17.3 Conclusion

Directions for possible extensions:

  • more complex games (e.g., more agents, options, and outcomes)

  • more interactions

  • allowing for uncertainty (e.g., in range of options, their likelihoods, and payoff matrices)

17.3.1 Summary

Social situations are often messier than non-social ones, but the corresponding models must not be more complex. In this chapter, the analysis of strategic games, population dynamics, and social networks illustrated some starting points for such models. More detailed models will primarily require a clear conceptual grasp on the research questions to be addressed by them.

17.3.2 Resources

Here are some pointers to sources of inspirations and ideas:


  • Axelrod & Hamilton (1981) is a classic on cooperation and the assessment of strategies in a tournament.

  • Nowé, Vrancx, & De Hauwere (2012) provide an introduction to game theory and illustrate how reinforcement learning (RL) can be applied to repeated games and Markov games.

Social learning

  • Page (2018) contains several chapters with models of social situations

Social networks