The evolution of conventions under condition-dependent mistakes

  • Ennio Bilancini
  • Leonardo BoncinelliEmail author
Research Article


We study the long-run conventions emerging in a stag-hunt game when agents are myopic best responders. Our main novel assumption is that errors converge to zero at a rate that is positively related to the payoff earned in the past. To fully explore the implications of this error model, we introduce a further novelty in the way we model the interaction structure, assuming that with positive probability agents remain matched together in the next period. We find that, if interactions are sufficiently persistent over time, then the payoff-dominant convention emerges in the long run, while if interactions are quite volatile, then the maximin convention can emerge even if it is not risk-dominant. We contrast these results with those obtained under two alternative error models: uniform mistakes and payoff-dependent mistakes.


Risk-dominant Payoff-dominant Maximin Stag hunt Stochastic stability 

JEL Classification

C72 C73 



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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.IMT School of Advanced StudiesLuccaItaly
  2. 2.Dipartimento di Scienze per l’Economia e l’ImpresaUniversità degli Studi di FirenzeFlorenceItaly

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