Cooperation behavior; Evolutionary game theory; The fixed number of neighbors; Vicsek model; Binary decision; Collective motions; Evolutionary games; Fixed numbers; Game-Based; Movement pattern; The fixed number of neighbor; Vicsek models; Computational Mathematics; Applied Mathematics
Abstract :
[en] In the face of collective motion, people often face a binary decision: they may interact with others and pay for communication, or they can choose to go alone and forgo these costs. Evolutionary game theory (EGT) emerges in this setting as a crucial paradigm to address this complex issue. In this study, an EGT-based Vicsek with a fixed number of neighbors is proposed. It assumed that the agent had a limited view and just considered a certain number of neighbors. Agents exhibit varying movement patterns depending on the strategies they choose. Each agent's payoff depends on balancing the benefits of group movement against the communication costs with selected neighbors. Using the Fermi rule, individuals adjust their strategies accordingly. The study indicates that agents achieve the highest levels of cooperation and the fastest convergence times in high-density environments. When density is constant, increasing the number of neighbors enhances the synchronization; when the number of neighbors remains unchanged, a lower density leads to better synchronization. Additionally, the results show that EGT could boost the synchronization and accelerate the convergence of self-propelled agents.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > FINATRAX - Digital Financial Services and Cross-organizational Digital Transformations
Disciplines :
Computer science Management information systems
Author, co-author :
Zhao, Hui; Department of Computer Science and Technology, Tongji University, Shanghai, China
Zhang, Zhenyu ; Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
TCHAPPI HAMAN, Igor ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > FINATRAX
Li, Li; Department of Control Science and Engineering, Tongji University, Shanghai, China
External co-authors :
yes
Language :
English
Title :
An evolutionary game-based vicsek model with a fixed number of neighbors
C. Darwin, On the origin of species, 2016, p. 1859.
Su, Q., McAvoy, A., Mori, Y., Plotkin, J.B., Evolution of prosocial behaviours in multilayer populations. Nature Human Behavior 6:3 (2022), 338–348.
Vicsek, T., Czirók, A., Ben-Jacob, E., Cohen, I., Shochet, O., Novel type of phase transition in a system of self-driven particles. Phys. Rev. Lett., 75(6), 1995, 1226.
Chatterjee, S., Mangeat, M., Woo, C.-U., Rieger, H., Noh, J.D., Flocking of two unfriendly species: the two-species Vicsek model. Phys. Rev. E, 107(2), 2023, 024607.
Briant, M., Diez, A., Merino-Aceituno, S., Cauchy theory for general kinetic Vicsek models in collective dynamics and mean-field limit approximations. SIAM J. Math. Anal. 54:1 (2022), 1131–1168.
Lu, X., Zhang, C., Qin, B., An improved Vicsek model of swarm based on remote neighbors strategy. Phys. A, Stat. Mech. Appl., 587, 2022, 126553.
You, F., Yang, H.-X., Li, Y., Du, W., Wang, G., A modified Vicsek model based on the evolutionary game. Appl. Math. Comput., 438, 2023, 127565.
Allen, B., Lippner, G., Chen, Y.-T., Fotouhi, B., Momeni, N., Yau, S.-T., Nowak, M.A., Evolutionary dynamics on any population structure. Nature 544:7649 (2017), 227–230.
Du, W., Guo, T., Chen, J., Li, B., Zhu, G., Cao, X., Cooperative pursuit of unauthorized uavs in urban airspace via multi-agent reinforcement learning. Transp. Res., Part C, Emerg. Technol., 128, 2021, 103122.
Dai, L., Zhou, X., Sun, Z., Xia, Y., Distributed hierarchical mpc based on evolutionary game for formation control with collision and obstacle avoidance. 2021 40th Chinese Control Conference (CCC), 2021, IEEE, 5026–5031.
Meloni, S., Buscarino, A., Fortuna, L., Frasca, M., Gómez-Gardeñes, J., Latora, V., Moreno, Y., Effects of mobility in a population of prisoner's dilemma players. Phys. Rev. E, Stat. Nonlinear Soft Matter Phys., 79(6), 2009, 067101.
Li, J., Zhang, C., Sun, Q., Chen, Z., Zhang, J., Changing the intensity of interaction based on individual behavior in the iterated prisoner's dilemma game. IEEE Trans. Evol. Comput. 21:4 (2016), 506–517.
Li, Y., Zhang, J., Perc, M., Effects of compassion on the evolution of cooperation in spatial social dilemmas. Appl. Math. Comput. 320 (2018), 437–443.
Szolnoki, A., Chen, X., Environmental feedback drives cooperation in spatial social dilemmas. Europhys. Lett., 120(5), 2018, 58001.
Li, Y., Wang, H., Du, W., Perc, M., Cao, X., Zhang, J., Resonance-like cooperation due to transaction costs in the prisoner's dilemma game. Phys. A, Stat. Mech. Appl. 521 (2019), 248–257.
Wang, C., Szolnoki, A., Inertia in spatial public goods games under weak selection. Appl. Math. Comput., 449, 2023, 127941.
Lee, H.-W., Cleveland, C., Szolnoki, A., Mercenary punishment in structured populations. Appl. Math. Comput., 417, 2022, 126797.
Wang, S., Chen, X., Xiao, Z., Szolnoki, A., Decentralized incentives for general well-being in networked public goods game. Appl. Math. Comput., 431, 2022, 127308.
Zhang, X., Jia, S., Li, X., Improving the synchronization speed of self-propelled particles with restricted vision via randomly changing the line of sight. Nonlinear Dyn. 90 (2017), 43–51.
Ginelli, F., Chaté, H., Relevance of metric-free interactions in flocking phenomena. Phys. Rev. Lett., 105(16), 2010, 168103.
Zhang, X., Fan, S., Wu, W., Enhancing synchronization of self-propelled particles via modified rule of fixed number of neighbors. Phys. A, Stat. Mech. Appl., 629, 2023, 129203.
Wang, C., Deng, J., Zhao, H., Li, L., Effect of Q-learning on the evolution of cooperation behavior in collective motion: an improved Vicsek model. Appl. Math. Comput., 482, 2024, 128956.
Perc, M., Szolnoki, A., Coevolutionary games—a mini review. Biosystems 99:2 (2010), 109–125.