Towards a mathematical theory of behavioral swarms
ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 125.

This paper presents a unified mathematical theory of swarms where the dynamics of social behaviors interacts with the mechanical dynamics of self-propelled particles. The term behavioral swarms is introduced to characterize the specific object of the theory which is subsequently followed by applications. As concrete examples for our unified approach, we show that several Cucker-Smale type models with internal variables fall down to our framework. The second part of the paper shows how the modeling can be developed, beyond the Cucker-Smale approach. This will be illustrated with the aid of numerical simulations in swarms whose movement strategy is sensitive to individual social behaviors. Finally, the presentation looks ahead to research perspectives.

DOI : 10.1051/cocv/2020071
Classification : 82D99, 91D10
Mots-clés : Collective dynamics, Cucker-Smale flocking, learning, living systems, self-organization, swarming
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     author = {Bellomo, Nicola and Ha, Seung-Yeal and Outada, Nisrine},
     editor = {Buttazzo, G. and Casas, E. and de Teresa, L. and Glowinsk, R. and Leugering, G. and Tr\'elat, E. and Zhang, X.},
     title = {Towards a mathematical theory of behavioral swarms},
     journal = {ESAIM: Control, Optimisation and Calculus of Variations},
     publisher = {EDP-Sciences},
     volume = {26},
     year = {2020},
     doi = {10.1051/cocv/2020071},
     mrnumber = {4188831},
     zbl = {1459.82166},
     language = {en},
     url = {http://www.numdam.org/articles/10.1051/cocv/2020071/}
}
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Bellomo, Nicola; Ha, Seung-Yeal; Outada, Nisrine. Towards a mathematical theory of behavioral swarms. ESAIM: Control, Optimisation and Calculus of Variations, Tome 26 (2020), article no. 125. doi : 10.1051/cocv/2020071. http://www.numdam.org/articles/10.1051/cocv/2020071/

[1] S.-M. Ahn, H.-O. Bae, S.-Y. Ha, Y. Kim and H. Lim, Application of flocking mechanisms, to the modeling of stochastic volatily. Math. Models Methods Appl. Sci. 23 (2013) 1603–1628. | DOI | MR | Zbl

[2] G. Ajmone Marsan, N. Bellomo and L. Gibelli, Stochastic evolutionary differential games toward a systems theory of behavioral social dynamics. Math. Models Methods Appl. Sci. 26 (2016) 1051–1093. | DOI | MR | Zbl

[3] G. Albi, N. Bellomo, L. Fermo, S.-Y. Ha, J. Kim, L. Pareschi, D. Poyato and J. Soler, Traffic, crowds, and swarms. From kinetic theory and multiscale methods to applications and research perspectives. Math. Models Methods Appl. Sci. 29 (2019) 1901–2005. | DOI | MR | Zbl

[4] G. Albi and L. Pareschi, Selective model-predictive control for flocking systems. Commun. Appl. Ind. Math. 13 (2018) 1–18. | MR | Zbl

[5] G Albi, L. Pareschi, G. Toscani and M. Zanella, Recent advances in opinion modeling: Control and social influence, in Active Particles, edited by N. Bellomo, P. Degond and E. Tadmor. Vol. 1 of Modeling and Simulation in Science, Engineering and Technology. Birkhäuser-Springer (2017) 49–98. | DOI | MR

[6] B. Aylaj, N. Bellomo, L. Gibelli and A. Reali, On a unified multiscale vision of behavioral crowds. Math. Models Methods Appl. Sci. 30 (2020) 1–22. | DOI | MR | Zbl

[7] H.-O. Bae, S.-Y. Cho, S.-K. Lee and S.-B. Yun. A particle model for herding phenomena induced by dynamic market signals. J. Stat. Phys. 177 (2019) 365–398. | DOI | MR | Zbl

[8] H.-O. Bae, S.-Y.Cho, J. Kim and S.-B. Yun, A kinetic description for the herding behavior in financial market. J. Stat. Phys. 176 (2019) 398–424. | DOI | MR | Zbl

[9] M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna, E. Cisbani, I. Giardina, V. Lecomte, A. Orlandi, G. Parisi, A. Procaccini, M. Viale, and V. Zdravkovic, Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study. Proc. Natl. Acad. Sci. U. S. A. 105 (2008) 1232–1237. | DOI

[10] N. Bellomo, A. Bellouquid, L. Gibelli and N. Outada, A Quest Towards a Mathematical Theory of Living Systems. Birkhäuser, New York (2017). | DOI | MR

[11] N. Bellomo, R. Bingham, M. A. J. Chaplain, G. Dosi, G. Forni, D. A. Knopoff, J. Lowengrub, R. Twarock, and M. E. Virgillito, A multi-scale model of virus pandemic: Heterogeneous interactive entities in a globally connected world. Math. Models Methods Appl. Sci. 30 (2020) 1591–1651. | DOI | MR | Zbl

[12] N. Bellomo, S. De Nigris, D. Knopoff, M. Morini, and P. Terna, Swarms dynamics towards a systems approach to social sciences and behavioral economy. Netw. Heterogeneous Media 15 (2020) 353–368. | DOI | MR | Zbl

[13] N. Bellomo, G. Dosi, D. A. Knopoff, and M. E. Virgillito, From particles to firms: on the kinetic theory of climbing up evolutionary landscapes. Math. Models Methods Appl. Sci. 30 (2020) 1441–1460. | DOI | MR | Zbl

[14] N. Bellomo, L. Gibelli, and N. Outada, On the interplay between behavioral dynamics and social interactions in human crowds. Kinetic Related Models 12 (2019) 397–409. | DOI | MR | Zbl

[15] N. Bellomo and S.-Y. Ha, A quest toward a mathematical theory of the dynamics of swarms. Math. Models Methods Appl. Sci. 27 (2017) 745–770. | DOI | MR | Zbl

[16] N. Bellomo and J. Soler, On the mathematical theory of the dynamics of swarms viewed as complex systems. Math. Models Methods Appl. Sci. 22, 1140006 (2012). | DOI | MR | Zbl

[17] A. Bellouquid and M. Delitala, Modelling Complex Biological Systems - A Kinetic Theory Approach. Birkhäuser, Boston (2006). | MR | Zbl

[18] U. Biccari, D. Ko and E. Zuazua, Dynamics and control for multi-agent networked systems: A finite-difference approach. Math. Models Methods Appl. Sci. 29 (2019) 755–790. | DOI | MR | Zbl

[19] C. Brugna and G. Toscani, Kinetic models of opinion formation in the presence of personal conviction. Phys. Rev. E 2015 (2015) 0052818. | DOI

[20] D. Burini and N. Chouhad, A Multiscale view of nonlinear diffusion in biology: From cells to tissues. Math. Models Methods Appl. Sci. 29 (2019) 791–823. | DOI | MR | Zbl

[21] D. Burini and S. De Lillo On the complex interaction between collective learning and social dynamics. Symmetry 11 (2019) 967. | DOI

[22] Y.-P. Choi, S.-Y. Ha and Z. Li, Emergent dynamics of the Cucker-Smale flocking model and its variants, in Active Particles, edited by N. Bellomo, P. Degond and E. Admor. Vol. 1 of Modeling and Simulation in Science, Engineering and Technology. Birkhäuser-Springer (2017) 299–231. | DOI | MR

[23] Y.-P. Choi, S.-Y. Ha and J. Morales, Emergent dynamics of the Kuramoto ensemble under the effect of inertia. Discr. Continu. Dyn. Syst. Ser. A 38 (2018) 4875–4913. | DOI | MR | Zbl

[24] F. Cucker and S. Smale, Emergent behavior in flocks. IEEE Trans. Autom. Control 52 (2007) 853–862. | DOI | MR | Zbl

[25] R. Eftimie and L. Gibelli, A kinetic theory approach for modelling tumour and macrophages heterogeneity and plasticity during cancer progression. Math. Models Methods Appl. Sci. 30 (2019) 659–683. | DOI | MR | Zbl

[26] D. Fang, S.-Y. Ha and S. Jin, Emergent behaviors of the Cucker-Smale ensemble under attractive-repulsive couplings and Rayleigh frictions. Math. Models Methods Appl. Sci. 29 (2019) 1349–1385. | DOI | MR | Zbl

[27] H. Frankowska, H. Zang, and X. Zang, Necessary optimality conditions for local minimizers of stochastic optimal control problems with state constraints. Trans. Am. Math. Soc. 372 (2019) 1289–1331. | DOI | MR | Zbl

[28] S.-Y. Ha, E. Jeong, J.-H. Kang and K. Kang, Emergence of multi-cluster configurations from attractive and repulsive interactions. Math. Models Methods Appl. Sci. 22 (2012) 1250013–1250055. | DOI | MR | Zbl

[29] S.-Y. Ha, J. Jung, J. Kim, J. Park and X. Zhang, Emergent behaviors of the swarmalator model for position-phase aggregation. Math. Models Methods Appl. Sci. 29 (2019) 2225–2269. | DOI | MR | Zbl

[30] S.-Y. Ha, D. Kim, D. Kim and W. Shim, Flocking dynamics of the inertial spin model with a multiplicative communication weight. J. Nonlinear Sci. 29 (2019) 1301–1342. | DOI | MR | Zbl

[31] S.-Y. Ha, J. Kim and T. Ruggeri, Emergent behaviors of thermodynamic Cucker-Smale particles. SIAM J. Math. Anal. 50 (2018) 3092–3121. | DOI | MR | Zbl

[32] S.-Y. Ha, J. Kim, J. Park and X. Zhang, Complete cluster predictability of the Cucker-Smale flocking model on the real line. Arch. Ratl. Mech. Anal. 231 (2019) 319–365. | DOI | MR | Zbl

[33] S.-Y. Ha and D. Levy, Particle, kinetic and fluid models for phototaxis. Discr. Continu. Dyn. Syst. Ser. B 12 (2009) 77–108. | MR | Zbl

[34] S.-Y. Ha, H. Park, T. Ruggeri and W. Shim, Emergent behaviors of thermodynamic Kuramoto ensemble on a regular ring lattice. J. Stat. Phys. 181 (2020) 917–943. | DOI | MR | Zbl

[35] S.-Y. Ha and T. Ruggeri, Emergent dynamics of a thermodynamically consistent particle model. Arch. Ratl. Mech. Anal. 223 (2017) 1397–1425. | DOI | MR | Zbl

[36] S.-Y. Ha and E. Tadmor, From particle to kinetic and hydrodynamic description of flocking. Kinetic Related Models 1 (2008) 415–435. | DOI | MR | Zbl

[37] Y. He and X. Mu, Cucker-Smale flocking subject to random failure on general digraphs. Automatica 106 (2019) 54–60. | DOI | MR | Zbl

[38] J.-H. Kang, S.-Y. Ha, K. Kang and E. Jeong, How do cultural classes emerge from assimilation and distinction? An extension of the Cucker-Smale flocking model. J. Math. Sociol. 38 (2014) 47–71. | DOI | MR | Zbl

[39] D. Kim and A. Quaini, A kinetic theory approach to model pedestrian dynamics in bounded domains with obstacles. Kinetic Related Models 12 (2019) 1273–1296. | DOI | MR | Zbl

[40] D. Kim and A. Quaini, Coupling kinetic theory approaches for pedestrian dynamics and disease contagion in a confined environment. Math. Models Methods Appl. Sci. 20 (2020) 1893–1915. | DOI | MR | Zbl

[41] D. Ko and E. Zuazua, Asymptotic behavior and control of a “guidance by repulsion” model. Math. Models Methods Appl. Sci. 20 (2020) 765–804. | DOI | MR | Zbl

[42] Y. Kuramoto, International symposium on mathematical problems in mathematical physics. Lecture Notes Theor. Phys. 30 (1975) 420.

[43] M. Lachowicz, H. Leszczyński and E. Puźniakowska–Galuch, Diffusive and anti-diffusive behavior for kinetic models of opinion dynamics. Symmetry 11 (2019) 1024. | DOI

[44] D. Maity, M. Tucsnak and E. Zuazua, Controllability and positivity constraints in population dynamics with age structuring and diffusion. J. Math. Pure Appl. 129 (2019) 153–179. | DOI | MR | Zbl

[45] L. Pareschi and G. Toscani, Interacting Multiagent Systems: Kinetic Equations and Monte Carlo Methods. Oxford university Press, Oxford (2013). | Zbl

[46] B. Piccoli, N. Pouradier Duteil and E. Trélat, Sparse control of Hegselmann-Krause models: black hole and declustering. SIAM J. Control Optim. 57 (2019) 2628–2659. | DOI | MR | Zbl

[47] D. Poyato and J. Soler, Euler-type equations and commutators in singular and hyperbolic limits of kinetic Cucker-Smale models. Math. Models Methods Appl. Sci. 27 (2017) 1089–1152. | DOI | MR | Zbl

[48] L. Ru, Z. Li and X. Xue, Cucker-Smale flocking with randomly failed interactions. J. Franklin Inst. 352 (2015) 1099–1118. | DOI | MR | Zbl

[49] R.A. Weinberg, The Biology of Cancer. Garland Sciences - Taylor and Francis, New York (2007).

[50] A. Zhigun, C. Surulescu and A. Hunt, A strongly degenerate diffusion-haptotaxis model of tumour invasion under the go-or-grow dichotomy hypothesis. Math. Models Methods Appl. Sci. 28 (2018) 2403–2428. | DOI | MR | Zbl

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Dedicated to the 60th anniversary of Enrique Zuazua.