IEOR8100: Economics, AI, and Optimization Lecture 1: Introduction and Examples

The course will examine three different areas of game theory and economics. Each area will have real-life applications that have been deployed. So why AI and optimization? A common theme underlying all the areas that we will study is that for each area, one or more of the real applications are enabled by AI and optimization. In particular, we will repeatedly see that economic solution concepts often have some underlying convex or mixed-integer formulation of the problem, that allows us to compute solutions. Furthermore, most applications will require scaling at a level where standard optimization methods are not enough. In those settings, AI methods such as abstraction or machine learning are often used. For example, we may have a game that is way too large to even fit in memory. In that case, we can generate some coarse-grained representation of the problem using abstraction or machine learning. This coarse-grained representation is then typically what we solve with optimization methods.