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3732. Decision Support Model for Incomplete Risk-Seeking Preferences
Invited abstract in session TC-45: Integer Programming for Decision Support, stream Decision Support Systems.
Tuesday, 12:30-14:00Room: 30 (building: 324)
Authors (first author is the speaker)
| 1. | Peng Xu
|
| Southampton Business School, University of Southampton | |
| 2. | Tri Tran
|
| Department of Operations, University of Groningen |
Abstract
Risk-seeking behavior has been extensively documented across various research fields such as decision sciences, economics and finance, psychology. In this research, we propose a decision support model based on reverse second-order stochastic dominance (RSD) to accommodate incomplete risk-seeking preferences. Specifically, we establish dominance conditions according to the RSD criterion in discrete state-space. We then develop a stochastic optimization model that enables to identify an optimal decision alternative whose dominance over a pre-specified benchmark is robust for all risk-seeking decision makers. Furthermore, we demonstrate that RSD-based optimization model can be formulated as a mixed-integer linear programming problem to generate decision recommendations. The developed decision support model is well-suited to support data-driven decision analytics problems, including production and operations management, logistics and supply chain management, and healthcare management, particularly in the presence of incomplete preference information.
Keywords
- Decision Analysis
- Stochastic Optimization
- Programming, Mixed-Integer
Status: accepted
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