Solving complex problems often requires exploring multiple approaches, especially when a solution is unknown. However, the collective risk of failure likely increases when solvers concentrate on a narrow set of approaches. In this paper, we develop a simple model to argue that markets dominated by a few large-scale experimenters---firms that launch multiple experiments---produce greater diversity in approaches than markets with many small-scale experimenters. This finding challenges the conventional wisdom that having many distinct experimenters leads to many distinct approaches. We test our model's predictions using data from pharmaceutical R&D. We define an experiment as a pre-clinical trial, a market as a therapeutic class--year, and an approach as the choice of target. Our estimates suggest that a unit increase in the average number of experiments conducted per firm in a market results in an increase of over three standard deviations in the diversity of targets explored, even while controlling for the total number of firms and experiments. Furthermore, a one-standard-deviation increase in target diversity corresponds to a 25.9 percentage point increase in the likelihood that at least one experiment progresses to Phase 1 clinical trials. Our findings have implications for technology policy, highlighting the importance of optimizing the allocation of experiments across firms to maximize diversity and the likelihood of success at the market level.
