Inattentive Inference

Format:Print
| Language:English

Abstract

This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning someone’s effort from their observable performance may require accounting for the otherwise irrelevant role of luck. This creates an attribution problem common to all information structures with multiple causes. We report controlled experimental evidence for pervasive overinference about states that affect utility—a form of “omitted variable bias” in belief updating—providing an explanation for a collection of well-known but previously unconnected misattribution patterns. In studying why systematic misattribution arises, we consistently find that errors are not due to excessive task complexity or effort avoidance. Instead, people form incomplete mental models of the information structure and fail to notice the need to account for alternative causes. These mental models are not stable but context-dependent: misattribution responds to a variety of attentional manipulations, but not to changes in the costs of inattention.