Prospect Theory

1. Prospect Theory Gun-woong Lee March 30, 2012

2. Agenda • Prospect Theory (PT) • Theoretical Background of PT • Expected Utility Theory vs. PT • Key Properties of PT • Evaluation • PT in the IS Domain • PT applications • Conclusion

3. Decision Making under Risk • Keywords • Uncertainty and risk • Choice between alternatives with outcomes and probabilities • Certainty vs. Gamble • Risk averse vs. Risk seeking • Approaches • Prescriptive: How decisions under uncertainty should be made. • Descriptive: How decisions are actually made.

4. Expected Utility Theory (EUT) • History • Proposed by Nicholas Bernoulli in 1713 • Sophisticated von Neumann and Morgenstern • Decision Making • Choice among alternative as through maximizing expected utility. • Expected Utility • EU= value*probability • Assumption • Rational decision maker: Decision maker is able to preferentially rank the outcomes and express this ranking in a numerical fashion. • Axioms • Completeness • Transitivity • Independence • Continuity

5. Overview Of Prospect Theory (PT) • History • Developed by Kahneman and Tversky (Econometrica, 1979) • The second most cited paper in economics • Motivation • Why people exhibit patterns of preference which appear incompatible with expected utility theory? • Main Idea • A psychologically-based framework that describes how the mental processes involved when people make a choice under risk. • Defines a relative “value function” • Prospect = Value * Decision weight • Assumptions • Gains and losses are relative to a reference point • People make a decision based on the potential values of gains and losses from a reference point (Status quo) rather than the final outcome. • People evaluate these gains and losses using heuristic approaches.

6. - Source: Dwicedi, Y., Wadem, M. and Schneberger, (2012) Information Systems Theory: Explaining and Predicting Our Digital Society, Springer.

7. Prospect Theory vs. Expected Utility Theory

8. Value Function • “People evaluate value functions for gains and losses separately.” • Gains • Concavity: Risk aversion • Diminishing marginal utility • U[$200-$100] > U[$1,200-$1,100] • Losses • Convexity: Risk taking • Diminishing marginal utility • Loss Aversion • Steeper for losses than for gains • The displeasure associated with losses is greater than the pleasure associate with the same amount of gains.

9. Decision Weights • “Psychologically people use different weighting schemes when evaluating outcomes” • Low Probabilities (overweighed) • The curve is steep • People are very sensitive to the difference between “impossible: and “possible” • Moderate Probabilities (underweighted) • The curve is too flat • People are under-influenced by changes in moderate probabilities • High Probabilities • The curve is steep (underweighted) • People are very sensitive to the difference between “certain” and “not certain”

10. Implications of Decision Weight (DW) • DW explains “certain effect” • People prefer an option that reduces the probability from .01 to .00 over from .02 to .01 • People prefer a 100% chance of getting $100 over a 95% chance of getting $120. • DW explains “pseudo-certainty effect” • “An insurance policy that protects against fire and flood can be presented as 1) one that provides 100% protection against fire and none against flood or 2) a 50% protection against both”

11. Framing Effects http://www.youtube.com/watch?v=Ng9V2JneJ68

12. Framing Effects • Choices can be framed in the positive (gains) or in the negative(losses) terms • PT suggests that in the certain circumstances, it is the way the choice are frames that is most critical. • Mortality vs. survival; Cash Discount vs. Credit Card Surcharge

13. Violation of Dominance • IF prospect A is at least as good as prospect B in every respect and better than B in at least one respect, then A should be preferred to B. • Choose between: • E: 25% chance to win $240 and 75% chance to lose $760 • F: 25% chance to win $250 and 75% chance to lose $750 • First examine both decisions, then indicate the options you prefer. • Choose between: • A: a sure gain of $240 • B: 25% chance to gain $1000 and 75% chance to gain nothing • Choose between: • C:a sure loss of $750 • D:75% chance to lose $1000 and 25% chance to lose nothing

14. Violation of Invariance

15. Evaluation • Is Prospect theory much better than expected utility theory? • Is it the best comprehensive description of the decision making process?

16. Limitations • The theory was developed for one-shot gambles. • The theory may predict inaccurate outcomes as is does not account for such factors as decision context and decision maker’ characteristics. • There is no technical or theoretical reasons why the theory cannot (or can) be applied beyond monetary gambles. • It assumes some level of sophistication by the decision maker • It is underspecified for complex decision making

17. An Empirical Study of Software Project Bidding • Motivation • What is the impact of an early price indication on the final bid? • How to explain the variations in bidding prices in the final bid. • Expectation • An early price indication to lead to lower bids (anchoring effect) • Results • Companies involved in the pre-study phase increase the bids in the bidding phase. • Companies involved in the pre-study phase tended to provide much higher bids than companies in the bidding phase. • None of the company-related variables were able to explain the differences in bids between the companies

18. An Empirical Study of Software Project Bidding • Use of Prospect Theory (Lose Aversion) • Bidding is something to lose (i.e., losses) • The risk of losing money in a high uncertainty situation is attributed a higher importance than the possibility of winning money. • Company will undercompensate for a decreased level of uncertainty • Different level of Uncertainty • The pre-study phase • High-uncertainty situation (incomplete description of requirement) • The bidding phase • Lower-uncertainty situation (complete description of requirement) • Overcompensate for high uncertainty • The companies added a risk premium that overcompensated for the level of uncertainty

19. Expectation Disconfirmation and Technology Adoption • Motivation • Methodological and analytical limitations in EDT research in IS. • Direct measurement of confirmation • It distorts the joint impact of components measures on various outcomes • Limited to linear model • It leads to oversimplifying the complexity of the joint effect (Attitudes and behaviors results from the congruence between expectation and experience) • Objective • Present a polynomial modeling and response surface methodology • Develop a polynomial model of expectation-disconfirmation in IS (curvilinear) • Empirically validate the proposed model

20. Expectation Disconfirmation and Technology Adoption • Use of Prospect Theory • Lose Aversion • Intensity of dissonance between pre- and post- exposure attitude (something to lose) • Behavior intention (outcomes) • The degree of disconfirmation increases the negative effect on behavioral intention to continue using a system becomes strong. • Hypotheses • H2a: Behavior intention decreases at a fast rate as the post-exposure usefulness increases and pre-exposure usefulness decreases. • H2b: Behavioral intention decreases at faster rate as the pre-exposure usefulness increase and post-exposure usefulness decreases.

21. Conclusion • PT represents a great improvement over classical EUT. • Many violations of EUT are explicitly predicted. • PT has produced new insights and predictions of human behavior in decision making. • PT is relatively NEW and not universal. • New hypotheses and constructs that use PT as a base will help researchers to better understand why and how we decide what we do.

22. Revisiting Framing Effect… • Framing Effect ( with moderate / high probabilities) • Risk averse in face of gain • Risk seeking in face of loss • Framing Effect (with low probabilities) • Risk seeking in face of gain • Risk averse in face of loss • WHY??