Christian Hilbe

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Modelling the evolution of social norms

Social norms are an important mechanism for cooperation in human societies. These social norms often include rules about when one should help others, and who deserves cooperation. These norms can be subject to evolutionary change – norms that are effective may spread in a population, whereas norms that prove ineffective go extinct. The mathematical field that explores the co-evolution of social norms and cooperation is called indirect reciprocity.

Researchers in this field explore which social norms can maintain cooperation in a population, assuming that cooperation is individually costly. Most of the existing models, however, are based on several simplifying assumptions. For example, most models assume that social norms lead to binary reputations: individuals can either be ‘good’ or ‘bad’. Similarly, most models assume that an individual’s reputation is mostly determined by its most recent behavior. There is no form of memory – antisocial behaviors have no lasting impact on one’s reputation.

The aim of this project is to go beyond these assumptions. We will develop models of social norms that allow for more nuanced reputations which depend on more than the individual’s very last action. These models can then be analyzed using a combination of mathematical approaches (from linear algebra, calculus, and stochastic processes), and with computer simulations.

Candidates should have a genuine interest in human behaviour and evolution, a good mathematical background, and ideally some simulation skills.

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