Recombinant Search
Paper joint with Steven Callander
Abstract:
As emphasized in Weitzman (1998), the search for novel ideas, technologies, and products is often combinatorial: familiar ideas are combined in unfamiliar ways to advance the stock of knowledge. We develop a model of directed Bayesian search over a rich multi-dimensional landscape of available ideas, some within a single field of knowledge and others combining distinct fields. The success of combinations is modeled through the sample paths of the Brownian staple, a natural extension of the Brownian motion framework of Callander (2011) to higher dimensions. We characterize the optimal search dynamics by a sequence of short-lived researchers. Recombinant search is fundamentally different from search within a single field. It is gradual and proceeds in a grid of existing knowledge: it pushes the frontier of at most one field at a time, combining a familiar idea from one field with an unfamiliar one in the other. The analysis illuminates the important role of derivative research—that is, intensive search that does not aim to advance the frontier of knowledge—in facilitating innovative research at the frontier. Such dynamics of optimal recombinant search resonate with stylized facts about the evolution of technology codes in patent innovation.