Learning dynamics in social dilemmas

MW Macy*, A Flache, Michael W. Macy

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

345 Citations (Scopus)
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The Nash equilibrium, the main solution concept in analytical game theory, cannot make precise predictions about the outcome of repeated mixed-motive games. Nor can it tell us much about the dynamics by which a population of players moves from one equilibrium to another. These limitations, along with concerns about the cognitive demands of forward-looking rationality, have motivated efforts to explore backward-looking alternatives to analytical game theory. Most of the effort has been invested in evolutionary models of population dynamics. We shift attention to a learning-theoretic alternative. Computational experiments with adaptive agents identify a fundamental solution concept for social dilemmas--stochastic collusion--based on a random walk from a self-limiting noncooperative equilibrium into a self-reinforcing cooperative equilibrium. However, we show that this solution is viable only within a narrow range of aspiration levels. Below the lower threshold, agents are pulled into a deficient equilibrium that is a stronger attractor than mutual cooperation, Above the upper threshold, agents are dissatisfied with mutual cooperation. Aspirations that adapt with experience (producing habituation to stimuli) do not gravitate into the window of viability; rather, they are the worst of both worlds. Habituation destabilizes cooperation and stabilizes defection. Results from the two-person problem suggest that applications to multiplex and embedded relationships will yield unexpected insights into the global dynamics of cooperation in social dilemmas.

Original languageEnglish
Pages (from-to)7229-7236
Number of pages8
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number3
Publication statusPublished - 14-May-2002
EventSackler Colloquium on Adaptive Agents, Intelligence, and Emergent Human Organization - Capturing Complexity through Agent-Based Modeling -
Duration: 4-Oct-20016-Oct-2001



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