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Daniel N Hauser, Ph.D.

Aalto University, Finland

Authors: Daniel N. Hauser, J. Aislinn Bohren

Abstract: A growing literature in economics seeks to model how agents process information and update beliefs. In this paper, we link two common approaches: (i) defining an updating rule that specifies a mapping from prior beliefs and the signal to the agent’s subjective posterior, and (ii) modeling an agent as a Bayesian learner with a misspecified model. The updating rule approach has a more transparent conceptual link to the underlying bias being modeled, while the misspecified model approach is ‘complete’, in that no further assumptions on belief-updating are necessary to analyze the model, and has well-developed solution concepts and convergence results. We show that any misspecified model can be decomposed into two objects that summarize the biases it introduces: the updating rule captures how the agent interprets realized information, while the forecast captures how the agent anticipates future information. We derive necessary and sufficient conditions for a forecast and updating rule pair to be represented by a misspecified model. This provides conceptual guidance for which model to select to represent a given bias. Finally, we consider two natural ways to select forecasts: introspection-proofness and naive consistency. We demonstrate how introspection-proofness places a natural bound on the magnitude of bias in an application with motivated reasoning, and how naive consistency impacts a firm’s ability to screen consumers in a credit market application.

Keywords: Model misspecification, belief formation, non-Bayesian updating, heuristics

Full Text: The Behavioral Foundations of Model Misspecification: A Decomposition