Artificial Intelligence and Scientific Discovery: A Model of Prioritized Search / Ajay K. Agrawal, John McHale, Alexander Oettl.

Format:

Book

Author/Creator:

Agrawal, Ajay K.

Contributor:

National Bureau of Economic Research.

McHale, John.

Oettl, Alexander.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31558.

NBER working paper series no. w31558

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

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.

Notes:

Print version record

August 2023.