Recombinant Search

Arjada Bardhi and Steven Callander

No 21350, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: We develop a model of directed Bayesian search over a multi-dimensional landscape of available ideas, consisting of two fields of knowledge as well as their combinations. Success of ideas is represented by the sample paths of a Brownian staple, an extension of the Brownian motion framework of Callander (2011) to higher dimensions. We characterize prediction and optimal search by a sequence of short-lived researchers. Prediction is complex: predicting the outcome of any novel idea generically requires considering the entire set of previously explored ideas. Derivative research plays a pivotal role in mitigating such complexity. We demonstrate that optimal frontier search is gradual, advancing at most one field at a time by combining a familiar idea from one field with a novel idea from another. These search dynamics align with observed patterns in patent innovation, drug discovery, and scientific citations.

JEL-codes: C61 D81 D83 O31 (search for similar items in EconPapers)
Date: 2026-04
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