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The emergence of coordinated and consistent macro behavior among self-interested individuals competing for limited resources represents a central inquiry in comprehending market mechanisms and collective behavior. Traditional economics tackles this challenge through a mathematical and theoretical lens, assuming individuals are entirely rational and markets tend to stabilize through the price mechanism. Our paper addresses this issue from an econophysics standpoint, employing reinforcement learning to construct a multi-agent system modeled on minority games. Our study has undertaken a comparative analysis from both collective and individual perspectives, affirming the pivotal roles of reward feedback and individual memory in addressing the aforementioned challenge. Reward feedback serves as the guiding force for the evolution of collective behavior, propelling it towards an overall increase in rewards. Individuals, drawing insights from their own rewards through accumulated learning, gain information about the collective state and adjust their behavior accordingly. Furthermore, we apply information theory to present a formalized equation for the evolution of collective behavior. Our research supplements existing conclusions regarding the mechanisms of a free market and, at a micro level, unveils the dynamic evolution of individual behavior in synchronization with the collective.
P. Młodzianowski and D. Młodzianowski, Identification of areas of econophysical models application, Econ. World, vol. 6, no. 1, pp. 34–44, 2018.
A. S. Chakrabarti and D. Ghosh, Emergence of anti-coordination through reinforcement learning in generalized minority games, J. Econ. Interact. Coord., vol. 14, no. 2, pp. 225–245, 2019.
H. Huang, Y. Cai, H. Xu, and H. Yu, A multiagent minority-game-based demand-response management of smart buildings toward peak load reduction, IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst., vol. 36, no. 4, pp. 573–585, 2016.
L. Chen, Complex network minority game model for the financial market modeling and simulation, Complexity, vol. 2020, pp. 1–11, 2020.
B. LeBaron and L. Tesfatsion, Modeling macroeconomies as open-ended dynamic systems of interacting agents, Am. Econ. Rev., vol. 98, no. 2, pp. 246–250, 2008.
J. E. Stiglitz and M. Gallegati, Heterogeneous interacting agent models for understanding monetary economies, East. Econ. J., vol. 37, no. 1, pp. 6–12, 2011.
D. Challet and Y. C. Zhang, Emergence of cooperation and organization in an evolutionary game, Phys. A Stat. Mech. Appl., vol. 246, nos.3&4, pp. 407–418, 1997.
W. B. Arthur, Inductive reasoning and bounded rationality, The American economic review, vol. 84, no. 2, pp. 406–411, 1994.
D. Challet, Inter-pattern speculation: Beyond minority, majority and $-games, J. Econ. Dyn. Control, vol. 32, no. 1, pp. 85–100, 2008.
D. Challet, M. Marsili, and Y. C. Zhang, Minority games and stylized facts, Phys. A Stat. Mech. Appl., vol. 299, nos.1&2, pp. 228–233, 2001.
D. Challet, M. Marsili, and Y. C. Zhang, Stylized facts of financial markets and market crashes in Minority Games, Phys. A Stat. Mech. Appl., vol. 294, nos.3&4, pp. 514–524, 2001.
R. Savit, R. Manuca, and R. Riolo, Adaptive competition, market efficiency, and phase transitions, Phys. Rev. Lett., vol. 82, no. 10, pp. 2203–2206, 1999.
S. P. Zhang, J. Q. Dong, L. Liu, Z. G. Huang, L. Huang, and Y. C. Lai, Reinforcement learning meets minority game: Toward optimal resource allocation, Phys. Rev. E, vol. 99, no. 3, p. 032302, 2019.
D. Challet and M. Marsili, Phase transition and symmetry breaking in the minority game, Phys. Rev. E, vol. 60, no. 6, pp. R6271–R6274, 1999.
D. Challet, A. De Martino, and M. Marsili, Dynamical instabilities in a simple minority game with discounting, J. Stat. Mech., vol. 2008, no. 4, p. L04004, 2008.
M. Paczuski, K. E. Bassler, and Á. Corral, Self-organized networks of competing Boolean agents, Phys. Rev. Lett., vol. 84, no. 14, pp. 3185–3188, 2000.
C. Borghesi, M. Marsili, and S. Miccichè, Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode, Phys. Rev. E Stat. Nonlin. Soft Matter Phys., vol. 76, no. 2, p. 026104, 2007.
J. Q. Zhang, Z. G. Huang, Z. X. Wu, R. Su, and Y. C. Lai, Controlling herding in minority game systems, Sci. Rep., vol. 6, p. 20925, 2016.
J. Q. Dong, Z. G. Huang, L. Huang, and Y. C. Lai, Triple grouping and period-three oscillations in minority-game dynamics, Phys. Rev. E, vol. 90, no. 6, p. 062917, 2014.
L. Kristoufek and M. Vosvrda, Herding, minority game, market clearing and efficient markets in a simple spin model framework, Commun. Nonlinear Sci. Numer. Simul., vol. 54, pp. 148–155, 2018.
S. P. Zhang, J. Q. Zhang, L. Chen, and X. D. Liu, Oscillatory evolution of collective behavior in evolutionary games played with reinforcement learning, Nonlinear Dyn., vol. 99, no. 4, pp. 3301–3312, 2020.
Y. Geng, Y. Liu, Y. Lu, C. Shen, and L. Shi, Reinforcement learning explains various conditional cooperation, Appl. Math. Comput., vol. 427, p. 127182, 2022.
M. A. L. Thathachar and P. S. Sastry, Varieties of learning automata: An overview, IEEE Trans. Syst., Man, Cybern. B, vol. 32, no. 6, pp. 711–722, 2002.
H. Beigy and M. R. Meybodi, A mathematical framework for cellular learning automata, Advs. Complex Syst., vol. 7, no. 03n04, pp. 295–319, 2004.
E. L. Thorndike, Animal intelligence: An experimental study of the associative processes in animals, Psychol. Rev. Monogr. Suppl., vol. 2, no. 4, pp. i–109, 1898.
J. Henrich, R. McElreath, A. Barr, J. Ensminger, C. Barrett, A. Bolyanatz, J. C. Cardenas, M. Gurven, E. Gwako, N. Henrich et al., Costly punishment across human societies, Science, vol. 312, no. 5781, pp. 1767–1770, 2006.
I. Erev and A. E. Roth, Predicting how people play games: Reinforcement learning in experimental games with unique, mixed strategy equilibria, The American economic review, vol. 88, no. 4, pp. 848–881, 1998.
D. Challet, M. Marsili, and Y. C. Zhang, Modeling market mechanism with minority game, Phys. A Stat. Mech. Appl., vol. 276, nos.1&2, pp. 284–315, 2000.
C. Hilbe, L. A. Martinez-Vaquero, K. Chatterjee, and M. A. Nowak, Memory- n strategies of direct reciprocity, Proc Natl Acad Sci U. S. A., vol. 114, no. 18, pp. 4715–4720, 2017.
C. Di, Q. Zhou, J. Shen, J. Wang, R. Zhou, and T. Wang, The coupling effect between the environment and strategies drives the emergence of group cooperation, Chaos Solitons Fractals, vol. 176, p. 114138, 2023.
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