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Seeing the Forest for the Trees? An Investigation of Network Knowledge

Author

Listed:
  • Arun G Chandrasekhar
  • Alireza Tahbaz-Salehi

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.

Suggested Citation

  • Arun G Chandrasekhar & Alireza Tahbaz-Salehi, 2018. "Seeing the Forest for the Trees? An Investigation of Network Knowledge," Working Papers id:12573, eSocialSciences.
    • Handle: RePEc:ess:wpaper:id:12573
    Note: Institutional Papers
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    Cited by:

    1. Lori Beaman & Niall Keleher & Jeremy Magruder & Carly Trachtman, 2021. "Urban Networks and Targeting: Evidence from Liberia," AEA Papers and Proceedings, American Economic Association, vol. 111, pages 572-576, May.
    2. Edoardo Gallo & Joseph Lee & Yohanes Eko Riyanto & Erwin Wong, 2023. "Cooperation and Cognition in Social Networks," Papers 2305.01209, arXiv.org.
    3. Mira Frick & Ryota Iijima & Yuhta Ishii, 2018. "Dispersed Behavior and Perceptions in Assortative Societies," Cowles Foundation Discussion Papers 2128R2, Cowles Foundation for Research in Economics, Yale University, revised Oct 2021.
    4. Nathan Canen & Jacob Schwartz & Kyungchul Song, 2020. "Estimating local interactions among many agents who observe their neighbors," Quantitative Economics, Econometric Society, vol. 11(3), pages 917-956, July.
    5. Simone Cerreia-Vioglio & Roberto Corrao & Giacomo Lanzani, 2020. "Robust Opinion Aggregation and its Dynamics," Working Papers 662, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    6. Battigalli, Pierpaolo & Panebianco, Fabrizio & Pin, Paolo, 2023. "Learning and selfconfirming equilibria in network games," Journal of Economic Theory, Elsevier, vol. 212(C).
    7. Zenou, Yves & Bochet, Olivier & Faure, Mathieu & Long, Yan, 2020. "Perceived Competition in Networks," CEPR Discussion Papers 15582, C.E.P.R. Discussion Papers.
    8. Crès, Hervé & Tvede, Mich, 2022. "Aggregation of opinions in networks of individuals and collectives," Journal of Economic Theory, Elsevier, vol. 199(C).

    More about this item

    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.;
    All these keywords.

    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • D8 - Microeconomics - - Information, Knowledge, and Uncertainty
    • D85 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Network Formation
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation
    • O1 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development

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