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复杂系统与复杂性科学  2022, Vol. 19 Issue (2): 39-44    DOI: 10.13306/j.1672-3813.2022.02.005
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基于分组选择的可调聚类网络中个体的合作行为研究
邓云生, 张纪会
青岛大学 a.复杂性科学研究所; b.山东省工业控制技术重点实验室,山东 青岛 266071
On the Cooperative Behavior of Individuals in Adjustable Clustering Networks Based on Grouping Selection
DENG Yunsheng, ZHANG Jihui
a. Institute of Complexity Science, b. Shandong Key Laboratory of Industrial Control Technology, Qingdao University, Qingdao 266071, China
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摘要 为研究用于划分分组的参数、个体的记忆长度、网络的聚类性等参数对合作行为的共同影响,在传统的复杂网络模型结合博弈论模型的研究基础上,提出一种分组选择的个体互动规则。通过仿真实验发现,分组选择的方法不仅可以有效促进可调聚类网络中合作行为的涌现,同样也可以促进方格网和小世界网络中个体的合作,该规则具有一定的普适性和可重复性。该研究提供了一种提高大规模群体的整体合作水平的方法,能够更深入地了解现实世界的合作现象,为探索个体合作行为背后的机理提供了一个新途径。
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邓云生
张纪会
关键词 合作行为可调聚类分组选择演化博弈    
Abstract:Cooperative behavior is widespread, and how to promote the emergence of cooperative behavior has been a hot issue of systems science. Combining the traditional complex network model with the game theory model, this paper proposes a grouping selection rule for individual interaction and investigates the joint influence of the parameter dividing the groups, the individual's memory length, and the network clustering coefficient on cooperative behavior. Through simulation experiments, it is found that the grouping selection can effectively promote not only the emergence of cooperative behaviors in adjustable clustering networks, but also the individuals' cooperation in lattice and small-world networks, indicating that the rule has certain universality and repeatability. It provides a new approach to improve the overall level of cooperation in large-scale groups, enabling us to gain a deeper understanding of real-world cooperation phenomena and opening up a new avenue for exploring the reasons behind individuals' cooperative behavior.
Key wordscooperative behavior    adjustable clustering    grouping selection    evolutionary games
收稿日期: 2021-03-16      出版日期: 2022-05-23
ZTFLH:  N94  
  F224  
基金资助:国家自然科学基金(61673228,62072260)
通讯作者: 张纪会(1969-),男,山东潍坊人,博士,教授,主要研究方向为智能优化理论、系统工程。   
作者简介: 邓云生(1982-),男,山东青岛人,博士研究生,主要研究方向为复杂网络、博弈论。
引用本文:   
邓云生, 张纪会. 基于分组选择的可调聚类网络中个体的合作行为研究[J]. 复杂系统与复杂性科学, 2022, 19(2): 39-44.
DENG Yunsheng, ZHANG Jihui. On the Cooperative Behavior of Individuals in Adjustable Clustering Networks Based on Grouping Selection. Complex Systems and Complexity Science, 2022, 19(2): 39-44.
链接本文:  
http://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2022.02.005      或      http://fzkx.qdu.edu.cn/CN/Y2022/V19/I2/39
[1] PENNISI E. How did cooperative behavior evolve[J]. Science, 2005, 309(5731): 93-93.
[2] NOWAK M A, MAY R M. Evolutionary games and spatial chaos[J].Nature, 1992, 359(6398): 826-829.
[3] BARABÁSI A L, ALBERT R, JEONG H. Mean-field theory for scale-free random networks[J]. Physica A: Statistical Mechanics and Its Applications, 1999, 272: 173-187.
[4] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world' networks[J]. Nature, 1998, 393(6684): 440-442.
[5] BULDYREV S V, PARSHANI R, PAUL G, et al. Catastrophic cascade of failures in interdependent networks[J]. Nature, 2010, 464(7291): 1025-1028.
[6] FU F, LIU L H, WANG L. Evolutionary prisoner's dilemma on heterogeneous Newman-Watts small-world network[J]. The European Physical Journal B, 2007, 56(4): 367-372.
[7] LUO C, ZHANG X, LIU H, et al. Cooperation in memory-based prisoner's dilemma game on interdependent networks[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 450: 560-569.
[8] DENG Y, ZHANG J. Memory-based prisoner's dilemma game with history optimal strategy learning promotes cooperation on interdependent networks[J]. Applied Mathematics and Computation, 2021, 390: 125675.
[9] HAUERT C, DOEBELI M. Spatial structure often inhibits the evolution of cooperation in the snowdrift game[J]. Nature, 2004, 428(6983): 643-646.
[10] YE W, FAN S. Evolutionary snowdrift game with rational selection based on radical evaluation[J]. Applied Mathematics and Computation, 2017, 294: 310-317.
[11] SHU F, LIU X, LI M. Impacts of memory on a regular lattice for different population sizes with asynchronous update in spatial snowdrift game[J]. Physics Letters A, 2018, 382(20): 1317-1323.
[12] ZHANG W, LI Y S, XU C, et al. Cooperative behavior and phase transitions in co-evolving stag hunt game[J]. Physica A: Statistical Mechanics and Its Applications, 2016, 443: 161-169.
[13] DONG Y, XU H, FAN S. Memory-based stag hunt game on regular lattices[J]. Physica A: Statistical Mechanics and Its Applications, 2019, 519: 247-255.
[14] SZOLNOKI A, PERC M. Impact of critical mass on the evolution of cooperation in spatial public goods games[J]. Physical Review E, 2010, 81(5): 057101.
[15] 章平,黄傲霜,罗宏维.不同类型复杂网络中个体合作行为互动的演化博弈模拟[J].复杂系统与复杂性科学, 2019, 16(3): 60-70.
ZHANG P, LUO A S, LUO H W. The evolutionary game simulation of individual cooperative behavior in different complex networks[J]. Complex Systems and Complexity Science, 2019, 16(3): 60-70.
[16] LÜ S, LI J, MI J, et al. The roles of heterogeneous investment mechanism in the public goods game on scale-free networks[J]. Physics Letters A, 2020, 384(17): 126343.
[17] HOLME P, KIM B J. Growing scale-free networks with tunable clustering[J]. Physical Review E, 2002, 65(2): 026107.
[18] ASSENZA S, GÓMEZ-GARDEES J, LATORA V. Enhancement of cooperation in highly clustered scale-free networks[J]. Physical Review E, 2008, 78(1): 017101.
[19] RONG Z, YANG H X, WANG W X. Feedback reciprocity mechanism promotes the cooperation of highly clustered scale-free networks[J]. Physical Review E, 2010, 82(4): 047101.
[20] WANG L, LI G, MA Y, et al. Structure properties of collaboration network with tunable clustering[J]. Information Sciences, 2020, 506: 37-50.
[21] 王娜,张玉林.碳税政策下制造商和再制造商竞争与合作博弈分析[J].电子科技大学学报(社科版), 2021, 23(2): 75-85.
WANG N, ZHANG Y L. Analysis of competition and cooperation between a manufacturer and a remanufacturer under carbon tax policy based on game theory[J]. Journal of UESTC (Social Sciences Edition), 2021, 23(2): 75-85.
[22] 高明,黄仁辉.群体情绪对风险型环境群体性事件的影响[J].电子科技大学学报(社科版), 2021, 23(1): 1-9.
GAO M, HUANG R H. The influence of group emotion on risk environmental group events[J]. Journal of UESTC (Social Sciences Edition), 2021, 23(1): 1-9.
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