Leveraging surrounding past strategies to maintain cooperation in the perverse prisoner's dilemma

IF 3.5 2区 数学 Q1 MATHEMATICS, APPLIED Applied Mathematics and Computation Pub Date : 2025-01-10 DOI:10.1016/j.amc.2024.129272
Akihiro Takahara , Tomoko Sakiyama
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Abstract

In spatial game theory, developed models preserving cooperators often incorporate memory to enhance realism. This study examined the role of memory in a spatial prisoner's dilemma. In the proposed model, all players use their own and their neighbors’ past memories and current states to update strategies under specific conditions. When a player's score is lower than that of a neighbor using the same strategy, and that neighbor has the highest score among all neighbors, the player revisits past strategies and adopts a less experienced strategy. This rule adjusts behavior under unfavorable conditions. Results showed that the proposed model effectively retains cooperators. Previous studies have often necessitated the use of long-term memory and intricate systems; however, the present model eliminates these requirements. Instead, it sustains cooperation by employing a strategy that relies on minimal historical information when determining the subsequent course of action.
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利用周围过去的策略,在反常的囚徒困境中保持合作
在空间博弈论中,保留合作者的成熟模型通常包含记忆以增强真实感。这项研究考察了记忆在空间囚徒困境中的作用。在提出的模型中,所有参与者在特定条件下使用自己和邻居的过去记忆和当前状态来更新策略。当玩家的得分低于使用相同策略的邻居,并且该邻居在所有邻居中得分最高时,玩家就会重新审视过去的策略并采用经验不足的策略。这条规则调整在不利条件下的行为。结果表明,该模型有效地保留了合作伙伴。以前的研究经常需要使用长期记忆和复杂的系统;然而,目前的模型消除了这些要求。相反,在决定后续行动方针时,它通过采用一种依赖于最小历史信息的策略来维持合作。
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来源期刊
CiteScore
7.90
自引率
10.00%
发文量
755
审稿时长
36 days
期刊介绍: Applied Mathematics and Computation addresses work at the interface between applied mathematics, numerical computation, and applications of systems – oriented ideas to the physical, biological, social, and behavioral sciences, and emphasizes papers of a computational nature focusing on new algorithms, their analysis and numerical results. In addition to presenting research papers, Applied Mathematics and Computation publishes review articles and single–topics issues.
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