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Predicting Efficiency in Threshold Public Good Games: A Learning Direction Theory Approach

Author

Listed:
  • Federica Alberti

    (University of Portsmouth)

  • Anna Cartwright

    (Coventry University)

  • Edward Cartwright

    (De Montfort University)

Abstract

In this paper we propose a tractable model of behavior in threshold public good games. The model is based on learning direction theory. We find that individual behavior is consistent with the predictions of the model. Moreover, the model is able to accurately predict the success rate of groups in providing the public good. We apply this to give novel insight on the assurance problem by showing that the problem (of coordinating on the inefficient equilibrium of no contributions) is only likely with a relatively low endowment. In developing the model we compare and contrast best reply learning and impulse balance theory. Our results suggest that best reply learning provides a marginally better fit with the data.

Suggested Citation

  • Federica Alberti & Anna Cartwright & Edward Cartwright, 2021. "Predicting Efficiency in Threshold Public Good Games: A Learning Direction Theory Approach," Working Papers in Economics & Finance 2021-01, University of Portsmouth, Portsmouth Business School, Economics and Finance Subject Group.
    • Handle: RePEc:pbs:ecofin:2021-01
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    References listed on IDEAS

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    More about this item

    Keywords

    public good; threshold; learning direction theory; impulse balance theory; counterfactual thinking;
    All these keywords.

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • H41 - Public Economics - - Publicly Provided Goods - - - Public Goods
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior

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