Information processing under imprecise risk with an insurance demand illustration
| Autor: | Jean-Yves Jaffray, Meglena Jeleva |
|---|---|
| Přispěvatelé: | DECISION, Laboratoire d'Informatique de Paris 6 (LIP6), Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS) |
| Rok vydání: | 2008 |
| Předmět: |
Decision support system
Operations research Computer science Applied Mathematics Decision tree 06 humanities and the arts 02 engineering and technology Subjective expected utility Decision rule 0603 philosophy ethics and religion Imprecise probability Deductible Theoretical Computer Science Artificial Intelligence 060302 philosophy 0202 electrical engineering electronic engineering information engineering Econometrics [INFO]Computer Science [cs] 020201 artificial intelligence & image processing Software Expected utility hypothesis Optimal decision |
| Zdroj: | International Journal of Approximate Reasoning International Journal of Approximate Reasoning, Elsevier, 2008, 49 (1), pp.117-129. ⟨10.1016/j.ijar.2007.06.013⟩ International Journal of Approximate Reasoning, 2008, 49 (1), pp.117-129. ⟨10.1016/j.ijar.2007.06.013⟩ |
| ISSN: | 0888-613X |
| DOI: | 10.1016/j.ijar.2007.06.013 |
| Popis: | International audience; This paper deals with the impact of information on the decisions of an agent whose beliefs are imprecise and whose preferences are not in accordance with the Subjective Expected Utility (SEU) model. We assume that his one-shot preferences are representable by a Hurwicz criterion with pessimism–optimism index α. We moreover assume that in a sequential decision making situation the decision maker acts according to the root dictatorship version of McClennen’s Resolute Choice model: he evaluates strategies at the root of the decision tree by the Hurwicz criterion and enforces the best strategy, thus behaving in a dynamically consistent manner. The use of Resolute Choice in an imprecise probability environment raises a general question: is information processed correctly in this model? To show that this question can be given a positive answer in standard cases (and also motivated by the accident-no accident variable in an automobile insurance context), we study the basic situation in which data are provided by the random sampling of a binary variable, and find the influence of the pessimism–optimism index on the optimal decisions to be decreasing with the sample size, the optimal decision rule only depending, asymptotically, on the relative frequencies observed. Then, we turn to the second question raised by the well known feature of Resolute Choice: non-consequentialism. Does the fact that the optimal decision rule may depend on unrealized outcomes necessarily lead to criticisable choices? We study a two-period insurance problem in which an individual has to choose his coverage at period two after observing the period one outcome (loss or no loss). It turns out that in the case where no loss happened, a seemingly irrelevant data – the first period deductible level – may influence the decision maker’s second period insurance choice. We analyse this result in relation with the existence and value of the pessimism–optimism degree. |
| Databáze: | OpenAIRE |
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