论文标题
友好的竞争解决方案，用于迭代的$ n $ - 个人公共善良游戏
Friendly-rivalry solution to the iterated $n$-person public-goods game
论文作者
论文摘要
重复的互动促进了未来阴影下有理人之间的合作，但是当涉及大量容易出错的人时，很难维持合作。构建合作纳什均衡的一种方法是找到一种“友好竞争”策略，该战略的目的是完全合作，但永远不会让同事变得更好。最近已经显示，对于迭代的囚犯在错误存在下的困境，可以使用以下五个规则设计友好的竞争对手：合作，如果每个人都这样做，请接受自己的错误，惩罚自己的错误，惩罚叛逃，在所有其他情况下找到机会并恢复合作。在这项工作中，我们通过概括这五个规则，为迭代的$ n $ person公共善良游戏构建了这种友好的竞争策略。由此产生的策略做出了参考之前的$ m = 2n-1 $ rounds的决定。 $ n = 2 $的友好竞争策略本质上具有进化的鲁棒性，因为没有突变策略在该人群中比中性突变体具有更高的固定概率。当$ n = 2 $和$ 3 $时，我们的进化模拟确实显示了拟议策略的出色表现。
Repeated interaction promotes cooperation among rational individuals under the shadow of future, but it is hard to maintain cooperation when a large number of error-prone individuals are involved. One way to construct a cooperative Nash equilibrium is to find a `friendly-rivalry' strategy, which aims at full cooperation but never allows the co-players to be better off. Recently it has been shown that for the iterated Prisoner's Dilemma in the presence of error, a friendly rival can be designed with the following five rules: Cooperate if everyone did, accept punishment for your own mistake, punish defection, recover cooperation if you find a chance, and defect in all the other circumstances. In this work, we construct such a friendly-rivalry strategy for the iterated $n$-person public-goods game by generalizing those five rules. The resulting strategy makes a decision with referring to the previous $m=2n-1$ rounds. A friendly-rivalry strategy for $n=2$ inherently has evolutionary robustness in the sense that no mutant strategy has higher fixation probability in this population than that of a neutral mutant. Our evolutionary simulation indeed shows excellent performance of the proposed strategy in a broad range of environmental conditions when $n= 2$ and $3$.
