STINMALE: Strategic Interactions with Machine-Learning Algorithms: The Role of Simple Beliefs

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One of the salient recent technological developments has been the growing role of automated decision-making, based on machine learning (ML) algorithms. Examples include online content provision, product pricing, credit scoring and autonomous driving. When modeling strategic interactions between human agents, economists conventionally use game theory, which assumes that agents pursue well-defined objectives with a correct understanding of causal and statistical regularities in their environment. When some agents are ML algorithms, we need to find new, analytically tractable ways to model how they interact with humans or among themselves. My aim in this project is to develop such theoretical methodologies and examine their implications in economic settings such as oligopolistic competition, credit markets or online content provision, including potential implications for market regulation.The cornerstone of my theoretical approach is the observation that ML algorithms are "simplicity seeking". They attempt to predict outcomes from a sample that contains data about observable characteristics, and they overcome the overfitting problem (namely, complex estimated models' tendency toward poor out-of-sample predictions) by penalizing complex models. Simplicity is also an aspect of "explainability" of ML algorithms - an important criterion for enhancing users' willingness to interact with such algorithms.I plan to formulate notions of equilibrium behavior in strategic and market interactions that incorporate simplicity seeking as a criterion in the formation of equilibrium beliefs. One notion will focus on the sample-based selection of predictive models, while another will focus on explainability as a criterion for selecting models that is traded off against their predictive accuracy. I will apply these new equilibrium concepts to economic settings such as credit markets with adverse selection, dynamic trust games and oligopoly pricing, congestion games and online content provision.