Regret & decision making

• What is regret? It’s– a negative emotion– Stems from a comparison of outcomes

• there is a choice that we did not take. had we decided differently our present situation could be better

– Anticipated regret: regret to potential outcome • if I cheat on my wife and she finds out I will regret it. Thus, I

don’t cheat• If I gamble and lose I will regret it, so I go for the sure bet.• People try to minimize regret

Regret & decision making

• Regret may explain shifts effects (the tendency to make riskier choices to avoid losses than to achieve gains)

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Violations of expected utility theory

• Framing effects

• Endowment effect:– Penn: give up right to minor lawsuits for a

discount– NJ additional cost for getting right to minor

lawsuit

• Sunk-cost effect: an action that has resulted in a loss is continued

Conterfactual thinking

• Conterfactual: What could have been– Roese (1997) Psych Bull, 121, 133-148

• Representative heuristic– In the lake there are more boats or sailing

boats? Children (7-y old): sailing boats

Computational Modeling Approachto Decision Making

• 1. Measurement of Preference • Decision making theories begin with the concept of a preference relation.• A, B, C are alternatives (or options)

– Gambles, Cars, Jobs, Houses, Medical Treatments• A p B means A is preferred or indifferent to B• Preference relations can be measured by • Choice (choose between A or B)• Certainty Equivalents (what is the dollar equivalent of each option)• Ratings (rate how strongly you like each option on a 10 point scale)• Different Measures of Preference do not always yield the same order• Producing preference reversals • e.g.,• Gamble A: .95 chance of winning $4 vs. nothing• Gamble B: .60 chance of winning $16 vs. .4 chance of losing $8• Choice Frequency A > Choice Frequency B• Certainty Equivalent for B > Certainty Equivalent for A• (see Slovic & Tversky, 1993, for a review)• Most theorists believe that choice is the most basic measure of preference (see Luce, 2000)•

Computational Modeling Approachto Decision Making

• 2. Conflict and the Probabilistic Nature of Preference • Suppose a person is given a choice between two options that are

approximately equal in weighted average value, inducing some type of conflict.

• The same pair of options is presented on two different occasions.• The probability of making an inconsistent choice is .33. In other

words, the person changes his or her mind 1 out of 3 times! (see, e.g., Starmer, 2000)

• The test – retest (within one week) correlation for selling prices is generally below .50 (less than 25% predictable across time). (Hershey & Schoemaker, 1989)

• This is a ubiquitous property of human behavior, but standard utility theories consider it an irrational aspect of human choice.

Computational Modeling Approachto Decision Making

• 3. Biological and Evolutionary Explanations for the Probabilistic Choice

• Exploratory Behavior – We need to continuously learn about uncertain probabilities of

payoffs in a changing, non-stationary environment.

• Unpredictable Behavior– We do not want our competitors to be able to perfectly predict

our behavior and use this to take advantage of us.

• Dynamic Motivational Systems– Our needs or goals change over time like hunger, thirst, sex

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Computational Modeling Approachto Decision Making

• 4. Psychological Explanations for Probabilistic Choice

• Fundamental Preference Uncertainty – We have fuzzy beliefs and uncertain values.

• Constructive Evaluations– We need to construct evaluations online, and the

frame may change, and attention may fluctuate.• Changing Strategies

– Using different choice rules can change preferences•

Computational Modeling Approachto Decision Making

•5. Implications for Standard Utility Theory

• Suppose we assume: Choose A over B A p B u(A) > u(B)– What problems does this generate?

• MaCrimmon (1968) asked 38 business managers to respond to 3 sets of choices, and 8 managers exhibited intransitivity’s. Should we reject utility theory?

• Absolutely not (e.g.,says Luce, 2000) these are just errors. After all, the nature of choice is probabilistic.

• Thus, standard utility theorists define• A p B Pr[ A | {A,B} ] .50• In the end, standard utility models are actually founded on probabilistic choice

assumptions. Axioms must be tested using statistical models.• But why is a .00002 change in probability from .49999 to .50001 more important than

a .48 change in probability from .51 to .99 or .49 to .01?• A model that accounts for the entire continuous range of probabilities is superior to

one that only accounts for two categories [0,.5) vs (.5, 1] of probabilities. •

Computational Modeling Approachto Decision Making

• 6. Decisions take time• Decision time is systematically related to choice probability

– Petrusic and Jamieson (1978)– Dror, Busemeyer, & Baselo al (1999)

• Choice probabilities become more extreme with longer deliberations– Simonson (1989) compromise effect– Dhar (2000) attraction effect

• Preferences can be reversed under time pressure – Edlund and Svenson (1993) – Diederich (2000)

• Preferences are dynamically inconsistent (Plans are not followed)– Ainslie (1975)– Busemeyer et al. (2000)– Trope et al (2002)

Computational Modeling Approachto Decision Making

• 7. Goals of Computational Models of Choice • Explain how conflicts are resolved

– the deliberation process described by William James• Account for the entire continuous range of choice probabilities [0,1]

– Not simply categorize whether they are above or below 50%– Explain paradoxical choice behavior

• Account for other manifestations of choice – Choice response time– Confidence Ratings

• Account for other manifestations of preference– Certainty equivalents– Buying or selling prices

• Explain the origins of weights and values• Build on principles from both cognitive psychology and neuro-psychology• Examples

– Decision Field Theory (Busemeyer & Townsend, 1993)– Neural Computational Model of Usher & McClelland (2002)– Constraint Satisfaction model of Guo and Holyoak (2002)