2 research outputs found
Effects of noise on convergent game learning dynamics
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.Comment: 17 pages, 8 figure