The fitness landscape of social norms in social dilemmas
Maximilian Puelma Touzel

TL;DR
This paper extends the evolutionary game theory of social norms to Markov games, analyzing how norms evolve and coordinate in complex social dilemmas with stochastic signals.
Contribution
It generalizes existing norm evolution models to Markov games and provides a detailed analysis of replicator dynamics in this context.
Findings
Mapped norms over signal and reward spaces.
Provided a detailed exposition of the underlying mechanics.
Analyzed the emergence of norms via replicator dynamics.
Abstract
By specifying behaviour across multiple agents, social norms are a coordination approach to resolving social dilemmas. Decentralized and wide adoption can be achieved by norms whose prescription involves interpreting stochastic signals in the environment. Such signals must have enough correlation to orchestrate mutually beneficial coordination and enough disincentivizing uncertainty about the benefits of exploiting that coordination. Evolutionary game theory of matrix games has been used to describe how, by rational agents comparing and adopting norms, a norm can evolve to become dominant in a population. Morsky \& Ak\c{c}ay (2019) classify norms according to a set of rationality criteria. Joint player strategies that adopt norms that are consistent with optimal single-player strategies with respect to expected reward naturally satisfy a correlated, rather than Nash game theoretic…
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