The short version. I ask why people cooperate, and what it takes for cooperation to hold up when a dilemma is shared by a group rather than a pair. Some of that work models the group directly as a higher-order network — an interaction that involves three, four or twenty people at once — because that is where the classic two-player theory of cooperation breaks down in interesting ways.
01
Evolutionary game theory & the evolution of cooperation
Drivers of cooperation in social dilemmas on higher-order networks — J. R. Soc. Interface 2025;
Evolutionary game selection creates cooperative environments — PRE 2024
Classical evolutionary game theory puts two players in a dilemma and asks when
cooperation survives. Real social dilemmas are rarely two-player: public goods,
common resources and collective action all involve groups.
My approach stays inside game theory — replicator dynamics, selection, the
payoffs that make cooperation stable or not. I model those group dilemmas on
hypergraphs and simplicial complexes because that is where the classic picture
breaks down: cooperation can appear explosively — a discontinuous transition,
with hysteresis, rather than the smooth onset the pairwise theory predicts.
Which mechanism dominates depends on how groups overlap, how benefits scale
with group size, and how players choose which game to play.
- Cooperation
- Public goods games
- Hypergraphs
- Simplicial complexes
Key papers
02
Collective human behaviour
Evidence that group interactions matter is scattered across statistical physics,
experimental economics, social psychology and computational social science, and
the fields rarely read each other.
With colleagues across those areas I have worked to draw that evidence together:
where higher-order effects have actually been measured in human behaviour, which
models capture them, and what the open problems are for anyone trying to predict
what a group will do.
- Computational social science
- Behavioural experiments
- Synthesis
Key papers
03
Cultural evolution & social structure
Cumulative culture — knowledge that gets better as it is passed along — needs
both innovation and enough connection between people for innovations to travel
and combine.
Working with evolutionary anthropologists, I use genetic and ecological data to
reconstruct how connected populations are, and ask whether that connectivity
predicts the complexity of their culture. In chimpanzees it does: more connected
populations have more complex tool use. My current work in Zürich extends this to
contemporary hunter-gatherer societies.
- Evolutionary anthropology
- Cumulative culture
- Population connectivity
Key papers
04
Nonequilibrium statistical physics
Thermodynamic uncertainty relation for energy transport — PRE 2021;
Zero-current nonequilibrium state in symmetric exclusion process — J. Stat. Mech. 2020
Where I started, and still the toolkit I reach for first. Stochastic processes
driven away from equilibrium: what happens to a random walker that is
periodically reset to its origin, how self-propelled particles behave under the
same protocol, and how tightly fluctuations constrain transport in small systems.
- Stochastic resetting
- Active matter
- Thermodynamic uncertainty relations
Key papers
05
Networks in the wild
From streaks to synergies: performance and scoring in the NBA — arXiv 2026;
An infectious diseases hazard map for India — Curr. Sci. 2021
Some problems are worth doing because the data is good and the question is fun.
Separating individual skill from team context in cricket and basketball; mapping
how India’s mobility and transport networks would carry an infectious disease.
These projects keep me honest: a model that only works on synthetic networks is
not yet a model of anything.
- Sports analytics
- Epidemic spreading
- Mobility networks
Key papers