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Preference Robust Optimization with Quasi-Concave Choice Functions for Multi-Attribute Prospects
Preference robust choice models concern decision-making problems where the decision maker's (DM) utility/risk preferences are ambiguous and the evaluation is based on the worst-case utility function/risk measure from a set of plausible utility functions/risk measures.
Quantitative Statistical Robustness for Tail-Dependent Law Invariant Risk Measures
When estimating the risk of a financial position with empirical data or Monte Carlo simulations via a tail-dependent law invariant risk measure such as the Conditional Value-at-Risk (CVaR), it is important to ensure the robustness of the statistical estimator particularly when the data contain noise.
Preference Elicitation and Robust Optimization with Multi-Attribute Quasi-Concave Choice Functions
Decision maker's preferences are often captured by some choice functions which are used to rank prospects.
