Multi-Scale Games: Representing and Solving Games on Networks with Group Structure

Authors

  • Kun Jin University of Michigan, Ann Arbor
  • Yevgeniy Vorobeychik Washington University in St. Louis
  • Mingyan Liu University of Michigan, Ann Arbor

Keywords:

Game Theory, Other Foundations of Game Theory & Economic Parad, Equilibrium

Abstract

Network games provide a natural machinery to compactly represent strategic interactions among agents whose payoffs exhibit sparsity in their dependence on the actions of others. Besides encoding interaction sparsity, however, real networks often exhibit a multi-scale structure, in which agents can be grouped into communities, those communities further grouped, and so on, and where interactions among such groups may also exhibit sparsity. We present a general model of multi-scale network games that encodes such multi-level structure. We then develop several algorithmic approaches that leverage this multi-scale structure, and derive sufficient conditions for convergence of these to a Nash equilibrium. Our numerical experiments demonstrate that the proposed approaches enable orders of magnitude improvements in scalability when computing Nash equilibria in such games. For example, we can solve previously intractable instances involving up to 1 million agents in under 15 minutes.

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Published

2021-05-18

How to Cite

Jin, K., Vorobeychik, Y., & Liu, M. (2021). Multi-Scale Games: Representing and Solving Games on Networks with Group Structure. Proceedings of the AAAI Conference on Artificial Intelligence, 35(6), 5497-5505. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16692

Issue

Section

AAAI Technical Track on Game Theory and Economic Paradigms