The Streetlight Effect in Data-Driven Exploration

We study exploration under uncertainty and show how access to data on past attempts can paradoxically hinder breakthrough discovery. We develop a model of the “streetlight effect” demonstrating that when data highlights attractive but ultimately suboptimal projects, it can narrow exploration and suppress innovation. In a laboratory experiment, we find that revealing the value of an enticing project lowers payoffs and reduces breakthrough discoveries. This drop stems from increased free-riding behavior, which crowds out the generation of new data. We validate our theory in the context of scientific research into the genetic origins of human diseases. To identify the causal impact of past data, we use an instrumental variable that leverages exogenous genetic overlaps between humans and laboratory mice, which reduces research costs for specific genes and leads to prioritized data collection about them. We find that diseases with early evidence of promising genetic targets are 16 percentage points less likely to yield breakthroughs than those where early efforts failed. While competition attenuates the streetlight effect, it does not eliminate it. Our paper provides the first systematic analysis of this phenomenon, outlining the conditions under which data leads agents to look under the lamppost rather than engage in socially beneficial exploration.