Working Paper: NBER ID: w31558
Authors: Ajay K. Agrawal; John McHale; Alexander Oettl
Abstract: We model a key step in the innovation process, hypothesis generation, as the making of predictions over a vast combinatorial space. Traditionally, scientists and innovators use theory or intuition to guide their search. Increasingly, however, they use artificial intelligence (AI) instead. We model innovation as resulting from sequential search over a combinatorial design space, where the prioritization of costly tests is achieved using a predictive model. We represent the ranked output of the predictive model in the form of a hazard function. We then use discrete survival analysis to obtain the main innovation outcomes of interest – the probability of innovation, expected search duration, and expected profit. We describe conditions under which shifting from the traditional method of hypothesis generation, using theory or intuition, to instead using AI that generates higher fidelity predictions, results in a higher likelihood of successful innovation, shorter search durations, and higher expected profits. We then explore the complementarity between hypothesis generation and hypothesis testing; potential gains from AI may not be realized without significant investment in testing capacity. We discuss the policy implications.
Keywords: Artificial Intelligence; Innovation; Hypothesis Generation; Predictive Models
JEL Codes: O31; O33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI-driven models that produce higher fidelity predictions (C45) | likelihood of successful innovation (O36) |
| higher fidelity predictions (C53) | search duration (C41) |
| higher fidelity predictions (C53) | expected profit (D33) |
| predictive model (C52) | probability of innovation (O35) |
| search duration (C41) | expected profit (D33) |
| testing capacity (C99) | potential gains from AI (O31) |
| improving prediction fidelity (C53) | re-ranking of potential designs (C52) |
| testing decisions (sequential vs. parallel) (C52) | innovation output (O36) |