5 papers
Equilibrium Information Aggregation under Machine Learning
Andrew Ellis, Michele Piccione, Shengxing Zhang
We introduce a framework for studying the equilibrium effects of machine learning. Agents process information using a Chow and Liu (1968) tree, a widely-used machine learning proce…
Limited belief propagation and contingent thinking
Andrew Ellis, Ran Spiegler
An agent updates her beliefs over a set of variables after observing some of them. We provide a representation of updated beliefs that captures limited propagation of her observati…
Subjective Causality in Choice
Andrew Ellis, Heidi Christina Thysen
Choices based on observational data depend on beliefs about which correlations reflect causality. An agent predicts the consequence of available actions using a dataset and her sub…
Correlation Concern
Andrew Ellis
In many choice problems, the interaction between several distinct variables determines the payoff of each alternative. I propose and axiomatize a model of a decision maker who reco…
Choice with Endogenous Categorization
Andrew Ellis, Yusufcan Masatlioglu
We propose and axiomatize the categorical thinking model (CTM) in which the framing of the decision problem affects how agents categorize alternatives, that in turn affects their e…