Agenda

Michael Jordan
University of California, Berkeley

Abstract

Contract theory is the study of economic incentives when parties transact in the presence of private information, such that prior distributions may differ and not be common knowledge. We augment classical contract theory to incorporate a role for learning from data, where the overall goal of the adaptive mechanism is to obtain desired statistical behavior. We consider applications of this framework to problems in federated learning, the delegation of data collection, and principal-agent regulatory mechanisms.

Discussants: David Dunson and Chris Holmes.

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This lecture is included in the programme of the 2024 ISBA World Meeting

For further information please contact isba2024@unive.it