Sakiyama Tomoko, Takahara Akihiro
Department of Information Systems Science, Faculty of Science and Engineering, Soka University, Tokyo, Japan.
Information Systems Science, Graduate School of Science and Engineering, Soka University, Tokyo, Japan.
PLoS Comput Biol. 2025 Aug 12;21(8):e1013329. doi: 10.1371/journal.pcbi.1013329. eCollection 2025 Aug.
Many studies have proposed spatial game theory on network systems. Heterogeneous structures seem to contribute to population dynamics. However, few studies have addressed both dynamical population evolution and network growth events, especially incorporating individual players' decision-making processes into the model. In this study, we considered a spatial prisoner's dilemma (SPD) on a random network. In our model, the players were allowed to access the recent past information on themselves and neighboring players. In the "unlikely to happen" scenario, players adopted a strategy that rarely happens, which may have brought some risks to players. Moreover, the players in our model evolved their link with other players by altering their neighborhood when they received a low payoff. As a result, we found that our model spontaneously evolved as an approximate scale-free network around a critical parameter. Interestingly, hub players sometimes decreased their node degree; thus, these players are changeable in our system.
许多研究提出了网络系统上的空间博弈论。异质结构似乎对种群动态有影响。然而，很少有研究同时涉及动态种群演化和网络增长事件，特别是将个体参与者的决策过程纳入模型。在本研究中，我们考虑了随机网络上的空间囚徒困境（SPD）。在我们的模型中，参与者可以获取自己和相邻参与者的近期过往信息。在“不太可能发生”的场景中，参与者采用了一种很少发生的策略，这可能给参与者带来一些风险。此外，我们模型中的参与者在获得低收益时，通过改变其邻域来演化与其他参与者的联系。结果，我们发现我们的模型在一个关键参数附近自发演化为近似无标度网络。有趣的是，中心参与者有时会降低其节点度；因此，这些参与者在我们的系统中是可变的。
