Decision Behavior

1. Class Session: Alternative Perspectives on Risky Decisions Decision Behavior John W. Payne BA 525 Fall, 2009

2. Outline for Today • Alternative Perspectives on Risk Taking: Emotion and Motivational Factors in Risky Decisions • Affect and the Weighting Function of Prospect Theory • Regret Theories • Risk taking and the Positive (Negative) Side of Emotion – some results from “Brain Studies” • Cultural Aspects of Risk Taking • Papers for class

3. Affect and Decision Weights: Rottenstreich & Hsee (2001) - 1 • Classic form of the weighting function – the impact of a given change in probability diminishes with its distance from impossibility and certainty. • 1% chance of $200 = $10 for sure, implying a $10 value for the first hundredth improvement in probability • 99% chance of $200 = $188 for sure, implying a value of $12 for the last hundredth of probability. • The “average” value for the 98 intermediate hundreds is therefore only about $1.82 per hundredth, i.e., $178/98. • Is there an interaction between the affective value of the prize (consequence) and the shape of the weighting function?

4. Affect and Decision Weights: Rottenstreich & Hsee (2001) - 2 • Hypothesis: Bigger impacts on going from impossible to possible (hope) and from certain to probable (fear) with less sensitivity to intermediate probabilities for consequences that are more affective. • Results • CE(1%, $500 European Trip Coupon) = $20 • CE(99%, $500 European Trip Coupon) = $450 • CE (1%, $500 Tuition Coupon) = $5 • CE (99%, Tuition Coupon) = $478 • Same pattern for avoidance of elective shock versus a $20 cash penalty - $7 and $10 (shock conditions) versus $1 and $18 for the cash. • Affect versus Cognition – Separate Processes? • Affect corrected by cognition?

5. Affect and Context – Bateman et al. (JBDM, 2007) • 7/36 chances of winning $9.00 else win nothing. How attractive on a 0 to 20 (extremely attractive) scale? (9.4) • 7/36 chances of winning $9.00 else lose $.05. How attractive on a 0 to 20 (extremely attractive) scale? (14.9). • Lose $.25 (11.7). • How about versus getting $2 for sure? • 61% gamble (-$.05) versus 33% prefer gamble with win nothing. • On the other hand, joint evaluation – base = 13.09 (win nothing) vs. 9.82 for lose $.05. Dominance detected? • The “evaluability” of $9 is enhanced by the presence of a $.05 loss in a single or separate evaluation. • The importance of contextual driven affect in the construction of preference. • People who are more “numerate” show this effect to a greater extent.

6. Emotions and Risk Taking • The findings on emotions on risk-taking are mixed. Sad individuals, for example, tend to prefer high-risk/high-reward options. Anxious individuals tend to prefer safer options. • Beliefs tend to assimilate towards the emotional state, e.g., a negative mood increases the level of perceived risk. • Angry people tend to have more optimistic expectations about the future than sad or fearful people. • A positive relationship (rather than negative) between perceived benefits and risks? • Studies of brain damage to regions that subserve emotion can lead to worse and better risky choice (see Bechara, et. al, 1997; Shiv et. al, 2005).

7. Emotional Reactions and Risk Preferences • Does the ability to feel emotional reactions to the outcome • of a risky decision increase or decrease “rational” responses? Damasio Target participants with lesions in brain related to the processing of emotions. Preference for more risk or more extreme gain. Poorer decisions.

8. Shiv (2005 Method) • At the beginning of the task, all participants were endowed with $20 of play money, which they were told to treat as real because they would receive a gift certificate for the amount they were left with at the end of the study. • Participants were told that they would be making several rounds of investment decisions and that, in each round, they had to decide between two options: invest $1 or not invest. On each round, if the participant decided not to invest, he or she would keep the dollar, and the task would advance to the next round. • If the participant decided to invest, he or she would hand over a dollar bill to the experimenter. The experimenter would then toss a coin in plain view. If the outcome of the toss were heads (50% chance), then the participant would lose the $1 that was invested; if the outcome of the toss were tails (50% chance), then $2.50 would be added to the participant's account. The task would then advance to the next round. • The task consisted of 20 rounds of investment decisions, and the different groups of participants took roughly the same time on the task.

9. Shiv (2005 Results) • Does the ability to feel emotional reactions to the outcome of a risky decision increase or decrease “rational” responses? Target participants with lesions in brain related to the processing of emotions. They did better. Target participants where not impacted by whether the prior outcome was a gain or a loss. Fig. 1. Percentage of rounds in which participants decided to invest $1.

10. 3th. Moment Preferences and Reference Levels *With negative skewness the median is greater than the mean (EV). More often good but more extreme bad. Positive skewness is the opposite.

11. Motivational Factors in Decisions: Larrick (1993) - 1 • Risk aversion as more than just a combination of probabilities and values but also involving such motivational factors as the fear of failure and regret. • Regret Theory – Post decision emtions. • Comparison against what might have been • A = $8 for sure vs. B = ($12, .67, 0) • Potential regret with A = ($12-$8) or 4 • Potential regret with B = ($8-$0) or 8 • Since regret is increasing in magnitude, 8 is more than twice the regret of 4. • Expected Regret = .67x4 for option A and .33x8 for option B which given that 8 is more than twice 4 than the expected regret for B is greater. • Possibility of context dependent preferences – what is the alternative?

12. Motivational Factors in Decisions: Larrick (1993) - 2 • Individual differences in motivational states • Atkinson’s Need for Achievement Theory • People undertake actions that will maximize feelings of achievement. • Achievement is a function of the difficulty of the task, greater achievement when probability is less. • Goal is to maximize feeling of achievement by maximizing probability of success X satisfaction, of when Prob. Success = .50. • Individual differences may exist in terms of avoiding failure versus achieving success. Extremes – either prob. Success = 1.0 or near 0 (no sense of loss) • Focus on skill tasks, e.g., basketball.

13. Motivational Factors in Decisions: Larrick (1993) - 3 • Lopes model of need for security versus hope, a form of rank dependent preferences. • Role of feedback • How might choice change if one knew the outcome of the foregone alternative? • Will people avoid knowledge to maintain image as a decision-maker? • Will people select what others select to minimize feelings of regret? • What happens if one has to explain a choice to others/ • How does the motivational processes interact with the more cognitive processes?

14. Cross-cultural differences in risk perception and attitudes: Weber & Hsee (1998) - 1 • Value – Risk models of decisions under risk • WTP for gamble = f(Value,Risk) with it generally assumed that Value – b*Risk. • Often Value = the EV of a gamble and Risk = the variance of returns. B is an individual difference variable reflecting the tradeoff between value and risk. • Assuming value judgments are similar, individual differences in risk taking could be due to either different values of b or different perceptions of Risk.

15. Cross-cultural differences in risk perception and attitudes: Weber & Hsee (1998) - 2 • Evidence for cross-cultural differences in risk preferences, e.g., US students more risk averse than Chinese students. • Hsee and Weber’s Cushion hypothesis for collectivism vs. individualism cultures. • Study and Results • Provide WTP amounts for sets of 12 gambles (twice) and followed by perceived risk judgments on a 0 to 100 (extremely risky) scale. • Participants from China, US, Germany, & Poland • Test-retest correlations were .78 for WTP judgments and .69 for risk judgments. • All subjects were risk averse • The least risk averse were Chinese, Polish, German, and then American. • The greatest risk was seen in the opposite order. • A model that included the subjective risk judgments outperformed an EV + Variance model in predicting WTP amounts. • American subjects had the largest negative coefficient for risk while Chinese subjects had the smallest. • Implications for negotiations and other tasks?

16. Wrap Up: Decisions under Risk • Expected Utility Models • Prospect Theory • Moment models • Information Processing Theories involving the risk dimensions (PW, $W, PL, $L) or Attention on potential and fear (upside outcomes & loss or worst outcomes). • Emotional and Motivational Factors • Cultural Effects • Implications for regulatory design?