论文标题
来自异步更新的进化游戏的网络重建
Network reconstruction from asynchronously updated evolutionary game
论文作者
论文摘要
囚犯困境（PD）游戏玩家之间的互动是通过进化游戏数据重建的。所有参与者都会与同行一起玩游戏，并在每轮比赛中获得相应的奖励。但是，他们的策略在进化PD游戏中异步更新。参与者之间相互作用的两种推理方法分别以幼稚的平均场（NMF）近似和最大对数似然估计（MLE）得出。这两种方法也经过数值测试，用于完全连接的不对称Sherrington-Kirkpatrick（SK）模型，以改变数据长度，不对称程度，回报和系统噪声（耦合强度）。我们发现，MLE方法的重建均方误差（MSE）与数据长度的倒数成正比，通常是NMF的一半（从更新时间的额外信息中受益）。两种方法在不对称程度上都是强大的，但可以更好地回报。与MLE相比，NMF对耦合强度更敏感，后者更喜欢弱耦合。
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.
