Highlights by Area

🎲 Game Theory

We work on foundational questions in evolutionary game theory — how strategies evolve in repeated interactions, what role can learning play in equilibrium selection, and how to use computation and AI to answer questions relevant to strategic interaction and decision making.

📄 Papers

• Picking strategies in games of cooperation 🔗 📄

  Julian García and Arne Traulsen. PNAS, 2025.

  How modellers choose which strategies to include in evolutionary game theory models can quietly shape the conclusions. We propose principles for more systematic strategy selection and argue that AI methods can help build richer models of cooperation.

• Repeated games with partner choice 🔗 📄

  Christopher Graser, Takako Fujiwara-Greve, Julian García and Matthijs van Veelen. PLOS Computational Biology, 2025.

  What happens when players can walk away and find a new partner? The option to leave actually increases cooperation — people end up choosing dependable partners, creating a form of assortment that standard repeated game models miss.

• No strategy can win in the repeated prisoner's dilemma 🔗 📄

  Julian García and Matthijs van Veelen. Frontiers in Robotics and AI, 2018.

  For every Nash equilibrium in the repeated prisoner's dilemma, there are sequences of mutants that destabilise it. Populations cycle between equilibria with different levels of cooperation — and this instability is inescapable regardless of how strategies are represented.

• Direct reciprocity in structured populations 🔗 📄

  Matthijs van Veelen, Julian García, David G. Rand and Martin A. Nowak. PNAS, 2012.

  Direct reciprocity alone does not lead to high cooperation in well-mixed populations. But combine repeated interactions with even a small amount of population structure, and conditional cooperation thrives.