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

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  • Luigi Butera
  • Philip J. Grossman
  • Daniel Houser
  • John A. 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 deci- sions. 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 benefi- cial 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 J. Grossman & Daniel Houser & John A. List & Marie Claire Villeval, 2020. "A New Mechanism to Alleviate the Crises of Confidence in Science - With an Application to the Public Goods Game," Monash Economics Working Papers 03-20, Monash University, Department of Economics.
  • Handle: RePEc:mos:moswps:2020-03
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    Cited by:

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    2. John A. List, 2024. "Optimally generate policy-based evidence before scaling," Nature, Nature, vol. 626(7999), pages 491-499, February.
    3. 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.
    4. Gary Charness & Brian Jabarian & John List, 2023. "Generation Next: Experimentation with AI," Artefactual Field Experiments 00777, The Field Experiments Website.
    5. Takeuchi, Ai & Seki, Erika, 2023. "Coordination and free-riding problems in the provision of multiple public goods," Journal of Economic Behavior & Organization, Elsevier, vol. 206(C), pages 95-121.
    6. 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.
    7. 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).
    8. Brodeur, Abel & Cook, Nikolai M. & Hartley, Jonathan S. & Heyes, Anthony, 2023. "Do Pre-Registration and Pre-Analysis Plans Reduce p-Hacking and Publication Bias?: Evidence from 15,992 Test Statistics and Suggestions for Improvement," GLO Discussion Paper Series 1147 [pre.], Global Labor Organization (GLO).
    9. 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.
    10. 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.
    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.

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    Keywords

    Replication; science; public goods; uncertainty; experiment.;
    All these keywords.

    JEL classification:

    • C91 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Individual Behavior

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