Familiarity and Choice

Familiarity and Choice

Francesco Cerigioni

Universitat Pompeu Fabra - Barcelona GSE

October 14, 2016

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Familiarity and Automatic Psychological Processes?

Implications for Markets?

I Part 1:Familiarity and Analogical Thinking.(Dual Process Theory)

I Part 2:Familiarity, Analogies and Noise Trading.

I Part 3: Familiarity and Endogenous Preferences.(Mere Exposure Effect)

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Dual Process Theory

The individual as interaction of two systems.

I Associative System (System 1):Effortless, Automatic and always running, i.e. Parallel.Analogical reasoning. Fast thinking, intuition.

I 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)

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Dual Process Theory

The individual as interaction of two systems.I Associative System (System 1):

Effortless, Automatic and always running, i.e. Parallel.Analogical reasoning. Fast thinking, intuition.



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Dual Process Theory





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Dual Process Theory





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Research Questions

Observed choices 6= maximization of individual preferences.

Market outcomes 6= models equilibria?

Intuition and markets?

But first...I What makes choices intuitive?

Answer: a behavioral model.I How can we distinguish conscious and intuitive choices?

Answer: an algorithm.

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Research Questions

Observed choices 6= maximization of individual preferences.Market outcomes 6= models equilibria?





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Research Questions






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Research Questions



But first...

I What makes choices intuitive?Answer: a behavioral model.

I How can we distinguish conscious and intuitive choices?Answer: an algorithm.

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Research Questions






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Research Questions




Answer: a behavioral model.

I How can we distinguish conscious and intuitive choices?Answer: an algorithm.

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Research Questions






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Research Questions






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The Idea

General modeling idea:

Every time the DM faces a decision problem:I S1 compares the decision environment with past ones.

If there are some that are similar enough, past behavior is replicated.Source of intuitive choices.

I Otherwise, S2 chooses the best available action.Source of conscious choices.

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The Idea

General modeling idea:Every time the DM faces a decision problem:

I S1 compares the decision environment with past ones.If there are some that are similar enough, past behavior is replicated.

Source of intuitive choices.I Otherwise, S2 chooses the best available action.

Source of conscious choices.

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The Idea


I S1 compares the decision environment with past ones.If there are some that are similar enough, past behavior is replicated.Source of intuitive choices.


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The Idea



I Otherwise, S2 chooses the best available action.

Source of conscious choices.

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The Idea




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Why is this relevant?

I Economic Relevance:New framework. Coexistence of sticky and adaptive behavior.A possible explanation of different phenomena.

I Noise Trading and Underreaction.I Generics Prescription Behavior.I Product Differentiation and First Mover Advantage.I Aggregate Consumption Smoothness vs Individual Variance.I etc.

I Relevance per se:Alternative approach to run revealed preference analysis.Richer Data. Centrality of time.

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The Model: Some Notation

I Set of actions X.

I 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. . .

I Decision problem at t: menu and decision environment, i.e. (At, et).Chosen action at.

I Obviously, at ∈ At ⊆ X and et ∈ E.

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I Set of actions X.I 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. . .



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I Set of actions X.I 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. . .I Decision problem at t: menu and decision environment, i.e. (At, et).

Chosen action at.I Obviously, at ∈ At ⊆ X and et ∈ E.

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I Set of actions X.I 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. . .



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Dual Decision (DD) Processes

I S1. Two components:I Similarity function σ : E × E → [0, 1]I Similarity threshold α ∈ [0, 1].

I 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.

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I S2. Preference relation �.

For every t we have:

at =



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I S2. Preference relation �.For every t we have:

at =



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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).

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Revelation Strategy


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).

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Revelation Strategy


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).

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Revealing S2

New ObservationAn observation t is New whenever at 6= at′ for all t′ < t.

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Two Useful Definitions

Unconditional FamiliarityThe unconditional familiarity of observation t isf(t) = max

s<t,as∈At

σ(et, es).

Conditional FamiliarityThe conditional familiarity of observation t isf(t|at) = max

s<t,as=at

σ(et, es).

Ex-Ante vs Ex-Post.

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s<t,as∈At

σ(et, es).


s<t,as=at

σ(et, es).

Ex-Ante vs Ex-Post.

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s<t,as∈At

σ(et, es).


s<t,as=at

σ(et, es).

Ex-Ante vs Ex-Post.

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Linked Observations

Linked ObservationsWe say that observation t is linked to observation s, and we writet ∈ L(s), whenever f(t|at) ≤ f(s). We say that observation t isindirectly linked to observation s if there exists a sequence ofobservations t1, . . . , tk such that t = t1, tk = s and ti ∈ L(ti+1) for everyi = 1, 2, . . . , k − 1.

The mechanism:if s is generated by S2 (S1) and t ∈ L(s) (s ∈ L(t)), then t is generatedby S2 (S1) too.

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Linked Observations

Linked ObservationsWe say that observation t is linked to observation s, and we writet ∈ L(s), whenever f(t|at) ≤ f(s). We say that observation t isindirectly linked to observation s if there exists a sequence ofobservations t1, . . . , tk such that t = t1, tk = s and ti ∈ L(ti+1) for everyi = 1, 2, . . . , k − 1.

The mechanism:if s is generated by S2 (S1) and t ∈ L(s) (s ∈ L(t)), then t is generatedby S2 (S1) too.

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Revealing S1

CycleA 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 CycleAn observation t is Least Novel in a Cycle whenever it maximizes theunconditional familiarity among the observations in the cycle.

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Revealing S1

CycleA 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 CycleAn observation t is Least Novel in a Cycle whenever it maximizes theunconditional familiarity among the observations in the cycle.

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Revealing S1 and S2

Let:I DN = All observations indirectly linked to new observations.I DC = All observations to which least novel in a cycle are indirectly linked.

Proposition 1For every collection of observations generated by a DD process:

I all decisions in DN are generated by S2 and all decisions in DC aregenerated by S1,

I if x is indirectly revealed preferred to y for the set of observationsDN , that is xR(DN )y, then x � y,

I maxt∈DN

f(t) ≤ α < mint∈DC

f(t|at).

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Revealing S1 and S2

Let:I DN = All observations indirectly linked to new observations.I DC = All observations to which least novel in a cycle are indirectly linked.

Proposition 1For every collection of observations generated by a DD process:

I all decisions in DN are generated by S2 and all decisions in DC aregenerated by S1,

I if x is indirectly revealed preferred to y for the set of observationsDN , that is xR(DN )y, then x � y,

I maxt∈DN

f(t) ≤ α < mint∈DC

f(t|at).

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Related Literature

Two Selves ModelsI Models à la Strotz (e.g. Gul and Pesendorfer(2001)):

Different Preferences vs Different SelvesSimilarity

I Case-based DT (Gilboa and Schmeidler(1995)):Always Max vs Sometimes Max

Behavioral DatasetI Welfare Relevant Domain (Bernheim and Rangel (2009), Apesteguia

and Ballester (2015))

+

Model-based (Rubinstein and Salant (2012), Masatlioglu, Nakajimaand Ozbay (2012), Manzini and Mariotti (2014))

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In the paper...

Other things analyzed in the paper:I Falsifiability. Axiomatic Characterization.I Memory.I Partial Knowledge of the Similarity.I Identifying the Similarity. Stickiness in an heterogeneous population.

Main Implication: coexistence sticky and adaptive behavior.

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In the paper...

Other things analyzed in the paper:I Falsifiability. Axiomatic Characterization.I Memory.I Partial Knowledge of the Similarity.I Identifying the Similarity. Stickiness in an heterogeneous population.

Main Implication: coexistence sticky and adaptive behavior.

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Analogies and Trading

Traders described by a DD process.

I S1: Analogies between market environments.For example, dividends.

I 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).

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Traders described by a DD process.I S1: Analogies between market environments.

For example, dividends.

I 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).

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For example, dividends.I 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).

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Similarity threshold distributed in the population.

Main Mechanism: For any change in the environment. . .Some traders perceive it (S2), some don’t (S1).

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Similarity threshold distributed in the population.Main Mechanism: For any change in the environment. . .Some traders perceive it (S2), some don’t (S1).

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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.

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Related Literature

I Barberis, Shleifer and Vishny (1998).Representative investor with wrong beliefs.

I Daniel, Hirshleifer and Subrahmanyam (1998).Overconfident traders.

I Hong and Stein (1999).News watchers and momentum traders.

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Take-Home Message

Main Message:Familiarity with market environment can have important and predictable

effects on market prices.

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Mere Exposure Effect

People tend to develop a preference for things merely because they havebeen 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?


I Dynamic status-quo bias. Home bias?I New insights into relationship between preferences and choices.

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The Model

As before:I Set of actions X.I Menu at t, At ⊆ X, chosen action at t, at ∈ At.

New:I u : X → R++, is the basic utility of alternative x.I f : R+ → R+ is the exposure function. (f(0) = 0,f ′ ≥ 0, f ′′ ≤ 0.)

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The Model


New:I u : X → R++, is the basic utility of alternative x.

I f : R+ → R+ is the exposure function. (f(0) = 0,f ′ ≥ 0, f ′′ ≤ 0.)

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The Model


New:I u : X → R++, is the basic utility of alternative x.I f : R+ → R+ is the exposure function. (f(0) = 0,f ′ ≥ 0, f ′′ ≤ 0.)

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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 fsuch 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 andalternative y have been chosen up until t.

Whenever f(r) = 0 for all r ∈ R, standard Luce Model.

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Dynamic Endowment Effect

Main Implication: Stochastic and Dynamic Endowment Effect. (Chew,Shen and Zhong (2015))

I If a DM follows a EBLM then he experiences the endowment effect.(Samuelson and Zeckhauser (1988))

I If a DM follows a EBLM then the endowment effect increases withexposure.(Strahilevitz and Lowenstein (1998))

I If a DM follows a EBLM, the number of alternatives dominating aparticular alternative x cannot increase every time alternative x ischosen.

I A naïve DM that follows a EBLM experiences loss aversion.(Kahneman, Knetsch and Thaler (1991))

I A naïve DM that follows a EBLM experiences present bias.(O’Donoghue and Rabin (1999))

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Dynamic Endowment Effect

Main Implication: Stochastic and Dynamic Endowment Effect. (Chew,Shen and Zhong (2015))

I If a DM follows a EBLM then he experiences the endowment effect.(Samuelson and Zeckhauser (1988))

I If a DM follows a EBLM then the endowment effect increases withexposure.(Strahilevitz and Lowenstein (1998))

I If a DM follows a EBLM, the number of alternatives dominating aparticular alternative x cannot increase every time alternative x ischosen.

I A naïve DM that follows a EBLM experiences loss aversion.(Kahneman, Knetsch and Thaler (1991))

I A naïve DM that follows a EBLM experiences present bias.(O’Donoghue and Rabin (1999))

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Exposure and Heterogeneity

New insights into opportunities and choice behavior. Home bias?

Consider linear EBLM (Lin-EBLM), that is f(r) = kr.

Limiting HeterogeneityAn homogeneous population choosing from X following a Lin-EBLM willshow heterogeneous behavior as t goes to infinity. The limitingdistribution of choice probabilities will be a Dirichlet distribution withparameters equal to the utilities of the different alternatives. That is, forany alternative x ∈ X, the probability pt(x|X) as t goes to infinity, willbe distributed following a Beta distribution with parameters equal tou(x) and

∑y 6=x u(y).

Falsifiability?In the paper: Axiomatic Characterization.

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New insights into opportunities and choice behavior. Home bias?Consider linear EBLM (Lin-EBLM), that is f(r) = kr.


∑y 6=x u(y).


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∑y 6=x u(y).

Falsifiability?

In the paper: Axiomatic Characterization.

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∑y 6=x u(y).


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Conclusions

Familiarity can be important for market behavior...

I ...because it affects analogical thinking.Analogical thinking can have huge impact on markets.

I ...because it affects perception of alternatives.Exposure to alternatives greatly affects choice behavior.

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Conclusions

Familiarity can be important for market behavior...I ...because it affects analogical thinking.

Analogical thinking can have huge impact on markets.

I ...because it affects perception of alternatives.Exposure to alternatives greatly affects choice behavior.

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Conclusions

Familiarity can be important for market behavior...I ...because it affects analogical thinking.

Analogical thinking can have huge impact on markets.I ...because it affects perception of alternatives.

Exposure to alternatives greatly affects choice behavior.

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THANK YOU!!!!

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Economy & Finance

Familiarity and Choice