  The interactions between players of prisoner's dilemma (PD) game are
reconstructed with evolutionary game data. All participants play the game with
their counterparts and gain corresponding rewards during each round of the
game. However, their strategies are updated asynchronously during the
evolutionary PD game. Two inference methods of the interactions between players
are derived with naive mean-field (nMF) approximation and maximum
log-likelihood estimation (MLE) respectively. The two methods are tested
numerically also for fully connected asymmetric Sherrington-Kirkpatrick (SK)
models, varying the data length, asymmetric degree, payoff and system noise
(coupling strength). We find that the reconstruction mean square error (MSE) of
MLE method is proportional to the inverse of 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 works better for large payoff.
Compared with MLE, nMF is more sensitive to the couplings strength which
prefers weak couplings.
