How do we make decisions in the face of risk? The need to make decisions in the presence of uncertainty cuts across a wide range of issues in science and human behavior. The underlying problems require both sophisticated modeling and advanced mathematical and statistical approaches and techniques.

This program will serve as an introduction to the long program on Decision Making and Uncertainty scheduled for Spring 2022. It aims to introduce participants to a variety of modeling questions and methods of current interest in this area. It will be built on “thematic clusters” of emerging areas of application.

Each cluster will begin with tutorial lectures on the first day followed by supporting lectures on mathematical and statistical topics related to the underlying theme. There will also be panel discussions, together with poster sessions and short presentations by the participants.

The intended audience is researchers interested in mathematical modeling and methods applicable to decision making under uncertainty in economics, finance, business, and other areas. Advanced Ph.D. students, postdocs, and junior faculty are especially encouraged to apply.

The program covers a diverse set of topics and each theme will be self-contained. Given the variety of both the applications and the methods, participants are encouraged to attend the entire program. Basic knowledge in probability, stochastics, and statistics is required.

The planned clusters are as follows.

DatesTopicOrganizer(s)
June 28-July 2Foundations of stochastic optimization, Dynamic Programming, and Hamilton-Jacobi-Bellman equationsThaleia Zariphopoulou (Mathematics and McCombs Business School, University of Texas at Austin)
July 5-7Optimal transport and machine learningMarcel Nutz (Statistics, Columbia University)
July 8-9Time-inconsistent and relaxed stochastic optimization, and applicationsXunyu Zhou (IEOR, Columbia University)
July 12-16Markov decision processes with dynamic risk measures: optimal control and learningTomasz Bielecki (IIT) and Andrzej Ruszczynski (Rutgers Business School)
Machine learning and Mean Field GamesXin Guo (IEOR, Berkeley)
July 19-23Models for climate change with ambiguity and misspecification concernsLars Hansen (Economics, University of Chicago)
Games with ambiguityPeter Klibanoff (Kellogg School, Northwestern University)