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Fashion, Cooperation, and Social Interactions

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Listed:
  • Zhigang Cao
  • Haoyu Gao
  • Xinglong Qu
  • Mingmin Yang
  • Xiaoguang Yang

Abstract

Fashion plays such a crucial rule in the evolution of culture and society that it is regarded as a second nature to the human being. Also, its impact on economy is quite nontrivial. On what is fashionable, interestingly, there are two viewpoints that are both extremely widespread but almost opposite: conformists think that what is popular is fashionable, while rebels believe that being different is the essence. Fashion color is fashionable in the first sense, and Lady Gaga in the second. We investigate a model where the population consists of the afore-mentioned two groups of people that are located on social networks (a spatial cellular automata network and small-world networks). This model captures two fundamental kinds of social interactions (coordination and anti-coordination) simultaneously, and also has its own interest to game theory: it is a hybrid model of pure competition and pure cooperation. This is true because when a conformist meets a rebel, they play the zero sum matching pennies game, which is pure competition. When two conformists (rebels) meet, they play the (anti-) coordination game, which is pure cooperation. Simulation shows that simple social interactions greatly promote cooperation: in most cases people can reach an extraordinarily high level of cooperation, through a selfish, myopic, naive, and local interacting dynamic (the best response dynamic). We find that degree of synchronization also plays a critical role, but mostly on the negative side. Four indices, namely cooperation degree, average satisfaction degree, equilibrium ratio and complete ratio, are defined and applied to measure people’s cooperation levels from various angles. Phase transition, as well as emergence of many interesting geographic patterns in the cellular automata network, is also observed.

Suggested Citation

  • Zhigang Cao & Haoyu Gao & Xinglong Qu & Mingmin Yang & Xiaoguang Yang, 2013. "Fashion, Cooperation, and Social Interactions," PLOS ONE, Public Library of Science, vol. 8(1), pages 1-14, January.
  • Handle: RePEc:plo:pone00:0049441
    DOI: 10.1371/journal.pone.0049441
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    References listed on IDEAS

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    1. Uche Okonkwo, 2007. "Luxury Fashion Branding," Palgrave Macmillan Books, Palgrave Macmillan, number 978-0-230-59088-5, December.
    2. Bikhchandani, Sushil & Hirshleifer, David & Welch, Ivo, 1992. "A Theory of Fads, Fashion, Custom, and Cultural Change in Informational Cascades," Journal of Political Economy, University of Chicago Press, vol. 100(5), pages 992-1026, October.
    3. Abhijit V. Banerjee, 1992. "A Simple Model of Herd Behavior," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 107(3), pages 797-817.
    4. Frank Schweitzer & Robert Mach, 2008. "The Epidemics of Donations: Logistic Growth and Power-Laws," PLOS ONE, Public Library of Science, vol. 3(1), pages 1-4, January.
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    Cited by:

    1. Grabisch, Michel & Poindron, Alexis & Rusinowska, Agnieszka, 2019. "A model of anonymous influence with anti-conformist agents," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    2. Michel Grabisch & Fen Li, 2020. "Anti-conformism in the Threshold Model of Collective Behavior," Dynamic Games and Applications, Springer, vol. 10(2), pages 444-477, June.
    3. Yali Dong & Cong Li & Yi Tao & Boyu Zhang, 2015. "Evolution of Conformity in Social Dilemmas," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-12, September.
    4. Rafał Apriasz & Tyll Krueger & Grzegorz Marcjasz & Katarzyna Sznajd-Weron, 2016. "The Hunt Opinion Model—An Agent Based Approach to Recurring Fashion Cycles," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-19, November.
    5. Qi Wang & Wensong Lin, 2024. "Fashion game on graphs with more than two actions," Journal of Combinatorial Optimization, Springer, vol. 48(4), pages 1-18, November.
    6. Zhigang Cao & Cheng-zhong Qin & Xiaoguang Yang & Boyu Zhang, 2019. "Dynamic matching pennies on networks," International Journal of Game Theory, Springer;Game Theory Society, vol. 48(3), pages 887-920, September.

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