具有更长记忆的条件合作
Conditional cooperation with longer memory
AI总结：
本研究将反应策略扩展至更长记忆，提出识别伙伴策略的算法，发现更长的记忆能提高反应-n策略的合作率，但需关注行动序列。
摘要：
直接互惠是合作进化中一种广泛存在的机制。在重复互动中，玩家可以根据之前的结果来调整自己的行为。一个众所周知的方法是由反应策略提供的，这些策略对对手的先前行动做出响应。在这里，我们将反应策略扩展到更长的记忆。一个反应-n策略会考虑对手最近n次行动的序列。一个反应-n计数策略记录对手在最近n轮中合作的次数。我们推导出一种算法，用于识别所有反应-n策略中的伙伴策略。我们给出了反应-2、反应-3策略以及反应-n计数策略中所有伙伴策略的明确条件。伙伴策略是那些确保相互合作而不被剥削的策略。我们进行了进化模拟，发现更长的记忆提高了反应-n策略的平均合作率，但对反应计数策略没有影响。关注行动的序列对于获得更长记忆的优势是必要的。
英文摘要：
Direct reciprocity is a wide-spread mechanism for evolution of cooperation. In repeated interactions, players can condition their behavior on previous outcomes. A well known approach is given by reactive strategies, which respond to the co-player's previous move. Here we extend reactive strategies to longer memories. A reactive-$n$ strategy takes into account the sequence of the last $n$ moves of the co-player. A reactive-$n$ counting strategy records how often the co-player has cooperated during the last $n$ rounds. We derive an algorithm to identify all partner strategies among reactive-$n$ strategies. We give explicit conditions for all partner strategies among reactive-2, reactive-3 strategies, and reactive-$n$ counting strategies. Partner strategies are those that ensure mutual cooperation without exploitation. We perform evolutionary simulations and find that longer memory increases the average cooperation rate for reactive-$n$ strategies but not for reactive counting strategies. Paying attention to the sequence of moves is necessary for reaping the advantages of longer memory.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
