Inattentive Inference
研究人们从包含无关状态的信息结构中推断世界状态时，如何产生系统性归因偏差，并通过实验发现这种偏差源于不完整的心理模型而非认知能力不足。
Abstract This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning from a customer review about a product’s quality requires accounting for the reviewer’s otherwise-irrelevant taste. 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 various misattribution patterns. In studying why systematic misattribution arises, we consistently find that errors are not due to deliberate effort avoidance or a lack of cognitive capacity. Instead, people behave as if they 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.