Short Program

Introduction to Decision Making and Uncertainty

Introduction to Decision Making and Uncertainty

June 28-July 23, 2021

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.

June 28-July 2 Human-machine interaction systems Thaleia Zariphopoulou
Mathematics and McCombs Business School, University of Texas at Austin
July 5-9 Behavioural finance Xunyu Zhou
IEOR, Columbia University
Markov decision processes with dynamic risk measures: optimal control and learning Andrzej Ruszczynski
Rutgers Business School
July 12-16 Optimal transport and machine learning Marcel Nutz
Statistics, Columbia University
Machine learning and resource allocation Xin Guo
IEOR, Berkeley
July 19-23 Models for climate change with ambiguity and misspecification concerns Lars Hansen
Economics, University of Chicago
Games with ambiguity Peter Klibanoff
Kellogg School, Northwestern University

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