Abstract
In this paper we examine the performance of theories of decision making under uncertainty/ambiguity from the perspective of their descriptive and predictive power. To this end, we employ an innovative experimental design which enables us to reproduce ambiguity in the laboratory in a transparent and non-probabilistic way. We find that judging theories on the basis of their theoretical appeal, or on their ability to do well in terms of estimation, is not the same as judging them on the basis of their predictive power. We find that the models that perform better in an aggregate sense are Gilboa and Schmeidler's MaxMin and MaxMax Expected Utility Models, and Ghiradarto et al.'s Alpha Model, implying that more elegant theoretical models do not perform as well as relatively simple models. This suggests that decision-makers, when confronted with a difficult problem, try to simplify it, rather than apply a sophisticated decision rule.
| Original language | English |
|---|---|
| Pages (from-to) | 81-111 |
| Number of pages | 31 |
| Journal | Journal of Risk and Uncertainty |
| Volume | 41 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - Oct 2010 |
Keywords
- Ambiguity
- Bingo blower
- Choquet expected utility
- Decision field theory
- Decision making
- (Subjective) expected utility
- (Gilboa and Schmeidler) MaxMin EU
- (Gilboa and Schmeidler) MaxMax EU
- (Ghirardato) alpha model
- MaxMin
- MaxMax
- Minimum regret
- Prospect theory
- Uncertainty
- AMBIGUITY
- RISK
- AVERSION
- UTILITY
- MODEL