Research article Special Issues

Multi-Local-Worlds economic and management complex adaptive system with agent behavior and local configuration

  • Received: 28 December 2023 Revised: 28 March 2024 Accepted: 01 April 2024 Published: 11 April 2024
  • The central focus of our investigation revolved around the convergence of agents' behavior toward a particular invariant distribution and determining the characteristics of the optimal strategies' distribution within the framework of a dynamical Multi-Local-Worlds complex adaptive system. This system was characterized by the co-evolution of agent behavior and local topological configuration. The study established a representation of an agent's behavior and local graphic topology configuration to elucidate the interaction dynamics within this dynamical context. As an illustrative example, we introduced three distinct agent types—smart agent, normal agent, and stupid agent—each associated with specific behaviors. The findings underscored that an agent's decision-making process was influenced by the evolution of random complex networks driven by preferential attachment, coupled with a volatility mechanism linked to its payment—a dynamic that propels the evolution of the complex adaptive system. Through simulation, we drew a conclusive observation that even when considering irrational behaviors characterized by limited information and memory constraints, the system's state converges to a specific attractor. This underscored the robustness and convergence properties inherent in the dynamical Multi-Local-Worlds complex adaptive system under scrutiny.

    Citation: Hebing Zhang, Xiaojing Zheng. Multi-Local-Worlds economic and management complex adaptive system with agent behavior and local configuration[J]. Electronic Research Archive, 2024, 32(4): 2824-2847. doi: 10.3934/era.2024128

    Related Papers:

  • The central focus of our investigation revolved around the convergence of agents' behavior toward a particular invariant distribution and determining the characteristics of the optimal strategies' distribution within the framework of a dynamical Multi-Local-Worlds complex adaptive system. This system was characterized by the co-evolution of agent behavior and local topological configuration. The study established a representation of an agent's behavior and local graphic topology configuration to elucidate the interaction dynamics within this dynamical context. As an illustrative example, we introduced three distinct agent types—smart agent, normal agent, and stupid agent—each associated with specific behaviors. The findings underscored that an agent's decision-making process was influenced by the evolution of random complex networks driven by preferential attachment, coupled with a volatility mechanism linked to its payment—a dynamic that propels the evolution of the complex adaptive system. Through simulation, we drew a conclusive observation that even when considering irrational behaviors characterized by limited information and memory constraints, the system's state converges to a specific attractor. This underscored the robustness and convergence properties inherent in the dynamical Multi-Local-Worlds complex adaptive system under scrutiny.



    加载中


    [1] A. Pelster, W. Günter, Selforganization In Complex Systems: The Past, Present, And Future Of Synergetics, in Proceedings of the International Symposium, Switzerland: Springer International, Publishing AG, Springer Cham, 2016.
    [2] M. Rietkerk, R. Bastiaansen, S. Banerjee, J. V. Koppel, M. Baudena, A. Doelman, Evasion of tipping in complex systems through spatial pattern formation, Science, 374 (2021), eabj0359. https://doi.org/10.1126/science.abj0359 doi: 10.1126/science.abj0359
    [3] M. A. Fuentes, A. Gerig, J. Vicente, Universal behavior of extreme price movements in stock markets, PLoS One, 4 (2009), e8243. https://doi.org/10.1371/journal.pone.0008243 doi: 10.1371/journal.pone.0008243
    [4] M. T. J. Heino, K. Knittle, C. Noone, F. Hasselman, N. Hankonen, Studying behaviour change mechanisms under complexity, Behav. Sci., 11 (2021), 77. https://doi.org/10.3390/bs11050077 doi: 10.3390/bs11050077
    [5] S. Bowles, E. A. Smith, M. B. Mulder, The emergence and persistence of inequality in premodern societies introduction to the special section, Curr. Anthropol., 51 (2010), 7–17. https://doi.org/10.1086/649206 doi: 10.1086/649206
    [6] S. Bartolucci, F. Caccioli, P. Vivo, A percolation model for the emergence of the Bitcoin Lightning Network, Sci. Rep., 10 (2020), 4488. https://doi.org/10.1038/s41598-020-61137-5 doi: 10.1038/s41598-020-61137-5
    [7] C. Hesp, M. Ramstead, A. Constant, P. Badcock, M. Kirchhoff, K. Friston, A multi-scale view of the emergent complexity of life: A free-energy proposal, in Evolution, Development and Complexity: Multiscale Evolutionary Models of Complex Adaptive Systems, Springer, Cham, 2019. https://doi.org/10.1007/978-3-030-00075-2_7
    [8] J. P. Bagrow, D. Wang, A. L. Barabasi, Collective response of human populations to large-scale emergencies, PLoS One, 6 (2011), e17680. https://doi.org/10.1371/journal.pone.0017680 doi: 10.1371/journal.pone.0017680
    [9] F. Brauer, Z. L. Feng, C. Castillo-Chavez, Discrete epidemic models, Math. Biosci. Eng., 7 (2010), 1–15. https://doi.org/10.3934/mbe.2010.7.1 doi: 10.3934/mbe.2010.7.1
    [10] S. E. Kreps, D. L. Kriner, Model uncertainty, political contestation, and public trust in science: Evidence from the COVID-19 pandemic, Sci. Adv., 6 (2020), eabd4563. https://doi.org/10.1126/sciadv.abd4563 doi: 10.1126/sciadv.abd4563
    [11] G. F. D. Arruda, L. G. S. Jeub, A. S. Mata, F. A. Rodrigues, Y. Moreno, From subcritical behavior to elusive transition in rumor models, Nat. Commun., 13 (2022), 3049. https://doi.org/10.1038/s41467-022-30683-z doi: 10.1038/s41467-022-30683-z
    [12] J. Andreoni, N. Nikiforakis, S. Siegenthaler, Predicting social tipping and norm change in controlled experiments, Proc. Natl. Acad. Sci., 118 (2021), e2014893118. https://doi.org/10.1073/pnas.2014893118 doi: 10.1073/pnas.2014893118
    [13] F. Clemente, M. Unterländer, O. Dolgova, O. Lao, A. Malaspinas, C. Papageorgopoulou, The genomic history of the Aegean palatial civilizations, Cell, 184 (2021), 2565–2586. https://doi.org/10.1016/j.cell.2021.03.039 doi: 10.1016/j.cell.2021.03.039
    [14] J. Li, C. Xia, G. Xiao, Y. Moreno. Crash dynamics of interdependent networks, Sci. Rep., 9 (2019), 14574. https://doi.org/10.1038/s41598-019-51030-1. doi: 10.1038/s41598-019-51030-1
    [15] N. Biderman, D. Shohamy, Memory and decision making interact to shape the value of unchosen options, Nat. Commun., 12 (2021), 4648. https://doi.org/10.1038/s41467-021-24907-x doi: 10.1038/s41467-021-24907-x
    [16] D. E. Levy, M. C. Pachucki, A. J. O'Malley, B. Porneala, A. Yaqubi, A. N. Thorndike, Social connections and the healthfulness of food choices in an employee population, Nat. Hum. Behav., 5 (2021), 1349–1357. https://doi.org/10.1038/s41562-021-01103-x doi: 10.1038/s41562-021-01103-x
    [17] P. Rizkallah, A. Sarracino, O. Bénichou, P. Lllien, Microscopic theory for the diffusion of an active particle in a crowded environment, Phys. Rev. Lett., 128 (2022), 038001. https://doi.org/10.1103/PhysRevLett.128.038001 doi: 10.1103/PhysRevLett.128.038001
    [18] D. Fernex, B. R. Noack, R Semaan, Cluster-based network modeling—From snapshots to complex dynamical systems, Sci. Adv., 7 (2021), eabf5006. https://doi.org/10.1126/SCIADV.ABF5006 doi: 10.1126/SCIADV.ABF5006
    [19] L. Gavassino, M. Antonelli, B. Haskell, Thermodynamic stability implies causality, Phys. Rev. Lett., 128 (2021), 010606. https://doi.org/10.48550/arXiv.2105.14621. doi: 10.48550/arXiv.2105.14621
    [20] P. Cardaliaguet, C. Rainer, Stochastic differential games with asymmetric information, Appl. Math. Opt., 59 (2009), 1–36. https://doi.org/10.1007/s00245-008-9042-0 doi: 10.1007/s00245-008-9042-0
    [21] P. Mertikopoulos, A. L. Moustakas, The emergence of rational behavior in the presence of stochastic perturbations, Ann. Appl. Probab., 20 (2010), 1359–1388. https://doi.org/10.1214/09-AAP651 doi: 10.1214/09-AAP651
    [22] I. Durham, A formal model for adaptive free choice in complex systems, Entropy, 22 (2020), 568. https://doi.org/10.3390/e22050568 doi: 10.3390/e22050568
    [23] J. H. Jiang, K. Ranabhat, X. Y. Wang, H. Rich, R. Zhang, C. Peng, Active transformations of topological structures in light-driven nematic disclination networks, Proc. Natl. Acad. Sci., 119 (2022), e2122226119. https://doi.org/10.1073/pnas.2122226119. doi: 10.1073/pnas.2122226119
    [24] K. Jiang, R. Merrill, D. You, P. Pan, Z. Li, Optimal control for transboundary pollution under ecological compensation: A stochastic differential game approach, J. Clean. Prod., 241 (2019), 118391. https://doi.org/10.1016/j.jclepro.2019.118391 doi: 10.1016/j.jclepro.2019.118391
    [25] W. Brian, Foundations of complexity economics, Nat. Rev. Phys., 3 (2021), 136–145. https://doi.org/10.1038/s42254-020-00273-3 doi: 10.1038/s42254-020-00273-3
    [26] M. Schlüter, L. J Haider, S. J. Lade, E. Lindkvist, C. Folke, Capturing emergent phenomena in social-ecological systems: an analytical framework, Ecol. Soc., 24 (2019), 11. https://doi.org/10.5751/ES-11012-240311. doi: 10.5751/ES-11012-240311
    [27] W. Steffen, K. Richardson, J. Rockstrm, H. Schellnhuber, O. P. Dube, S. Dutreuil, et.al., The emergence and evolution of Earth System Science, Nat. Rev. Earth. Env., 1 (2020), 54–63. https://doi.org/10.1038/s43017-019-0005-6 doi: 10.1038/s43017-019-0005-6
    [28] H. P. Maia, S. C. Ferreira, M. L. Martins, Adaptive network approach for emergence of societal bubbles, Phys. A, 572 (2021), 125588. https://doi.org/10.1016/j.physa.2020.125588 doi: 10.1016/j.physa.2020.125588
    [29] Z. H. Zhang, H. W. Wu, J. Yang, R. Pan, M. Kuang, Research on the evolution of supply chain based on complex adaptive system theory, in 1st International Conference on Business, Economics, Management Science (BEMS 2019), Atlantis Press, 80 (2019), 558–567. https://doi.org/10.2991/bems-19.2019.101
    [30] W. Zou, D. V. Senthikumar, Z. Meng, J. Kurths, Quenching, aging, and reviving in coupled dynamical networks, Phys. Rep., 931 (2021), 1–72. https://doi.org/10.1016/j.physrep.2021.07.004 doi: 10.1016/j.physrep.2021.07.004
    [31] J. H. Liang, S. J. Wang, C. S. Zhou, Less is more: Wiring-economical modular networks support self-sustained firing-economical neural avalanches for efficient processing, Natl. Sci. Rev., 9 (2022), nwab102. https://doi.org/10.1093/nsr/nwab102 doi: 10.1093/nsr/nwab102
    [32] Z. Fulker, P. Forber, R. Smead, C. Riedl, Spite is contagious in dynamic networks, Nat. Commun., 12 (2021), 260. https://doi.org/10.1038/s41467-020-20436-1 doi: 10.1038/s41467-020-20436-1
    [33] R. Berner, S. Vock, E. Schöll, S. Yanchuk, Desynchronization transitions in adaptive networks, Phys. Rev. Lett., 126 (2021), 028301. https://doi.org/10.1103/physrevlett.126.028301 doi: 10.1103/physrevlett.126.028301
    [34] M. A. Mahmoud, M. S. Ahmad, S. A. Mostafa, Norm-based behavior regulating technique for multi-agent in complex adaptive systems, IEEE Access, 7 (2019), 126662–126678. https://doi.org/10.1109/access.2019.2939019 doi: 10.1109/access.2019.2939019
    [35] G. Dosi, A. Roventini, More is different... and complex! the case for agent-based macroeconomics, J. Evol. Econ., 29 (2019), 1–37. https://doi.org/10.1007/s00191-019-00609-y doi: 10.1007/s00191-019-00609-y
    [36] M. C. Miguel, J. T. Parley, R. Pastor-Satorras, Effects of heterogeneous social interactions on flocking dynamics, Phys. Rev. Lett., 120 (2018), 068303. https://doi.org/10.1103/PhysRevLett.120.068303. doi: 10.1103/PhysRevLett.120.068303
    [37] T. Hassler, J. Ullrich, M. Bernardino, A large-scale test of the link between intergroup contact and support for social change, Nat. Hum. Behav., 4 (2020), 380–386. https://doi.org/10.1038/s41562-019-0815-z doi: 10.1038/s41562-019-0815-z
    [38] F. M. Neffke, The value of complementary co-workers, Sci. Adv., 5 (2019), eaax3370. https://doi.org/10.1126/sciadv.aax3370 doi: 10.1126/sciadv.aax3370
    [39] S. A. Levin, H. V. Milner, C. Perrings, The dynamics of political polarization, Proc. Natl. Acad. Sci., 118 (2021), e2116950118. https://doi.org/10.1073/pnas.2116950118 doi: 10.1073/pnas.2116950118
    [40] C. L. Priol, P. L. Doussal, A. Rosso, Spatial clustering of depinning avalanches in presence of long-range interactions, Phys. Rev. Lett., 126 (2021), 025702. https://doi.org/10.1103/PhysRevLett.126.025702 doi: 10.1103/PhysRevLett.126.025702
    [41] T. Narizuka, Y. Yamazaki, Lifetime distributions for adjacency relationships in a vicsek model, Phys. Rev. E., 100 (2019), 032603. https://doi.org/10.1103/PhysRevE.100.032603 doi: 10.1103/PhysRevE.100.032603
    [42] J. Brown, T. Bossomaier, L. Barnett, Information flow infinite flocks, Sci. Rep., 10 (2020), 3837. https://doi.org/10.1038/s41598-020-59080-6 doi: 10.1038/s41598-020-59080-6
    [43] L. Tiokhin, M. Yan, T. J. Morgan, Competition for priority harms the reliability of science, but reforms can help, Nat. Hum. Behav., 5 (2021), 857–867. https://doi.org/10.1038/s41562-020-01040-1. doi: 10.1038/s41562-020-01040-1
    [44] R. K. Colwell, Spatial scale and the synchrony of ecological disruption, Nature, 599 (2021), E8–E10. https://doi.org/10.1038/s41586-021-03759-x doi: 10.1038/s41586-021-03759-x
    [45] J. E. Allgeier, T. J. Cline, T. E. Walsworth, G. Wathen, C. A. Layman, D. E. Schindler, Individual behavior drives ecosystem function and the impacts of harvest, Sci. Adv., 6 (2020), eaax8329. https://doi.org/10.1126/sciadv.aax8329 doi: 10.1126/sciadv.aax8329
    [46] B. J. Tóth, G.Palla, E. Mones, G. Havadi, N. Páll, P. Pollner, et.al., Emergence of leader-follower hierarchy among players in an on-line experiment, in 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), IEEE, (2018), 1184–1190. https://doi.org/10.1109/ASONAM.2018.8508278
    [47] A. N. Tump, T. J. Pleskac, R. H. Kurvers, Wise or mad crowds? The cognitive mechanisms under-lying information cascades, Sci. Adv., 6 (2020), eabb0266. https://doi.org/10.1126/sciadv.abb0266 doi: 10.1126/sciadv.abb0266
    [48] R. Berner, S. Vock, E. Scholl, S. Yanchuk, Desynchronization transitions in adaptive networks, Phys. Rev. Lett., 126 (2021), 028301. https://doi.org/10.1103/PhysRevLett.126.028301 doi: 10.1103/PhysRevLett.126.028301
    [49] L. Zhang, W. Chen, M. Antony, K. Y. Szeto, Phase diagram of symmetric iterated prisoner's dilemma of two-companies with partial imitation rule, preprint, arXiv: 1103.6103. https://doi.org/10.48550/arXiv.1103.6103
    [50] G. Chen, Small noise may diversify collective motion in Vicsek model, IEEE Trans. Autom. Control, 62 (2016), 636–651. https://doi.org/10.1109/tac.2016.2560144 doi: 10.1109/tac.2016.2560144
    [51] J. Clinton, J. Cohen, J. Lapinski, M. Trussler, Partisan pandemic: How partisanship and public health concerns affect individuals' social mobility during covid-19, Sci. Adv., 7 (2021), eabd7204. https://doi.org/10.1126/sciadv.abd7204 doi: 10.1126/sciadv.abd7204
    [52] Y. Ma, E. W. M. Lee, Spontaneous synchronization of motion in pedestrian crowds of different densities, Nat. Hum. Behav., 5 (2021), 447–457. https://doi.org/10.1038/s41562-020-00997-3 doi: 10.1038/s41562-020-00997-3
    [53] B. Mahault, A. Patelli, H. Chate, Deriving hydrodynamic equations from dry active matter models in three dimensions, J. Stat. Mech: Theory Exp., 9 (2018), 093202. https://doi.org/10.1088/1742-5468/aad6b5 doi: 10.1088/1742-5468/aad6b5
    [54] A. M. Smith, M. Pósfai, M. Rohden, A. D. González, L. Duenas-Osorio, R. M. D'Souza, Competitive percolation strategies for network recovery, Sci. Rep., 9 (2019), 11843. https://doi.org/10.1038/s41598-019-48036-0 doi: 10.1038/s41598-019-48036-0
    [55] Z. Fulker, P. Forber, R. Smead, C. Riedl, Spite is contagious in dynamic networks, Nat. Commun., 12 (2021), 260. https://doi.org/10.1038/s41467-020-20436-1 doi: 10.1038/s41467-020-20436-1
    [56] G. J. Baxter, S. N. Dorogovtsev, A. V. Goltsev, J. F. Mendes, Heterogeneous k-core versus bootstrap percolation on complex networks, Phys. Rev. E., 83 (2011), 051134. https://doi.org/10.1103/physreve.83.051134 doi: 10.1103/physreve.83.051134
    [57] S. Mittal, Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective, Cogn. Syst. Res., 21 (2013), 22–39. https://doi.org/10.1016/j.cogsys.2012.06.003 doi: 10.1016/j.cogsys.2012.06.003
    [58] L. Cremene, M. Cremene, The Social Honesty game-A computational analysis of the impact of conformity and identity on honest behavior contagion in complex social systems, Chaos, Solitons Fractals, 144 (2021), 110710. https://doi.org/10.1016/j.chaos.2021.110710. doi: 10.1016/j.chaos.2021.110710
    [59] X. L. Ruan, C. Xu, Adaptive dynamic event-triggered control for multi-agent systems with matched uncertainties under directed topologies, Phys. A, 586 (2022), 126450. https://doi.org/10.1016/j.physa.2021.126450 doi: 10.1016/j.physa.2021.126450
    [60] J. S. Pan, Y. Q. Li, X. Liu, H. P. Hu, Y. Hu, Modeling Collective Behavior of Posting Microblog by Stochastic Differential Equation with Jump, preprint, arXiv: 1710.02651. https://doi.org/10.48550/arXiv.1710.02651
    [61] D. W. K. Yeung, Dynamically consistent cooperative solution in a differential game of transboundary industrial pollution, J. Optim. Theory Appl., 134 (2007), 143–160. https://doi.org/10.1007/s10957-007-9240-y doi: 10.1007/s10957-007-9240-y
    [62] K. S. Kumar, Nonzero sum stochastic differential games with discounted payoff criterion: An approximating markov chain approach, SIAM J. Control Optim., 47 (2010), 374–395. https://doi.org/10.1137/060650623. doi: 10.1137/060650623
    [63] M. Mijalkov, A. McDaniel, J. Wehr, G. Volpe, Engineering sensorial delay to control phototaxis and emergent collective behaviors, Phys. Rev. X, 6 (2016), 011008. https://doi.org/10.1103/PhysRevX.6.011008 doi: 10.1103/PhysRevX.6.011008
    [64] T. Akimoto, S. Shinkai, Y. Aizawa, Distributional behavior of time averages of non-L1 observables in one-dimensional intermittent maps with infinite invariant measures, J. Stat. Phys., 158 (2014), 476–493. https://doi.org/10.1007/s10955-014-1138-0 doi: 10.1007/s10955-014-1138-0
    [65] M. Staudigi, Co-evolutionary dynamics and Bayesian interaction games, Int. J. Game Theory, 42 (2013), 179–210. https://doi.org/10.1007/s00182-012-0331-0 doi: 10.1007/s00182-012-0331-0
  • Reader Comments
  • © 2024 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(377) PDF downloads(23) Cited by(0)

Article outline

Figures and Tables

Figures(7)  /  Tables(3)

Other Articles By Authors

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog