Accounting for the adverse impact of "non-average" events has become essential in many applications involving decision making under uncertainty. Its implementation through decision models, namely stochastic programs, requires careful measurement of risk that reflects one's concern about uncertain outcomes. Important theories such as convex risk measures outline conditions required for risk measurement but provide little guidance for cases not meeting the conditions. Unfortunately, such cases are more than common in real-life situations. In particular, in this talk, we study cases where the distribution required by a law invariant risk measure is not available and/or the risk preference required by a risk measure cannot be identified. We aim to provide theoretical, computational, and empirical evidence that in these cases optimization can be a powerful tool to measure risk in a systematic fashion that is hard to achieve otherwise. Applications to operation management and finance will be presented.
Jonathan Yu-Meng Li is an assistant professor in the Telfer School of Management at University of Ottawa, Canada. His research focuses on the interplay between optimization theory (stochastic, robust, and inverse optimization, as well as hybrids thereof) and risk theory and its application in risk management. Jonathan holds a PhD from the University of Toronto in operations research.
*Light refreshments will be served at 2:30pm
200 University Avenue West
Waterloo, ON N2L 3G1