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Modelling Social Dilemmas: A Regret-driven Neural Network Model

Author

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  • Ganesh S. Birajdar
  • Balaraju Battu
  • Krishnavtar Jaiswal
  • V. S. Chandrasekhar Pammi

Abstract

Marchiori and Warglien (2008 , Science, 319(5866), 1111–1113) showed that a simple regret-driven neural network model outperforms almost all competing models when predicting human choice behaviour in games with ‘unique equilibrium in mixed strategies’. Considering its effectiveness in this class of games, we scale up the model to account for strategically more important decision-making scenarios like prisoners’ dilemma (PD). The modification is based on the assumption that the trajectory of behaviour observed in a repeated PD experiment is the result of the bidirectional attraction between pareto-optimal (mutual cooperation) versus self-interested defection (mutual defection) in repeated PD game. The simulation results significantly capture the qualitative trends in behaviour over time.

Suggested Citation

  • Ganesh S. Birajdar & Balaraju Battu & Krishnavtar Jaiswal & V. S. Chandrasekhar Pammi, 2016. "Modelling Social Dilemmas: A Regret-driven Neural Network Model," Studies in Microeconomics, , vol. 4(2), pages 115-126, December.
    • Handle: RePEc:sae:miceco:v:4:y:2016:i:2:p:115-126
    DOI: 10.1177/2321022216647261
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    References listed on IDEAS

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