Learning When to Quit: An Empirical Model of Experimentation

Emanuele Tarantino, Timothy S. Simcoe and Bernhard Ganglmair ()

No 12733, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: We study a dynamic model of the decision to continue or abandon a research project. Researchers improve their ideas over time and also learn whether those ideas will be adopted by the scientific community. Projects are abandoned as researchers grow more pessimistic about their chance of success. We estimate the structural parameters of this dynamic decision problem using a novel data set that contains information on both successful and abandoned projects submitted to the Internet Engineering Task Force (IETF), an organization that creates and maintains internet standards. Using the model and parameter estimates, we simulate two counterfactual policies: a cost-subsidy and a prize-based incentive scheme. For a fixed budget, subsidies have a larger impact on research output, but prizes perform better when accounting for researchers' opportunity costs.

Keywords: Learning; Experimentation; Standardization; Dynamic discrete choice (search for similar items in EconPapers)
JEL-codes: D83 O31 O32 (search for similar items in EconPapers)
Date: 2018-02
New Economics Papers: this item is included in nep-ict and nep-ppm
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Citations: View citations in EconPapers (3)

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Working Paper: Learning When to Quit: An Empirical Model of Experimentation (2018)
Working Paper: Learning When to Quit: An Empirical Model of Experimentation (2018)
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