What is it about?

Classical game theory typically analyzes interactions between two (or a few) players and aims to answer how each individual can maximize their utility. Such a question, from a mathematical perspective, becomes very intricate and complex as a player must consider the utilities of all other players and any possible set of beliefs. However, In many real-world multi-agent systems, the goal is shifted towards the understanding of the complex ecologies of behaviors emerging from a given dilemma (or "game") played in a large population of individuals. This is where evolutionary game theory (EGT) shines as a theoretical and computational framework. In this tutorial, we aim to introduce the main principles and models in EGT. We will offer an overview of the research in the field using social dilemmas as the key use cases to explain all concepts. All topics will be illustrated using EGTtools in Jupyter notebooks.

We will cover the following topics:

  1. Introduction to Game Theory
  2. Infinite and Finite populations
  3. Games on Networks

Material

We have prepared some jupyter notebooks for you to follow the tutorial interactively. You can access them here and can run them in Google Colab: Tutorial Notebooks

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Some info about the presenter

personal picture

Elias Fernández Domingos

Postdoctoral Researcher at the AI Lab, Vrije Universiteit Brussel, Belgium

Elias is currently a Post-doctoral researcher (F.W.O. fellow) at the Artificial Intelligence Lab of the Vrije Universiteit Brussel. Also affiliated with the Machine Learning Group (Université Libre de Bruxelles). He is interested in the origins of cooperation in social interactions and how we can maintain it in an increasingly complex and hybrid human-AI world. In his research, he applies concepts and methods from (Evolutionary) Game Theory, Behavioural economics, and Machine Learning to model collective (strategic) behaviour and validate it through behavioural economic Experiments. He is the creator of EGTtools a Python/C++ toolbox for Evolutionary Game Theory.

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Important references

  1. Nash Jr, John F. "Equilibrium points in n-person games." Proceedings of the national academy of sciences 36.1 (1950): 48-49.
  2. M. Nowak, Evolutionary dynamics; exploring the equations of life.
  3. Sigmund, K., 2010. The Calculus of Selfishness. Princeton University Press.
  4. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M. & Hwang, D. Complex networks: Structure and dynamics. Physics Reports 424, 175–308 (2006).
  5. Domingos, Elias Fernández, Francisco C. Santos, and Tom Lenaerts. "EGTtools: Evolutionary game dynamics in Python." Iscience 26.4 (2023).