Performative Prediction: A New Approach to Social Science?
Speaker: Jamie Michelson
Abstract: In the past decade major advances have been made in machine learning and statistics by explicitly modeling the feedback loop between a model or algorithm and its outcome. Despite this growing body of work (variously called ‘performative prediction’ or ‘strategic classification’), these ideas have been slow to reach applied social science. Yet, paradoxically, social scientists and philosophers of science have been grappling with the same ideas for over a century. This talk aims to give an overview of performative prediction from the perspective of the social sciences and philosophy of science. The goals of this talk are twofold. First, to summarize the developments of neighboring scientific disciplines for statisticians and computer scientists so they can better understand the historical importance of these questions. Second, to argue that while there are two distinct approaches to performative prediction, only one represents a revolutionary new paradigm in modeling social phenomena. No prior knowledge of the social sciences or philosophy of science is assumed. Two major works in this area--"Performative Prediction" (Perdomo et al. 2020) and "Strategic Classification" (Hardt et al. 2016)--will loosely serve as the technical foundation for the talk.