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2255. Optimization Models for Cumulative Prospect Theory under Incomplete Preference Information
Invited abstract in session MD-11: Behavioral Decision Analysis III, stream Behavioural OR.
Monday, 14:30-16:00Room: 12 (building: 116)
Authors (first author is the speaker)
| 1. | Juuso Liesiö
|
| Department of Information and Service Management, Aalto University School of Business | |
| 2. | Peng Xu
|
| Southampton Business School, University of Southampton |
Abstract
Prospect stochastic dominance conditions can be used to compare pairs of uncertain decision alternatives when the decision makers' choice behavior is characterized by cumulative prospect theory, but their preferences are not precisely specified. This paper extends the use of prospect stochastic dominance conditions to decision settings in which the use of pairwise comparisons is not possible due to the high or possibly infinite number of decision alternatives. In particular, we first establish equivalence results between these conditions and the existence of solutions to a specific system of linear inequalities. We then utilize these results to develop stochastic optimization models whose feasible solutions are guaranteed to dominate a specified benchmark distribution. These models can be used to identify if there exists a decision alternative within a set that is preferred to a given benchmark by all decision makers with an S-shaped value function and a pair of inverse S-shaped probability weighting functions. As such the models offer a flexible tool to conduct behavioral analyses in decision settings such as portfolio selection, procurement optimization or inventory management. We demonstrate the application of these models using real return data on industry portfolios.
Keywords
- Behavioural OR
- Decision Analysis
- Programming, Mixed-Integer
Status: accepted
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