Effects of noise on convergent game learning dynamics

Details Effects of noise on convergent game learning dynamics Effects of noise on convergent game learning dynamics

2011-09-22

arxiv.org/abs/1109.4853v2

J. Phys. A: Math. Theor. 45 (2012) 105001

Nonlinear Sciences

Adaptation and Self-Organizing Systems

17 pages, 8 figures

Scientific paper

10.1088/1751-8113/45/10/105001

We study stochastic effects on the lagging anchor dynamics, a reinforcement learning algorithm used to learn successful strategies in iterated games, which is known to converge to Nash points in the absence of noise. The dynamics is stochastic when players only have limited information about their opponents' strategic propensities. The effects of this noise are studied analytically in the case where it is small but finite, and we show that the statistics and correlation properties of fluctuations can be computed to a high accuracy. We find that the system can exhibit quasicycles, driven by intrinsic noise. If players are asymmetric and use different parameters for their learning, a net payoff advantage can be achieved due to these stochastic oscillations around the deterministic equilibrium.

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