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J R Soc Interface. 2018 Mar;15(140). pii: 20170835. doi: 10.1098/rsif.2017.0835.

Conflict and convention in dynamic networks.

Author information

1
Network Science Institute, Northeastern University, Boston, MA, USA.
2
Department of Philosophy, Tufts University, Medford, MA, USA.
3
Department of Philosophy and Religion, Northeastern University, Boston, MA, USA.
4
Network Science Institute, Northeastern University, Boston, MA, USA c.riedl@neu.edu.
5
D'Amore-McKim School of Business, Northeastern University, Boston, MA, USA.
6
College of Computer and Information Science, Northeastern University, Boston, MA, USA.
7
Institute for Quantitative Social Science, Harvard University, Cambridge, MA, USA.

Abstract

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models.

KEYWORDS:

convention; evolutionary dynamics; game theory; networks; reinforcement learning

PMID:
29563244
PMCID:
PMC5908527
[Available on 2019-03-01]
DOI:
10.1098/rsif.2017.0835

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