# 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.

## Presentation on theme: "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."— Presentation transcript:

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)

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 Approach to 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 Approach to 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 Approach to 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

Computational Modeling Approach to 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 Approach to 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 Approach to 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 Approach to 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)

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