and Rule-Governed. Analytical reasoning. Slow thinking, consciousness. 3 / 27
  9. 9. Dual Process Theory The individual as interaction of two systems. Associative System (System 1): Effortless, Automatic and always running, i.e. Parallel. Analogical reasoning. Fast thinking, intuition. Analytical System (System 2): Effortful, Controlled and Rule-Governed. Analytical reasoning. Slow thinking, consciousness. Old theory: Schneider and Shiffrin (1977), Evans (1977), Mc Andrews et al. (1987)... More recently: Sanfey et al. (2006), Evans and Frankish (2009), Kahneman (2011) 3 / 27
  10. 10. Research Questions Observed choices = maximization of individual preferences. 4 / 27
  11. 11. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? 4 / 27
  12. 12. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? 4 / 27
  13. 13. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? But first... 4 / 27
  14. 14. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? But first... What makes choices intuitive? 4 / 27
  15. 15. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? But first... What makes choices intuitive? Answer: a behavioral model. 4 / 27
  16. 16. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? But first... What makes choices intuitive? Answer: a behavioral model. How can we distinguish conscious and intuitive choices? 4 / 27
  17. 17. Research Questions Observed choices = maximization of individual preferences. Market outcomes = models equilibria? Intuition and markets? But first... What makes choices intuitive? Answer: a behavioral model. How can we distinguish conscious and intuitive choices? Answer: an algorithm. 4 / 27
  18. 18. The Idea General modeling idea: 5 / 27
  19. 19. The Idea General modeling idea: Every time the DM faces a decision problem: S1 compares the decision environment with past ones. If there are some that are similar enough, past behavior is replicated. 5 / 27
  20. 20. The Idea General modeling idea: Every time the DM faces a decision problem: S1 compares the decision environment with past ones. If there are some that are similar enough, past behavior is replicated. Source of intuitive choices. 5 / 27
  21. 21. The Idea General modeling idea: Every time the DM faces a decision problem: S1 compares the decision environment with past ones. If there are some that are similar enough, past behavior is replicated. Source of intuitive choices. Otherwise, S2 chooses the best available action. 5 / 27
  22. 22. The Idea General modeling idea: Every time the DM faces a decision problem: S1 compares the decision environment with past ones. If there are some that are similar enough, past behavior is replicated. Source of intuitive choices. Otherwise, S2 chooses the best available action. Source of conscious choices. 5 / 27
  23. 23. Why is this relevant? Economic Relevance: New framework. Coexistence of sticky and adaptive behavior. A possible explanation of different phenomena. 6 / 27
  24. 24. Why is this relevant? Economic Relevance: New framework. Coexistence of sticky and adaptive behavior. A possible explanation of different phenomena. Noise Trading and Underreaction. Generics Prescription Behavior. Product Differentiation and First Mover Advantage. Aggregate Consumption Smoothness vs Individual Variance. etc. 6 / 27
  25. 25. Why is this relevant? Economic Relevance: New framework. Coexistence of sticky and adaptive behavior. A possible explanation of different phenomena. Noise Trading and Underreaction. Generics Prescription Behavior. Product Differentiation and First Mover Advantage. Aggregate Consumption Smoothness vs Individual Variance. etc. Relevance per se: Alternative approach to run revealed preference analysis. Richer Data. Centrality of time. 6 / 27
  26. 26. The Model: Some Notation Set of actions X. 7 / 27
  27. 27. The Model: Some Notation Set of actions X. Set of decision environments E. e ∈ E: possible relevant characteristics of the choice problem. 7 / 27
  28. 28. The Model: Some Notation Set of actions X. Set of decision environments E. e ∈ E: possible relevant characteristics of the choice problem. Example: menus (budget sets), i.e. E = 2X {∅}. 7 / 27
  29. 29. The Model: Some Notation Set of actions X. Set of decision environments E. e ∈ E: possible relevant characteristics of the choice problem. Example: menus (budget sets), i.e. E = 2X {∅}. Other possibilities: attributes, frames. . . 7 / 27
  30. 30. The Model: Some Notation Set of actions X. Set of decision environments E. e ∈ E: possible relevant characteristics of the choice problem. Example: menus (budget sets), i.e. E = 2X {∅}. Other possibilities: attributes, frames. . . Decision problem at t: menu and decision environment, i.e. (At, et). Chosen action at. 7 / 27
  31. 31. The Model: Some Notation Set of actions X. Set of decision environments E. e ∈ E: possible relevant characteristics of the choice problem. Example: menus (budget sets), i.e. E = 2X {∅}. Other possibilities: attributes, frames. . . Decision problem at t: menu and decision environment, i.e. (At, et). Chosen action at. Obviously, at ∈ At ⊆ X and et ∈ E. 7 / 27
  32. 32. Dual Decision (DD) Processes S1. Two components: Similarity function σ : E × E → [0, 1] Similarity threshold α ∈ [0, 1]. 8 / 27
  33. 33. Dual Decision (DD) Processes S1. Two components: Similarity function σ : E × E → [0, 1] Similarity threshold α ∈ [0, 1]. S2. Preference relation . 8 / 27
  34. 34. Dual Decision (DD) Processes S1. Two components: Similarity function σ : E × E → [0, 1] Similarity threshold α ∈ [0, 1]. S2. Preference relation . For every t we have: at = at for some t < t such that σ(et, et ) > α and at ∈ At the maximal element in At with respect to , otherwise. 8 / 27
  35. 35. Revelation Strategy How can we distinguish between S1 and S2? 9 / 27
  36. 36. Revelation Strategy How can we distinguish between S1 and S2? General idea behind the algorithm: Suppose I know some conscious (intuitive) observations. 9 / 27
  37. 37. Revelation Strategy How can we distinguish between S1 and S2? General idea behind the algorithm: Suppose I know some conscious (intuitive) observations. Observations that are relatively less similar (more similar) with their past ⇒ Conscious too (Intuitive too). 9 / 27
  38. 38. Revealing S2 New Observation An observation t is New whenever at = at for all t < t. 10 / 27
  39. 39. Two Useful Definitions Unconditional Familiarity The unconditional familiarity of observation t is f(t) = max s<t,as∈At σ(et, es). 11 / 27
  40. 40. Two Useful Definitions Unconditional Familiarity The unconditional familiarity of observation t is f(t) = max s<t,as∈At σ(et, es). Conditional Familiarity The conditional familiarity of observation t is f(t|at) = max s<t,as=at σ(et, es). 11 / 27
  41. 41. Two Useful Definitions Unconditional Familiarity The unconditional familiarity of observation t is f(t) = max s<t,as∈At σ(et, es). Conditional Familiarity The conditional familiarity of observation t is f(t|at) = max s<t,as=at σ(et, es). Ex-Ante vs Ex-Post. 11 / 27
  42. 42. Linked Observations Linked Observations We say that observation t is linked to observation s, and we write t ∈ L(s), whenever f(t|at) ≤ f(s). We say that observation t is indirectly linked to observation s if there exists a sequence of observations t1, . . . , tk such that t = t1, tk = s and ti ∈ L(ti+1) for every i = 1, 2, . . . , k − 1. 12 / 27
  43. 43. Linked Observations Linked Observations We say that observation t is linked to observation s, and we write t ∈ L(s), whenever f(t|at) ≤ f(s). We say that observation t is indirectly linked to observation s if there exists a sequence of observations t1, . . . , tk such that t = t1, tk = s and ti ∈ L(ti+1) for every i = 1, 2, . . . , k − 1. The mechanism: if s is generated by S2 (S1) and t ∈ L(s) (s ∈ L(t)), then t is generated by S2 (S1) too. 12 / 27
  44. 44. Revealing S1 Cycle A set of observations t1, t2, . . . , tk forms a cycle if ati+1 ∈ Ati , i = 1, . . . , k − 1 and at1 ∈ Atk , where all chosen alternatives are different. 13 / 27
  45. 45. Revealing S1 Cycle A set of observations t1, t2, . . . , tk forms a cycle if ati+1 ∈ Ati , i = 1, . . . , k − 1 and at1 ∈ Atk , where all chosen alternatives are different. Least Novel in a Cycle An observation t is Least Novel in a Cycle whenever it maximizes the unconditional familiarity among the observations in the cycle. 13 / 27
  46. 46. Revealing S1 and S2 Let: DN = All observations indirectly linked to new observations. DC = All observations to which least novel in a cycle are indirectly linked. 14 / 27
  47. 47. Revealing S1 and S2 Let: DN = All observations indirectly linked to new observations. DC = All observations to which least novel in a cycle are indirectly linked. Proposition 1 For every collection of observations generated by a DD process: all decisions in DN are generated by S2 and all decisions in DC are generated by S1, if x is indirectly revealed preferred to y for the set of observations DN , that is xR(DN )y, then x y, max t∈DN f(t) ≤ α < min t∈DC f(t|at). 14 / 27
  48. 48. Related Literature Two Selves Models Models à la Strotz (e.g. Gul and Pesendorfer(2001)): Different Preferences vs Different Selves Similarity Case-based DT (Gilboa and Schmeidler(1995)): Always Max vs Sometimes Max Behavioral Dataset Welfare Relevant Domain (Bernheim and Rangel (2009), Apesteguia and Ballester (2015)) + Model-based (Rubinstein and Salant (2012), Masatlioglu, Nakajima and Ozbay (2012), Manzini and Mariotti (2014)) 15 / 27
  49. 49. In the paper... Other things analyzed in the paper: Falsifiability. Axiomatic Characterization. Memory. Partial Knowledge of the Similarity. Identifying the Similarity. Stickiness in an heterogeneous population. 16 / 27
  50. 50. In the paper... Other things analyzed in the paper: Falsifiability. Axiomatic Characterization. Memory. Partial Knowledge of the Similarity. Identifying the Similarity. Stickiness in an heterogeneous population. Main Implication: coexistence sticky and adaptive behavior. 16 / 27
  51. 51. Analogies and Trading Traders described by a DD process. 17 / 27
  52. 52. Analogies and Trading Traders described by a DD process. S1: Analogies between market environments. For example, dividends. 17 / 27
  53. 53. Analogies and Trading Traders described by a DD process. S1: Analogies between market environments. For example, dividends. S2: Chooses the optimal portfolio. 17 / 27
  54. 54. Analogies and Trading Traders described by a DD process. S1: Analogies between market environments. For example, dividends. S2: Chooses the optimal portfolio. Similarity threshold distributed in the population. 17 / 27
  55. 55. Analogies and Trading Traders described by a DD process. S1: Analogies between market environments. For example, dividends. S2: Chooses the optimal portfolio. Similarity threshold distributed in the population. Main Mechanism: For any change in the environment. . . Some traders perceive it (S2), some don’t (S1). 17 / 27
  56. 56. Analogies and Empirical Regularities Risk (Shiller (1992)) Noise traders increase the risk in the economy. Underreaction (Cutler et al. (1991)) Prices underreact to changes in information in the short-run. Momentum (Jegadeesh and Titman (1993)) Prices show momentum. Overreaction (De Bondt and Thaler (1985)) Prices overreact to changes in information in the long-run. 18 / 27
  57. 57. Related Literature Barberis, Shleifer and Vishny (1998). Representative investor with wrong beliefs. Daniel, Hirshleifer and Subrahmanyam (1998). Overconfident traders. Hong and Stein (1999). News watchers and momentum traders. 19 / 27
  58. 58. Take-Home Message Main Message: Familiarity with market environment can have important and predictable effects on market prices. 20 / 27
  59. 59. Mere Exposure Effect People tend to develop a preference for things merely because they have been exposed to them. 21 / 27
  60. 60. Mere Exposure Effect People tend to develop a preference for things merely because they have been exposed to them. Very important effect for cognitive sciences. (Zajonc (1968), Pliner (1982), Gordon and Holyoak (1983), Bornstein and d’Agostino (1992), Monahan, Murphy and Zajonc (2000), Harmon-Jones and Allen (2001), Zajonc (2001), Huang and Hsieh (2013) and others). 21 / 27
  61. 61. Mere Exposure Effect People tend to develop a preference for things merely because they have been exposed to them. Very important effect for cognitive sciences. (Zajonc (1968), Pliner (1982), Gordon and Holyoak (1983), Bornstein and d’Agostino (1992), Monahan, Murphy and Zajonc (2000), Harmon-Jones and Allen (2001), Zajonc (2001), Huang and Hsieh (2013) and others). Main idea and question: individuals exposed to menus and choices, dynamic choice behavior? 21 / 27
  62. 62. Mere Exposure Effect People tend to develop a preference for things merely because they have been exposed to them. Very important effect for cognitive sciences. (Zajonc (1968), Pliner (1982), Gordon and Holyoak (1983), Bornstein and d’Agostino (1992), Monahan, Murphy and Zajonc (2000), Harmon-Jones and Allen (2001), Zajonc (2001), Huang and Hsieh (2013) and others). Main idea and question: individuals exposed to menus and choices, dynamic choice behavior? Why is this relevant? Dynamic status-quo bias. Home bias? New insights into relationship between preferences and choices. 21 / 27
  63. 63. The Model As before: Set of actions X. Menu at t, At ⊆ X, chosen action at t, at ∈ At. 22 / 27
  64. 64. The Model As before: Set of actions X. Menu at t, At ⊆ X, chosen action at t, at ∈ At. New: u : X → R++, is the basic utility of alternative x. 22 / 27
  65. 65. The Model As before: Set of actions X. Menu at t, At ⊆ X, chosen action at t, at ∈ At. New: u : X → R++, is the basic utility of alternative x. f : R+ → R+ is the exposure function. (f(0) = 0,f ≥ 0, f ≤ 0.) 22 / 27
  66. 66. The Model Simple model based on Luce (1959): Exposure Biased Luce Model (EBLM) p is an EBLM if there exist a basic utility u and an exposure function f such that ∀A ⊆ X: pt(x|A) = u(x) + f(nx) y∈A u(y) + f(ny) where nx and ny are the number of times that alternative x and alternative y have been chosen up until t. Whenever f(r) = 0 for all r ∈ R, standard Luce Model. 23 / 27
  67. 67. Dynamic Endowment Effect Main Implication: Stochastic and Dynamic Endowment Effect. (Chew, Shen and Zhong (2015)) 24 / 27
  68. 68. Dynamic Endowment Effect Main Implication: Stochastic and Dynamic Endowment Effect. (Chew, Shen and Zhong (2015)) If a DM follows a EBLM then he experiences the endowment effect. (Samuelson and Zeckhauser (1988)) If a DM follows a EBLM then the endowment effect increases with exposure. (Strahilevitz and Lowenstein (1998)) If a DM follows a EBLM, the number of alternatives dominating a particular alternative x cannot increase every time alternative x is chosen. A naïve DM that follows a EBLM experiences loss aversion. (Kahneman, Knetsch and Thaler (1991)) A naïve DM that follows a EBLM experiences present bias. (O’Donoghue and Rabin (1999)) 24 / 27
  69. 69. Exposure and Heterogeneity New insights into opportunities and choice behavior. Home bias? 25 / 27
  70. 70. Exposure and Heterogeneity New insights into opportunities and choice behavior. Home bias? Consider linear EBLM (Lin-EBLM), that is f(r) = kr. Limiting Heterogeneity An homogeneous population choosing from X following a Lin-EBLM will show heterogeneous behavior as t goes to infinity. The limiting distribution of choice probabilities will be a Dirichlet distribution with parameters equal to the utilities of the different alternatives. That is, for any alternative x ∈ X, the probability pt(x|X) as t goes to infinity, will be distributed following a Beta distribution with parameters equal to u(x) and y=x u(y). 25 / 27
  71. 71. Exposure and Heterogeneity New insights into opportunities and choice behavior. Home bias? Consider linear EBLM (Lin-EBLM), that is f(r) = kr. Limiting Heterogeneity An homogeneous population choosing from X following a Lin-EBLM will show heterogeneous behavior as t goes to infinity. The limiting distribution of choice probabilities will be a Dirichlet distribution with parameters equal to the utilities of the different alternatives. That is, for any alternative x ∈ X, the probability pt(x|X) as t goes to infinity, will be distributed following a Beta distribution with parameters equal to u(x) and y=x u(y). Falsifiability? 25 / 27
  72. 72. Exposure and Heterogeneity New insights into opportunities and choice behavior. Home bias? Consider linear EBLM (Lin-EBLM), that is f(r) = kr. Limiting Heterogeneity An homogeneous population choosing from X following a Lin-EBLM will show heterogeneous behavior as t goes to infinity. The limiting distribution of choice probabilities will be a Dirichlet distribution with parameters equal to the utilities of the different alternatives. That is, for any alternative x ∈ X, the probability pt(x|X) as t goes to infinity, will be distributed following a Beta distribution with parameters equal to u(x) and y=x u(y). Falsifiability? In the paper: Axiomatic Characterization. 25 / 27
  73. 73. Conclusions Familiarity can be important for market behavior... 26 / 27
  74. 74. Conclusions Familiarity can be important for market behavior... ...because it affects analogical thinking. Analogical thinking can have huge impact on markets. 26 / 27
  75. 75. Conclusions Familiarity can be important for market behavior... ...because it affects analogical thinking. Analogical thinking can have huge impact on markets. ...because it affects perception of alternatives. Exposure to alternatives greatly affects choice behavior. 26 / 27
  76. 76. THANK YOU!!!! 27 / 27

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