Microfoundations of Algorithmic decisions

When theorizing the causal effects that algorithmic decisions have on a population, an important modeling choice arises. We can model the change to a population in the aggregate, or we can model the response to a decision rule at the individual level. Standard economic microfoundations, for instance, ground the response in the utility-maximizing behavior of individuals.

Providing context from sociological and economic theory, I will argue why this methodological problem is of significant importance to machine learning. I will focus on the relationships and differences between two recent lines of work, called strategic classification and performative prediction. While performative prediction takes a macro-level perspective on distribution shifts induced by algorithmic predictions, strategic classification builds on standard economic microfoundations. Based on work with Meena Jagadeesan and Celestine Mendler-Dünner, I will discuss the serious shortcomings of standard microfoundations in the context of machine learning and speculate about the alternatives that we have.