Inference of interactions between players based on asynchronously updated evolutionary game data

Abstract

The interactions between players of the prisoner’s dilemma game are inferred using observed game data. All participants play the game with their counterparts and gain corresponding rewards during each round of the game. The strategies of each player are updated asynchronously during the game. Two inference methods of the interactions between players are derived with naïve mean-field (nMF) approximation and maximum log-likelihood estimation (MLE), respectively. Two methods are tested numerically also for fully connected asymmetric Sherrington–Kirkpatrick models, varying the data length, asymmetric degree, payoff, and system noise (coupling strength). We find that the mean square error of reconstruction for the MLE method is inversely proportional to the data length and typically half (benefit from the extra information of update times) of that by nMF. Both methods are robust to the asymmetric degree but work better for large payoffs. Compared with MLE, nMF is more sensitive to the strength of couplings and prefers weak couplings.