Regret & decision making

1. 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

2. Regret & decision making • Regret may explain shifts effects (the tendency to make riskier choices to avoid losses than to achieve gains)

3. 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

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

5. Representative heuristic • In the lake there are more boats or sailing boats? Children (7-y old): sailing boats

6. 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)

7. 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.

8. 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

9. 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

10. 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.

11. 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)

12. 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)