Author

J. Plotkin

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Preprint Oct 2026

Multi-Agent Learning of Reactive Strategies in Iterated Games

Learning agents, whether humans or algorithms, adapt to one another rather than to a fixed environment. Selfish learning need not produce a good outcome for the group, especially when learners face a social dilemma. The iterated prisoner's dilemma (IPD) is the classical example, but it is usually studied in populations...

Jim Wolfe, J. Plotkin · 0 citations

#machine learning Preprint Oct 2026

An evolutionary origin of collective decision making in humans and machines

Groups of individuals can solve collective problems more accurately than any single member, by aggregating their opinions. Recent theoretical work has identified individual-level reward schemes that allow uninformed individuals to evolve collective intelligence from the bottom up, through social learning. Yet these res...

Guo-Cheng Wang, Qi Su, J. Plotkin · 0 citations

Preprint Sep 2026

Prevalence and the rate of antigenic evolution are jointly determined in an eco-evolutionary SIR model

Influenza A persists by continually changing the antigens that its host population recognizes. How fast it evolves and how many hosts it infects are not independent, because the immunity that selects new variants is produced by the infections the pathogen has already caused. We analyze a model of Andreasen et.\ al.\ th...

Daniel Gomez, J. Plotkin · 0 citations