Date and time: Thursday 7 May 2026, 12:00-13:00 CEST (arrive at 11:45 for FREE LUNCH*)
Speaker: Joshua B. Plotkin, University of Pennsylvania
Title: Cooperation in Structured Populations: From Classical Game Theory to Modern Network Dynamics
Where: Digital Futures hub, Osquars Backe 5, floor 2 at KTH main campus OR Zoom
Directions: https://www.digitalfutures.kth.se/contact/how-to-get-here/
OR
Zoom: https://kth-se.zoom.us/j/69560887455
*To get a FREE LUNCH, you need to register — first come, first served. Maximum 50 participants.
The hub will be open 30 minutes before and after the event for those who wish to stay longer.
Host: Angela Fontan angfon@kth.se
Co-host: Silun Zhang silunz@kth.se
Bio: Joshua Plotkin is the Walter H. and Leonore C. Annenberg Professor of Natural Sciences at the University of Pennsylvania, where he co-directs the Penn Center for Mathematical Biology. Professor Plotkin is an applied mathematician with appointments in the Departments of Biology, Mathematics, and Computer and Information Sciences.
His work leverages mathematical models of populations as a framework for understanding broad patterns of biological, cultural, and social evolution.
Abstract: Why do self-interested individuals cooperate? In this seminar, I will begin with a didactic overview of how this question is framed mathematically. Starting from classical game theory and social dilemmas, I will explain how evolutionary game theory replaces rational choice with differential reproductive success, and how the replicator equation emerges as the deterministic limit of an underlying stochastic birth–death process in well-mixed populations. I will then turn to structured populations, where individuals interact on graphs rather than in homogeneous mixtures.
Foundational results — including the discovery that birth–death and death–birth updating lead to fundamentally different outcomes, and that pair-approximation methods render spatial games analytically tractable — revealed that microscopic update rules and network topology interact in subtle but decisive ways. Subsequent frameworks for arbitrary, irregular graphs have further connected evolutionary dynamics to graph-theoretic quantities such as reproductive value and coalescence times.
Building on this foundation, I will describe recent developments that extend evolutionary game theory beyond static, pairwise graphs. These include models in which behavior is conditioned on network position, asymmetric social interactions that break payoff symmetry, coevolving strategies and network structure, multilayer systems in which interaction and replacement occur on distinct networks, and higher-order interactions that move beyond edges to the dynamics on hypergraphs. Across these developments, a unifying theme emerges: cooperation cannot be understood from payoffs alone, but from the interplay between incentives, stochastic updating, and population structure. Tracing this intellectual arc highlights how the study of cooperation has evolved into a fertile meeting ground for evolutionary theory, stochastic processes, and network science.
