The Streetlight Effect in Data-Driven Exploration
Summary
Question. Does more data about past attempts help or hurt the search for breakthroughs?
Approach. A model of the "streetlight effect", in which data that reveals an enticing but suboptimal project narrows exploration; a pre-registered laboratory experiment; and data on research into the genetic origins of human diseases, using exogenous genetic overlap between humans and laboratory mice as an instrument for which genes get studied first.
Finding. In the experiment, revealing the value of an enticing project lowers payoffs and reduces breakthrough discoveries, because subjects free-ride on the data others generate and less new data is produced. In genetics research, diseases with early evidence of promising genetic targets are 16 percentage points less likely to yield breakthroughs than diseases where early efforts failed. Competition attenuates the effect but does not eliminate it.
Why it matters. Access to data is usually assumed to speed discovery. The paper identifies the conditions under which it makes researchers look under the lamppost instead, which bears on how scientific data and research funding are organized.
Abstract
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 then apply our theory in the context of scientific research into the genetic origins of human diseases, focusing on the drivers of limited exploration. 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 analysis of this phenomenon, outlining the conditions under which data leads agents to look under the lamppost rather than engage in socially beneficial exploration.
How to cite
Hoelzemann, Johannes, Gustavo Manso, Abhishek Nagaraj, and Matteo Tranchero (2026). The Streetlight Effect in Data-Driven Exploration. Working paper.
@unpublished{hoelzemann2026streetlight,
author = {Hoelzemann, Johannes and Manso, Gustavo and Nagaraj, Abhishek and Tranchero, Matteo},
title = {The Streetlight Effect in Data-Driven Exploration},
note = {Working paper},
year = {2026}
}