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Learning When to Quit: An Empirical Model of Experimentation

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

Working Papers from eSocialSciences

Abstract: The paper studies 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. It estimates 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, it also simulates 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: eSS; learning; Internet Engineering Task Force (IETF); internet standard; parameters; counterfactual policies; cost-subsidy; prize-based incentive scheme; decision; research report; scientific community; novel data. (search for similar items in EconPapers)
Date: 2018-03
Note: Institutional Papers
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Working Paper: Learning When to Quit: An Empirical Model of Experimentation (2018) Downloads
Working Paper: Learning When to Quit: An Empirical Model of Experimentation (2018) Downloads
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