`

Timezone: »

 
Microfoundations of Algorithmic decisions
Moritz Hardt

Tue Dec 14 10:45 AM -- 11:12 AM (PST) @ None
in Learning and Decision-Making with Strategic Feedback (StratML) »

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.

Author Information

Moritz Hardt (University of California, Berkeley)

More from the Same Authors