Data and Code for: Mental Models and Learning: The Case of Base-Rate Neglect

Are systematic biases in decision making self-corrected in the long run when agents are accumulating feedback informative of optimal behavior? This paper focuses on a canonical updating problem where the dominant deviation from optimal behavior is base-rate neglect. Using a laboratory experiment, we document persistence of suboptimal behavior in the presence of feedback. Using diagnostic treatments, we study the mechanisms hindering learning from feedback. We investigate the generalizability of these results to other settings by also studying long-run behavior in a voting problem where failure to condition on being pivotal generates suboptimal behavior. Our findings provide insights on what types of mistakes should be expected to be persistent in the presence of feedback. Our results suggest mistakes are more likely to be persistent when they are driven by incorrect mental models that miss or misrepresent important aspects of the environment. Such models induce confidence in initial answers, limiting engagement with and learning from feedback. These results have implications for how policies should be designed to counteract behavioral biases. The paper uses two data sets. The first consists of 455 subjects who participated in laboratory experiments conducted at UC Santa Barbara and UC San Diego. The second data set consists of 520 prolific participants.