Computational Models for Complex Social Dilemmas

Primary supervisor

Julian Garcia Gallego

Research area

Computational and Collective Intelligence

The most challenging problems of our time are social dilemmas. Thes are situations where individuals are incentivised to free ride on others, but successful group outcomes depend on everyone’s contributions. Examples include, climate change action or compliance with non-pharmaceutical interventions in a large-scale pandemic. In both cases, individuals can rely on others doing their share, but when everyone adopts such a free-riding strategy the public good collapses [1].

In this project we study how boundedly rational agents can learn to coordinate their actions for successful collective action. We study the underlying theory, using the framework of Evolutionary Game Theory and build models for concrete applications based on this theory [2]. The ultimate goal of this project is to develop new computational methods for modelling social dilemmas that can account for real-world complexity in agents’ behaviour. We will build on novel computational techniques to produce realistic enough models that can be falsified empirically [3].

Our research group studies how groups of agents can learn to cooperate. Most of our research focuses on social dilemmas, i.e., situations where poor group outcomes arise from optimal individual choices. We use this framework to study: Multi-agent Systems and AI, Social Systems, and Models in Biology and Evolution. Please check our publications for more details: http://garciajulian.com

[1] Garcia Julian and Traulsen Arne. “Evolution of coordinated punishment to enforce cooperation from an unbiased strategy space” J. R. Soc. Interface. (2019) 162019012720190127http://doi.org/10.1098/rsif.2019.0127

[2] Hilbe, Christian, Štěpán Šimsa, Krishnendu Chatterjee, and Martin A. Nowak. “Evolution of Cooperation in Stochastic Games.” Nature 559, no. 7713 (July 2018): 246–49. https://doi.org/10.1038/s41586-018-0277-x.

[3] McNamara, John M. “Towards a Richer Evolutionary Game Theory.” Journal of The Royal Society Interface 10, no. 88 (November 6, 2013): 20130544. https://doi.org/10.1098/rsif.2013.0544.

#sustainability

Required knowledge

  • Have an excellent academic track record in computer science or a cognate field. An Honors degree with HD/H1 or equivalent is essential;

  • A strong interest in the topic of the research;

  • Excellent written and verbal communication skills;

  • An interest in game theory, solid skills in mathematical modelling, and good programming skills.

Project funding

Other

Learn more about minimum entry requirements.