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Microfoundations of Algorithmic decisions
Moritz Hardt

Tue Dec 14 10:45 AM -- 11:12 AM (PST) @
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)

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