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A New Mechanism to Alleviate the Crises of Confidence in Science - With an Application to the Public Goods Game

Author

Listed:
  • Luigi Butera
  • Philip Grossman
  • Daniel Houser
  • John List
  • Marie-Claire Villeval
Abstract
Creation of empirical knowledge in economics has taken a dramatic turn in the past few decades. One feature of the new research landscape is the nature and extent to which scholars generate data. Today, in nearly every field the experimental approach plays an increasingly crucial role in testing theories and informing organizational decisions. Whereas there is much to appreciate about this revolution, recently a credibility crisis has taken hold across the social sciences, arguing that an important component of Fischer (1935)'s tripod has not been fully embraced: replication. Indeed, while the importance of replications is not debatable scientifically, current incentives are not sufficient to encourage replications from the individual researcher's perspective. We propose a novel mechanism that promotes replications by leveraging mutually beneficial gains between scholars and editors. We develop a model capturing the trade-offs involved in seeking independent replications before submission of a paper to journals. We showcase our method via an investigation of the effects of Knightian uncertainty on cooperation rates in public goods games, a pervasive and yet largely unexplored feature in the literature.

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  • Luigi Butera & Philip Grossman & Daniel Houser & John List & Marie-Claire Villeval, 2020. "A New Mechanism to Alleviate the Crises of Confidence in Science - With an Application to the Public Goods Game," Artefactual Field Experiments 00684, The Field Experiments Website.
    • Handle: RePEc:feb:artefa:00684
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    Cited by:

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    2. Gary Charness & Brian Jabarian & John A. List, 2023. "Generation Next: Experimentation with AI," NBER Working Papers 31679, National Bureau of Economic Research, Inc.
    3. Brodeur, Abel & Cook, Nikolai M. & Hartley, Jonathan S. & Heyes, Anthony, 2022. "Do Pre-Registration and Pre-analysis Plans Reduce p-Hacking and Publication Bias?," GLO Discussion Paper Series 1147, Global Labor Organization (GLO).
    4. Robert Ammerman & Anne Duggan & John List & Lauren Supplee & Dana Suskind, 2021. "The role of open science practices in scaling evidence-based prevention programs," Natural Field Experiments 00741, The Field Experiments Website.
    5. Moritz A. Drupp & Menusch Khadjavi & Rudi Voss, 2024. "The Truth-Telling of Truth-Seekers: Evidence from Online Experiments with Scientists," CESifo Working Paper Series 10897, CESifo.
    6. Igor Asanov & Christoph Buehren & Panagiota Zacharodimou, 2020. "The power of experiments: How big is your n?," MAGKS Papers on Economics 202032, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    7. John A. List, 2024. "Optimally generate policy-based evidence before scaling," Nature, Nature, vol. 626(7999), pages 491-499, February.
    8. Abel Brodeur & Nikolai M. Cook & Jonathan S. Hartley & Anthony Heyes, 2024. "Do Preregistration and Preanalysis Plans Reduce p-Hacking and Publication Bias? Evidence from 15,992 Test Statistics and Suggestions for Improvement," Journal of Political Economy Microeconomics, University of Chicago Press, vol. 2(3), pages 527-561.
    9. Dorner, Zack & Tucker, Steven & Hassan, Gazi M, 2024. "Heterogeneous productivity stabilizes public good contributions under certainty, uncertainty and ambiguity," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 110(C).
    10. Ai Takeuchi & Erika Seki, 2023. "Overcoming problems of coordination and freeriding in a game with multiple public goods: dynamic contribution with information provision," The Japanese Economic Review, Springer, vol. 74(3), pages 379-411, July.
    11. Ahsanuzzaman, & Palm-Forster, Leah H. & Suter, Jordan F., 2022. "Experimental evidence of common pool resource use in the presence of uncertainty," Journal of Economic Behavior & Organization, Elsevier, vol. 194(C), pages 139-160.
    12. John A. List & Azeem M. Shaikh & Atom Vayalinkal, 2023. "Multiple testing with covariate adjustment in experimental economics," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(6), pages 920-939, September.

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    JEL classification:

    • A11 - General Economics and Teaching - - General Economics - - - Role of Economics; Role of Economists
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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