Heterogeneity and Environmental Preferences Shape the Evolution of Cooperation in Supply Networks
Authors: 
Dong Mu, Xiongping Yue Academic Editor: Danilo ComminielloAuthors Info & Claims
Abstract
Supply networks as complex systems are significant challenges for decision-makers in predicting the evolution of cooperation among firms. The impact of environmental heterogeneity on firms is critical. Environment-based preference selection plays a pivotal role in clarifying the existence and maintenance of cooperation in supply networks. This paper explores the implication of the heterogeneity of environment and environment-based preference on the evolution of cooperation in supply networks. Cellular automata are considered to examine the synchronized evolution of cooperation and defection across supply networks. The Prisoner’s Dilemma Game and Snowdrift Game reward schemes have been formed, and the heterogeneous environment and environmental preference have been applied. The results show that the heterogeneous environment’s degree leads to higher cooperation for both Prisoner’s Dilemma Game and Snowdrift Game. We also probe into the impact of the environmental preference on the evolution of cooperation, and the results of which confirm the usefulness of preference of environment. This work offers a valuable perspective to improve the level of cooperation among firms and understand the evolution of cooperation in supply networks.
References
[1]
H. Xia, “Improve the resilience of multilayer supply chain networks,” Complexity, vol. 2020, 9 pages, 2020.
[2]
M. Sofitra, K. Takahashi, K. Morikawa, and K. Morikawa, “The coevolution of interconnected relationship strategies in supply networks,” International Journal of Production Research, vol. 53, no. 22, pp. 6919–6936, 2015.
[3]
S. Perera, M. Bell, and M. Bliemer, “Network science approach to modelling emergence and topological robustness of supply networks: a review and perspective,” 2018, https://arxiv.org/abs/1803.09913.
[4]
M. R. Khaji and R. Shafaei, “A system dynamics approach for strategic partnering in supply networks,” International Journal of Computer Integrated Manufacturing, vol. 24, no. 2, pp. 106–125, 2011.
[5]
A. Brintrup, Y. Wang, and A. Tiwari, “Supply networks as complex systems: a network-science-based characterization,” IEEE Systems Journal, vol. 11, no. 4, pp. 2170–2181, 2017.
[6]
Y. Kim, T. Y. Choi, T. Yan, and K. Dooley, “Structural investigation of supply networks: a social network analysis approach,” Journal of Operations Management, vol. 29, no. 3, pp. 194–211, 2011.
[7]
A. Nair, C. Blome, T. Y. Choi, and G. Lee, “Re-visiting collaborative behavior in supply networks-structural embeddedness and the influence of contextual changes and sanctions,” Journal of Purchasing and Supply Management, vol. 24, no. 2, pp. 135–150, 2018.
[8]
G. Yang, C. Zhu, and W. Zhang, “Adaptive and probabilistic strategy evolution in dynamical networks,” Physica A: Statistical Mechanics and its Applications, vol. 518, pp. 99–110, 2019.
[9]
S. Perera, D. Kasthurirathna, M. Bell, and M. Bliemer, “Topological rationality of supply chain networks,” International Journal of Production Research, vol. 58, pp. 1–24, 2019.
[10]
N. Rashedi and H. Kebriaei, “Cooperative and non-cooperative Nash solution for linear supply function equilibrium game,” Applied Mathematics and Computation, vol. 244, pp. 794–808, 2014.
[11]
H. Barcelo and V. Capraro, “Group size effect on cooperation in one-shot social dilemmas,” Scientific Reports, vol. 5, 2015.
[12]
J. Zhao, C. Luo, and Y. Zheng, “Evolutionary dynamics of the cooperation clusters on interdependent networks,” Physica A: Statistical Mechanics and its Applications, vol. 517, pp. 132–140, 2019.
[13]
L. John, P. McCormick, T. McCormick, G. R. McNeill, and J. Boardman, “Working to understand cooperative forces in government extended enterprises: concepts and methodology,” IEEE Systems Journal, vol. 6, no. 4, pp. 675–687, 2012.
[14]
F. Dercole, F. Della Rossa, and C. Piccardi, “Direct reciprocity and model-predictive rationality explain network reciprocity over social ties,” Scientific Reports, vol. 9, 2019.
[15]
C. Liu, J. Shi, T. Li, and J. Liu, “Aspiration driven coevolution resolves social dilemmas in networks,” Applied Mathematics and Computation, vol. 342, pp. 247–254, 2019.
[16]
M. Wiedermann, J. F. Donges, J. Heitzig et al., “Macroscopic description of complex adaptive networks coevolving with dynamic node states,” Physical Review E, Statistical, Nonlinear, and Soft Matter Physics, vol. 91, p. 52801, 2015.
[17]
H. P. Thadakamalla, U. N. Raghavan, S. Kumara, and A. Albert, “Survivability of multiagent-based supply networks: a topological perspective,” IEEE Intelligent Systems, vol. 19, no. 5, pp. 24–31, 2004.
[18]
J. Liu, H. Meng, W. Wang, Z. Xie, and Q. Yu, “Evolution of cooperation on independent networks: the influence of asymmetric information sharing updating mechanism,” Applied Mathematics and Computation, vol. 340, pp. 234–241, 2019.
[19]
T. Sakiyama and I. Arizono, “An adaptive replacement of the rule update triggers the cooperative evolution in the Hawk-Dove game,” Chaos, Solitons & Fractals, vol. 121, pp. 59–62, 2019.
[20]
C. Xia, J. Wang, L. Wang, S. Sun, J. Sun, and J. Wang, “Role of update dynamics in the collective cooperation on the spatial snowdrift games: beyond unconditional imitation and replicator dynamics,” Chaos, Solitons & Fractals, vol. 45, no. 9-10, pp. 1239–1245, 2012.
[21]
G.-H. Cui, Z. Wang, Y.-C. Yang, S.-W. Tian, and J. Yue, “Heterogeneous game resource distributions promote cooperation in spatial prisoner’s dilemma game,” Physica A: Statistical Mechanics and its Applications, vol. 490, pp. 1191–1200, 2018.
[22]
K. Huang, X. Zheng, and Y. Su, “Effect of heterogeneous sub-populations on the evolution of cooperation,” Applied Mathematics and Computation, vol. 270, pp. 681–687, 2015.
[23]
J. Jin, C. Chu, C. Shen et al., “Heterogeneous fitness promotes cooperation in the spatial prisoner’s dilemma game,” Chaos, Solitons & Fractals, vol. 106, pp. 141–146, 2018.
[24]
L. Sun, S. Rajiv, and J. Chu, “Beyond the more the merrier: the variety effect and consumer heterogeneity in system markets,” International Journal of Research in Marketing, vol. 33, no. 2, pp. 261–275, 2016.
[25]
G. Li, Y.-G. Gu, and Z.-H. Song, “Evolution of cooperation on heterogeneous supply networks,” International Journal of Production Research, vol. 51, no. 13, pp. 3894–3902, 2013.
[26]
Y. E. Wu, Z. Zhang, and S. Chang, “Heterogeneous indirect reciprocity promotes the evolution of cooperation in structured populations,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 28, no. 12, p. 123108, 2018.
[27]
C. Colon and M. Ghil, “Economic networks: heterogeneity-induced vulnerability and loss of synchronization,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 27, no. 12, p. 126703, 2017.
[28]
B. Andres and R. Poler, “A decision support system for the collaborative selection of strategies in enterprise networks,” Decision Support Systems, vol. 91, pp. 113–123, 2016.
[29]
N. K. Verma and A. K. Chatterjee, “A multiple-retailer replenishment model under VMI: accounting for the retailer heterogeneity,” Computers & Industrial Engineering, vol. 104, pp. 175–187, 2017.
[30]
M. De Keizer, R. Akkerman, M. Grunow, J. M. Bloemhof, R. Haijema, and J. G. A. J. Van Der Vorst, “Logistics network design for perishable products with heterogeneous quality decay,” European Journal of Operational Research, vol. 262, no. 2, pp. 535–549, 2017.
[31]
A. Nakkas and Y. Xu, “The impact of valuation heterogeneity on equilibrium prices in supply chain networks,” Production and Operations Management, vol. 28, no. 2, pp. 241–257, 2019.
[32]
W. Zhou, T. Jing, W. Cheng, T. Chen, and Y. Huo, “Combinatorial auction based spectrum allocation under heterogeneous supply and demand,” Computer Communications, vol. 60, pp. 109–118, 2015.
[33]
H. Liu, N. Yang, Z. Yang, J. Lin, and Y. Zhang, “The impact of firm heterogeneity and awareness in modeling risk propagation on multiplex networks,” Physica A: Statistical Mechanics and its Applications, vol. 539, p. 122919, 2020.
[34]
A. Verhetsel, R. Kessels, P. Goos, T. Zijlstra, N. Blomme, and J. Cant, “Location of logistics companies: a stated preference study to disentangle the impact of accessibility,” Journal of Transport Geography, vol. 42, pp. 110–121, 2015.
[35]
S. Chang, Z. Zhang, Y. Li, Y. E. Wu, and Y. Xie, “Investment preference promotes cooperation in spatial public goods game,” PLoS One, vol. 13, 2018.
[36]
S. Zhang, Z. Zhang, Y. E. Wu, M. Yan, and Y. Li, “Strategy preference promotes cooperation in spatial evolutionary games,” Physica A: Statistical Mechanics and its Applications, vol. 514, pp. 181–188, 2019.
[37]
Y. Li and H. Ye, “Effect of the migration mechanism based on risk preference on the evolution of cooperation,” Applied Mathematics and Computation, vol. 320, pp. 621–632, 2018.
[38]
G. Yu, F. Li, and Y. Yang, “Robust supply chain networks design and ambiguous risk preferences,” International Journal of Production Research, vol. 55, no. 4, pp. 1168–1182, 2017.
[39]
C. Du and J. Li, “Preferential learning and memory resolve social dilemma,” Chaos, Solitons & Fractals, vol. 110, pp. 16–19, 2018.
[40]
C. Huang, Q. Dai, H. Cheng, and H. Li, “Preferential selection based on degree difference in the spatial prisoner’s dilemma games,” EPL (Europhysics Letters), vol. 120, no. 1, p. 18001, 2017.
[41]
M. C. Cooper and L. M. Ellram, “Characteristics of supply chain management and the implications for purchasing and logistics strategy,” The International Journal of Logistics Management, vol. 4, no. 2, pp. 13–24, 1993.
[42]
J. Rodewald, J. Colombi, K. Oyama, and A. Johnson, “Methodology for simulation and analysis of complex adaptive supply network structure and dynamics using information theory,” Entropy, vol. 18, no. 10, p. 367, 2016.
[43]
S. S. Perera, M. G. H. Bell, M. Piraveenan, D. Kasthurirathna, and M. Parhi, “Topological structure of manufacturing industry supply chain networks,” Complexity, vol. 2018, 23 pages, 2018.
[44]
Y. S. H. R. Jianbang Du, “The relationship of delivery frequency with the cost and resource operational efficiency: a case study of Jingdong logistics,” Mathematics and Computer Science, vol. 3, pp. 129–140, 2018.
[45]
T. Y. Choi and D. R. Krause, “The supply base and its complexity: implications for transaction costs, risks, responsiveness, and innovation,” Journal of Operations Management, vol. 24, no. 5, pp. 637–652, 2006.
[46]
E. J. S. Hearnshaw and M. M. J. Wilson, “A complex network approach to supply chain network theory,” International Journal of Operations & Production Management, vol. 33, no. 4, pp. 442–469, 2013.
[47]
M. A. Bellamy and R. C. Basole, “Network analysis of supply chain systems: a systematic review and future research,” Systems Engineering, vol. 16, no. 2, pp. 235–249, 2013.
[48]
A. Nair, R. Narasimhan, and T. Y. Choi, “Supply networks as a complex adaptive system: toward simulation-based theory building on evolutionary decision making,” Decision Sciences, vol. 40, no. 4, pp. 783–815, 2009.
[49]
Y. E. Wu, Z. Zhang, and S. Chang, “Reciprocal reward promotes the evolution of cooperation in structured populations,” Chaos, Solitons & Fractals, vol. 119, pp. 230–236, 2019.
[50]
J. Jin, C. Shen, C. Chu, and L. Shi, “Incorporating dominant environment into individual fitness promotes cooperation in the spatial prisoners’ dilemma game,” Chaos, Solitons & Fractals, vol. 96, pp. 70–75, 2017.
[51]
J. Xu, Z. Deng, B. Gao et al., “Popularity-driven strategy updating rule promotes cooperation in the spatial prisoner’s dilemma game,” Applied Mathematics and Computation, vol. 353, pp. 82–87, 2019.
[52]
M. A. Nowak and R. M. May, “Evolutionary games and spatial chaos,” Nature, vol. 359, no. 6398, pp. 826–829, 1992.
[53]
G. Szabó and G. Fáth, “Evolutionary games on graphs,” Physics Reports, vol. 446, no. 4–6, pp. 97–216, 2007.
[54]
H. Guo, C. Shen, D. Dai, M. Zhang, C. Chu, and L. Shi, “Environment promotes the evolution of cooperation in spatial voluntary prisoner’s dilemma game,” Applied Mathematics and Computation, vol. 315, pp. 47–53, 2017.
[55]
Y. E. Wu, S. Zhang, and Z. Zhang, “Environment-based preference selection promotes cooperation in spatial prisoner’s dilemma game,” Scientific Reports, vol. 8, 2018.
[56]
P. Zhu, X. Wang, D. Jia, Y. Guo, S. Li, and C. Chu, “Investigating the co-evolution of node reputation and edge-strategy in prisoner’s dilemma game,” Applied Mathematics and Computation, vol. 386, p. 125474, 2020.
[57]
P. Zhu, Z. Song, H. Guo, Z. Wang, and T. Zhao, “Adaptive willingness resolves social dilemma in network populations,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 29, no. 11, p. 113114, 2019.
Recommendations
A Self-Adaptive Strategy for Evolution of Cooperation in Distributed Networks
This paper studies the phenomenon of the evolution of cooperation in distributed networks by using an iterated game. An iterated game in a distributed network is a multiple round game, where in each round, a player gains a payoff by playing a game with ...
Evolution of cooperation using random pairing on social networks
SEAL'06: Proceedings of the 6th international conference on Simulated Evolution And LearningWe studied the evolution of cooperation on social networks based on personal reputation using random pairing rule. Small-world networks and scale-free networks are used as practical network model. The iterated prisoner's dilemma game are adopted as ...
The influence of own historical information and environmental historical information on the evolution of cooperation
Highlights- A model to study the evolution of cooperation was established.
- Comprehensively ...
AbstractThis article has studied the influence of own historical information and environmental historical information on the evolution of cooperation in the three common social dilemma games. The results show that only the past two-step ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
Copyright © 2021 Dong Mu and Xiongping Yue.
This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher
John Wiley & Sons, Inc.
United States
Publication History
Published: 01 January 2021
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 06 Jan 2025