Rapid Learning and Adaptive Search in Complex Environments: How Underestimating Noise in Performance Feedback Can Leverage and Resolve Errors of Commission

We investigate how managers’ assumptions about noise in performance feedback impact adaptive search in complex environments. Using computer simulation, we demonstrate that rapid learners—managers who underestimate feedback noise and, therefore, revise their beliefs more aggressively—make more commission errors early on but can quickly self-correct these errors through additional experimentation. Over time, rapid learners reduce commission errors by increasingly detecting feedback with a high signal-to-noise ratio, and this prompts resampling and belief refinement, allowing them to outperform even unbiased Bayesian learners in environments that are of greater complexity. In contrast, cautious learners—managers who overestimate feedback noise and, therefore, take longer to infer confidently the superiority of better alternatives—make fewer commission errors but more omission errors. Cautious learners perform poorly in complex environments but are effective in simpler search spaces, in which sensitivity to subtle performance differences is advantageous. Our findings provide boundary conditions for the learning literature’s recommendation of gradual belief updating under uncertainty. Specifically, we show that underestimating feedback noise—and, thus, learning rapidly—can strike an effective balance: it encourages broad exploration early on, enabling the refinement of beliefs as managers encounter and repeatedly sample exceptionally high-performing solutions.