This article provides comparative perspectives on collective behaviors that are widely found throughout the animal kingdom, ranging from insect and crustacea swarms, fish schools, bird flocks, and mammal herds to human crowds. Studies of nonhuman animal and human collective behaviors have progressed almost separately even though they have a similar history. Theoretical studies have investigated the reproduction of collective phenomena from simple inter-individual rules, and subsequent empirical and experimental studies have found diverse and complex collective behaviors that are difficult to explain with classical theoretical models. As a consequence, a wide variety of interaction rules have been proposed. To determine models to be implemented in nature and find fundamental mechanisms of collective behaviors, this paper argues that we should compare collective behaviors among various species while adopting Tinbergen’s four questions regarding mechanism, function, development, and evolution as a methodological basis. As an example of a comparative collective behavior paradigm, we introduce our studies in which a mutual anticipation mechanism inspired by nonhuman animal collective behaviors can be linked to a self-organization function in human collective behaviors. We expect that the study of comparative collective behaviors will expand, the methodology will become more sophisticated, and new perspectives regarding the multitemporal features of collective behaviors will emerge.

1. [1] I. D. Couzin, “Collective minds,” Nature, Vol.445, p. 715, 2007. https://doi.org/10.1038/445715a
2. [2] J. K. Parrish and L. Edelstein-Keshet, “Complexity, pattern, and evolutionary trade-offs in animal aggregation,” Science, Vol.284, pp. 99-101, 1999. https://doi.org/10.1126/science.284.5411.99
3. [3] D. Sumpter, “Collective animal behavior,” Princeton University Press, 2010.
4. [4] I. D. Couzin, “Collective cognition in animal groups,” Trends in Cognitive Sciences, Vol.13, No.11, pp. 517-524, 2009. https://doi.org/10.1016/j.tics.2008.10.002
5. [5] C. W. Reynolds, “Flocks, Herds, and Schools: A Distributed Behavioral Model,” Comput. Graph., Vol.21, No.4, pp. 25-34, 1987. https://doi.org/10.1145/37402.37406
6. [6] I. Aoki, “A Simulation Study on the Schooling Mechanism in Fish,” Bull. Jpn. Soc. Fish., Vol.40, pp. 1081-1088, 1982. https://doi.org/10.2331/suisan.48.1081
7. [7] A. Okubo, “Dynamical aspects of animal grouping: swarms, schools, flocks, and herds,” Adv. Biophys., Vol.22, pp. 1-94, 1986. https://doi.org/10.1016/0065-227X(86)90003-1
8. [8] C. K. Hemelrijk, H. Hildenbrandt, J. Reinders, and E. J. Stamhuis, “Emergence of oblong school shape: models and empirical data of fish,” Ethology, Vol.116, No.11, pp. 1099-1112, 2010. https://doi.org/10.1111/j.1439-0310.2010.01818.x
9. [9] I. D. Couzin, J. Krause, R. James, G. D. Ruxton, and N. R. Franks, “Collective memory and spatial sorting in animal groups,” J. Theor. Biol., Vol.218, No.1, pp. 1-11, 2002. https://doi.org/10.1006/jtbi.2002.3065
10. [10] I. L. Bajec, N. Zimic, and M. Mraz, “Simulating flocks on the wing: the fuzzy approach,” J. Theor. Biol., Vol.233, No.2, pp. 199-220, 2005. https://doi.org/10.1016/j.jtbi.2004.10.003
11. [11] Y. Vicsek, A. Czirok, E. Ben-Jacob, and O. Shochet, “Novel Type of Phase Transition in a System of Self-Driven Particles,” Phys. Rev. Lett., Vol.75, No.6, pp. 1226-1229, 1995. https://doi.org/10.1103/PhysRevLett.75.1226
12. [12] J. Buhl et al., “From Disorder to Order in marching locusts,” Science, Vol.312, No.5778, pp. 1402-1406, 2006. https://doi.org/10.1126/science.1125142
13. [13] J. Toner and S. Ramaswamy, “Hydrodynamics and phases of flocks,” Ann. Phys., Vol.318, No.1, pp. 170-244, 2005. https://doi.org/10.1016/j.aop.2005.04.011
14. [14] J. Toner and Y. Tu, “Long-range order in a two-dimensional dynamical XY model: how birds fly together?,” Phys. Rev. Lett., Vol.75, pp. 4326-4329, 1995. https://doi.org/10.1103/PhysRevLett.75.4326
15. [15] J. Toner and Y. Tu, “Flocks, herds, and schools: a quantitative theory of flocking,” Phys. Rev. E, Vol.58, pp. 4828-4858, 1998. https://doi.org/10.1103/PhysRevE.58.4828
16. [16] Y. Tu, J. Toner, and M. Ulm, “Sound waves and the absence of Galilean invariance in flocks,” Phys. Rev. Lett., Vol.80, pp. 4819-4822, 1998. https://doi.org/10.1103/PhysRevLett.80.4819
17. [17] D. J. G. Pearce, A. M. Miller, G. Rowlands, and M. S. Turner, “Role of projection in the control of bird flocks,” Proc. Natl Acad. of Sci. USA, Vol.111, No.29, pp. 10422-10426, 2014. https://doi.org/10.1073/pnas.1402202111
18. [18] M. Ballerini et al., “Empirical investigation of starling flocks: A benchmark study in collective animal behavior,” Anim. Behav., Vol.76, pp. 201-215, 2008. https://doi.org/10.1016/j.anbehav.2008.02.004
19. [19] M. Ballerini et al., “Interaction ruling animal collective behavior depends on topological rather than metric distance: evidence from a field study,” Proc. Natl Acad. Sci. USA, Vol.105, No.4, pp. 1232-1237, 2008. https://doi.org/10.1073/pnas.0711437105
20. [20] Y. Katz, K. Tunstrøm, C. C. Ioannou, C. Huepe, and I. D. Couzin, “Inferring the structure and dynamics of interactions in schooling fish,” Proc. Natl Acad. Sci. USA, Vol.108, No.46, pp. 18720-18725, 2011. https://doi.org/10.1073/pnas.1107583108
21. [21] A. Cavagna, S. M. D. Queirós, I. Giardina, F. Stefanini, and M. Viale, “Diffusion of individual birds in starling flocks,” Proc. R. Soc. B, Vol.280, pp. 1471-2954, 2013. https://doi.org/10.1098/rspb.2012.2484
22. [22] S. Bazazi, F. Bartumeus, J. J. Hale, and I. D. Couzin, “Intermittent Motion in Desert Locusts: Behavioral Complexity in Simple Environments,” PLoS Comput. Biol., Vol.8, Article No.e1002498, 2012. https://doi.org/10.1371/journal.pcbi.1002498
23. [23] J. Buhl et al., “From Disorder to Order in Marching Locusts,” Science, Vol.312, No.5778, pp. 1402-1406, 2006. https://doi.org/10.1126/science.1125142
24. [24] A. Cavagna et al., “Scale-free correlations in the starling flocks,” Proc. Natl Acad. Sci. USA, Vol.107, No.26, pp. 11865-11870, 2010. https://doi.org/10.1073/pnas.1005766107
25. [25] A. Procaccini et al., “Propagating waves in starling, Sturnus vulgaris, flocks under predation,” Anim. Behav., Vol.82, pp. 759-765, 2011. https://doi.org/10.1016/j.anbehav.2011.07.006
26. [26] A. J. King et al., “Selfish-herd behaviour of sheep under threat,” Curr. Biol., Vol.22, No.14, pp. 561-562, 2012. https://doi.org/10.1016/j.cub.2012.05.008
27. [27] A. Berdahl, C. J. Torney, C. C. Ioannou, J. Faria, and I. D. Couzin, “Emergent sensing of complex environments by mobile animal groups,” Science, Vol.339, No.6119, pp. 574-576, 2013. https://doi.org/10.1126/science.1225883
28. [28] A. Attanasi et al., “Collective Behaviour without Collective Order in Wild Swarms of Midges,” PLoS Comput. Biol., Vol.10, Article No.e1003697, 2014. https://doi.org/10.1371/journal.pcbi.1003697
29. [29] A. Strandburg-Peshkin, D. R. Farine, I. D. Couzin, and M. C. Crofoot, “Shared decision-making drives collective movement in wild baboons,” Science, Vol.348, No.6241, pp. 1358-1361, 2015. https://doi.org/10.1126/science.aaa5099
30. [30] K. Tunstrøm, Y. Katz, C. C. Ioannou, C. Huepe, M. J. Lutz et al., “Collective States, Multistability and Transitional Behavior in Schooling Fish,” PLoS Comput. Biol., Vol.9, No.2, Article No.e1002915, 2013. https://doi.org/10.1371/journal.pcbi.1002915
31. [31] R. Bastien and P. Romanczuk, “A model of collective behavior based purely on vision,” Science Advances, Vol.6, No.6, Article No.eaay0792, 2020. https://doi.org/10.1126/sciadv.aay0792
32. [32] W. F. Bode, D. W. Franks, and A. J. Wood, “Making Noise: Emergent Stochasticity in Collective Motion,” J. Theor. Biol., Vol.267, No.3, pp. 292-299, 2010. https://doi.org/10.1016/j.jtbi.2010.08.034
33. [33] W. F. Bode, D. W. Franks, and A. J. Wood, “Limited Interactions in Flocks: Relating Model Simulation to Empirical Data,” J. R. Soc. Interface, Vol.8, pp. 301-304, 2010. https://doi.org/10.1098/rsif.2010.0397
34. [34] H. J. Charlesworth and M. S. Turner, “Intrinsically motivated collective motion,” Proc. Natl Acad. Sci., Vol.116, No.31, pp. 15362-15367, 2019. https://doi.org/10.1073/pnas.1822069116
35. [35] P. Romanczuk, I. D. Couzin, and L. Schimansky-Geier, “Collective Motion due to Individual Escape and Pursuit Response,” Phys. Rev. Lett., Vol.102, Article No.010602, 2009. https://doi.org/10.1103/PhysRevLett.102.010602
36. [36] P. Romanczuk and L. Schimansky-Geier, “Swarming and pattern formation due to selective attraction and repulsion,” Interface Focus, Vol.2, pp. 746-756, 2012. https://doi.org/10.1098/rsfs.2012.0030
37. [37] J. Gautrais, F. Ginelli, R. Fournier, S. Blanco, and M. Soria, “Deciphering Interactions in Moving Animal Groups,” PLoS Comput. Biol., Vol.8, No.9, Article No.e1002678, 2012. https://doi.org/10.1371/journal.pcbi.1002678
38. [38] G. Le Bon, “The crowd: A study of the popular mind,” London: Ernest Benn, 1895.
39. [39] C. Feliciani, K. Shimura, and K. Nishinari, “Introduction to Crowd Management: Managing Crowds in the Digital Era: Theory and Practice,” Springer Nature, 2022.
40. [40] D. Helbing and A. Johansson, “Pedestrian, crowd, and evacuation dynamics,” R. A. Meyers (Ed.), “Encyclopedia of Complexity and Systems Science,” Springer, pp. 6476-6495, 2009.
41. [41] D. Helbing and P. Molnar, “Social force model for pedestrian dynamics,” Phy. Rev. E, Vol.51, pp. 4282-4286, 1995. https://doi.org/10.1103/PhysRevE.51.4282
42. [42] D. Helbing, I. Farkas, and T. Vicsek, “Simulating dynamical features of escape panic,” Nature, Vol.407, pp. 487-490, 2000. https://doi.org/10.1038/35035023
43. [43] C. Burstedde, K. Klauck, A. Schadschneider, and J. Zittartz, “Simulation of pedestrian dynamics using a two-dimensional cellular automaton,” Physica A, Vol.295, pp. 507-525, 2001.
44. [44] A. Kirchner, H. Klüpfel, A. Schadschneider, K. Nishinari, and M. Schreckenberg, “Simulation of competitive egress behavior: Comparison with aircraft evacuation data,” Physica A, Vol.324, No.3-4, pp. 689-697, 2003. https://doi.org/10.1016/S0378-4371(03)00076-1
45. [45] M. Moussaïd, N. Perozo, S. Garnier, D. Helbing, and G. Theraulaz, “The walking behaviour of pedestrian social groups and its impact on crowd dynamics,” PLoS ONE, Vol.5, Article No.e10047, 2010. https://doi.org/10.1371/journal.pone.0010047
46. [46] I. Karamouzas, B. Skinner, and S. J. Guy, “Universal power law governing pedestrian interactions,” Phys. Rev. Lett., Vol.113, Article No.238701, 2014. https://doi.org/10.1103/PhysRevLett.113.238701
47. [47] D. Helbing, A. Johansson, and H. Z. Al-Abideen, “Dynamics of crowd disasters: an empirical study,” Phys. Rev. E, Vol.75, Article No.046109, 2007. https://doi.org/10.1103/PhysRevE.75.046109
48. [48] Y. Ma, E. W. M. Lee, M. Shi, and R. K. K. Yuen, “Spontaneous synchronization of motion in pedestrian crowds of different densities,” Nature Human Behaviour, Vol.5, No.4, pp. 447-457, 2021. https://doi.org/10.1038/s41562-020-00997-3
49. [49] D. R. Parisi, A. G. Sartorio, J. R. Colonnello, A. Garcimartín, L. A. Pugnaloni, and I. Zuriguel, “Pedestrian dynamics at the running of the bulls evidence an inaccessible region in the fundamental diagram,” Proc. of the National Academy of Sciences, Vol.118, No.50, Article No.e2107827118, 2021. https://doi.org/10.1073/pnas.2107827118
50. [50] M. Moussaïd, D. Helbing, and G. Theraulaz, “How simple rules determine pedestrian behavior and crowd disasters,” Proc. Natl Acad. Sci. U.S.A., Vol.108, No.17, pp. 6884-6888, 2011. https://doi.org/10.1073/pnas.1016507108
51. [51] A. Johansson, “Constant-net-time headway as a key mechanism behind pedestrian flow dynamics,” Phys. Rev. E, Vol.80, Article No.026120, 2009. https://doi.org/10.1103/PhysRevE.80.026120
52. [52] A. Johansson, D. Helbing, and P. K. Shukla, “Specification of the social force pedestrian model by evolutionary adjustment to video tracking data,” Adv. Complex Syst., Vol.10, pp. 271-288, 2007. https://doi.org/10.1142/S0219525907001355
53. [53] A. Corbetta, J. A. Meeusen, C. M. Lee, R. Benzi, and F. Toschi, “Physics-based modeling and data representation of pairwise interactions among pedestrians,” Physical Review E, Vol.98, Article No.062310, 2018. https://doi.org/10.1103/PhysRevE.98.062310
54. [54] S. Nowak and A. Schadschneider, “Quantitative analysis of pedestrian counterflow in a cellular automaton model,” Phys. Rev. E, Vol.85, Article No.066128, 2012. https://doi.org/10.1103/PhysRevE.85.066128
55. [55] Q. Xu, M. Chraibi, and A. Seyfried, “Anticipation in a velocity-based model for pedestrian dynamics,” Transportation Research Part C: Emerging Technologies, Vol.133, Article No.103464, 2021. https://doi.org/10.1016/j.trc.2021.103464
56. [56] Y. X. Lü, Z. X. Wu, and J. Y. Guan, “Pedestrian dynamics with mechanisms of anticipation and attraction,” Physical Review Research, Vol.2, Article No.043250, 2020. https://doi.org/10.1103/PhysRevResearch.2.043250
57. [57] I. Echeverría-Huarte and A. Nicolas, “Anticipating Collisions, Navigating in Complex Environments, Elbowing, Pushing, and Smartphone-Walking: A Versatile Agent-Based Model for Pedestrian Dynamics,” arXiv Preprint, arXiv:2211.03419, 2022. https://doi.org/10.48550/arXiv.2211.03419
58. [58] M. Boeckle, M. Schiestl, A. Frohnwieser, R. Gruber, R. Miller, T. Suddendorf, and N. S. Clayton, “New Caledonian crows plan for specific future tool use,” Proc. of the Royal Society B, Vol.287, Article No.20201490, 2020. https://doi.org/10.1098/rspb.2020.1490
59. [59] K. Kagaya, T. Kubota, and K. Nakajima, “Self-organized criticality for dendritic readiness potential,” arXiv Preprint, arXiv:2209.09075, 2022. https://doi.org/10.48550/arXiv.2209.09075
60. [60] G. Rizzolatti, L. Fogassi, and V. Gallese, “Neurophysiological mechanisms underlying the understanding and imitation of action,” Nature Reviews Neuroscience, Vol.2, pp. 661-670, 2001. https://doi.org/10.1038/35090060
61. [61] E. C. Tolman, “Cognitive maps in rats and men,” Psychological Review, Vol.55, No.4, pp. 189-208, 1948. https://doi.org/10.1037/h0061626
62. [62] C. Darwin, “Origin of certain instincts,” Nature, Vol.7, pp. 417-418, 1873. https://doi.org/10.1038/007417a0
63. [63] G. G. Gallup Jr., “Chimpanzees: self-recognition,” Science, Vol.167, No.3914, pp. 86-87, 1970. https://doi.org/10.1126/science.167.3914.86
64. [64] M. Kohda, S. Sogawa, A. L. Jordan, N. Kubo, S. Awata, S. Satoh, T. Kobayashi, A. Fujita, and R. Bshary, “Further evidence for the capacity of mirror self-recognition in cleaner fish and the significance of ecologically relevant marks,” PLoS Biology, Vol.20, Article No.e3001529, 2022. https://doi.org/10.1371/journal.pbio.3001529
65. [65] K. W. Rio, G. C. Dachner, and W. H. Warren, “Local interactions underlying collective motion in human crowds,” Proc. of the Royal Society B: Biological Sciences, Vol.285, Article No.20180611, 2018. https://doi.org/10.1098/rspb.2018.0611
66. [66] G. C. Dachner, T. D. Wirth, E. Richmond, and W. H. Warren, “The visual coupling between neighbours explains local interactions underlying human ‘flocking’,” Proc. of the Royal Society B, Vol.289, Article No.20212089, 2022. https://doi.org/10.1098/rspb.2021.2089
67. [67] N. Tinbergen, “On aims and methods of ethology,” Zeitschrift für Tierpsychologie, Vol.20, No.4, pp. 410-433, 1963. https://doi.org/10.1111/j.1439-0310.1963.tb01161.x
68. [68] R. Harpaz, A. C. Aspiras, S. Chambule, S. Tseng, M. A. Bind, F. Engert, M. C. Fishman, and A. Bahl, “Collective behavior emerges from genetically controlled simple behavioral motifs in zebrafish,” Science Advances, Vol.7, No.41, Article No.eabi7460, 2021. https://doi.org/10.1126/sciadv.abi7460
69. [69] W. Tang, J. D. Davidson, G. Zhang, K. E. Conen, J. Fang, F. Serluca, J. Li, X. Xiong, M. Coble, T. Tsai, G. Molind, C. H. Fawcett, E. Sanchez, P. Zhu, I. D. Couzin, and M. C. Fishman, “Genetic Control of Collective Behavior in Zebrafish,” iScience, Vol.23, No.3, Article No.100942, 2020. https://doi.org/10.1016/j.isci.2020.100942
70. [70] C. C. Ioannou, V. Guttal, and I. D. Couzin, “Predatory fish select for coordinated collective motion in virtual prey,” Science, Vol.337, No.6099, pp. 1212-1215, 2012. https://doi.org/10.1126/science.1218919
71. [71] A. Berdahl, C. J. Torney, C. C. Ioannou, J. Faria, and I. D. Couzin, “Emergent sensing of complex environments by mobile animal groups,” Science, Vol.339, No.6119, pp. 574-576, 2013. https://doi.org/10.1126/science.1225883
72. [72] C. Doran, D. Bierbach, J. Lukas, P. Klamser, T. Landgraf, H. Klenz, and J. Krause, “Fish waves as emergent collective antipredator behavior,” Current Biology, Vol.32, No.3, pp. 708-714, 2022. https://doi.org/10.1016/j.cub.2021.11.068
73. [73] N. Mizumoto, S. Miyata, and S. C. Pratt, “Inferring collective behavior from a fossilized fish shoal,” Proc. of the Royal Society B, Vol.286, Article No.20190891, 2019. https://doi.org/10.1098/rspb.2019.0891
74. [74] B. P. Burford, R. R. Williams, N. J. Demetras, N. Carey, J. Goldbogen, W. F. Gilly, and M. W. Denny, “The limits of convergence in the collective behavior of competing marine taxa,” Ecology and Evolution, Vol.12, No.3, Article No.e8747, 2022. https://doi.org/10.1002/ece3.8747
75. [75] R. C. Hinz and G. G. de Polavieja, “Ontogeny of collective behavior reveals a simple attraction rule,” Proc. of the National Academy of Sciences, Vol.114, No.9, pp. 2295-2300, 2017. https://doi.org/10.1073/pnas.1616926114
76. [76] C. von Krüchten and A. Schadschneider, “Empirical study on social groups in pedestrian evacuation dynamics,” Phys. A, Vol.475, pp. 129-141, 2017. https://doi.org/10.1016/j.physa.2017.02.004
77. [77] S. J. Gould and E. S. Vrba, “Exaptation—a missing term in the science of form,” Paleobiology, Vol.8, No.1, pp. 4-15, 1982. https://doi.org/10.1017/S0094837300004310
78. [78] H. Murakami, T. Tomaru, Y. Nishiyama, T. Moriyama, T. Niizato, and Y. P. Gunji, “Emergent runaway into an avoidance area in a swarm of soldier crabs,” PLoS One, Vol.9, Article No.e97870, 2014. https://doi.org/10.1371/journal.pone.0097870
79. [79] T. Tomaru, H. Murakami, T. Niizato, Y. Nishiyama, K. Sonoda, T. Moriyama, and Y. P. Gunji, “Information transfer in a swarm of soldier crabs,” Artificial Life and Robotics, Vol.21, pp. 177-180, 2016. https://doi.org/10.1007/s10015-016-0272-y
80. [80] Y. P. Gunji, H. Murakami, T. Niizato, K. Sonoda, and A. Adamatzky, “Passively Active – Actively Passive Mutual Anticipation in a Communicative Swarm,” P. L. Simeonov, L. S. Smith, and A. C. Ehresmann (Eds.), “Integral Biomathics,” pp. 169-180, Springer, Berlin, Heidelberg, 2012. https://doi.org/10.1007/978-3-642-28111-2_16
81. [81] H. Murakami, T. Tomaru, T. Niizato, Y. Nishiyama, K. Sonoda, T. Moriyama, and Y. P. Gunji, “Collective behavior of soldier crab swarm in both ring-and round-shaped arenas,” Artificial Life and Robotics, Vol.20, pp. 315-319, 2015. https://doi.org/10.1007/s10015-015-0232-y
82. [82] Y. P. Gunji, H. Murakami, T. Niizato, A. Adamatzky, Y. Nishiyama, M. Toda, and K. Enomoto, “An embodied swarm in co-creation,” Proc. SICE Annual Conf. 2011, pp. 2587-2589, 2011.
83. [83] Y. P. Gunji, H. Murakami, T. Tomaru, and V. Basios, “Inverse Bayesian inference in swarming behaviour of soldier crabs,” Philos. Trans. R. Soc. A, Vol.376, No.2135, Article No.20170370, 2018. https://doi.org/10.1098/rsta.2017.0370
84. [84] Y. P. Gunji, T. Kawai, H. Murakami, T. Tomaru, M. Minoura, and S. Shinohara, “Lévy walk in swarm models based on Bayesian and inverse Bayesian inference,” Computational and Structural Biotechnology J., Vol.19, pp. 247-260, 2021. https://doi.org/10.1016/j.csbj.2020.11.045
85. [85] H. Murakami, T. Niizato, Y. Nishiyama, and Y. P. Gunji, “Inherent noise appears as a Lévy walk in fish schools,” Scientific Reports, Vol.5, No.1, Article No.10605, 2015. https://doi.org/10.1038/srep1060
86. [86] T. Niizato, K. Sakamoto, Y. I. Mototake, H. Murakami, T. Tomaru, T. Hoshika, and T. Fukushima, “Finding continuity and discontinuity in fish schools via integrated information theory,” PLoS One, Vol.15, No.2, Article No.e0229573, 2020. https://doi.org/10.1371/journal.pone.0229573
87. [87] T. Niizato, K. Sakamoto, Y. I. Mototake, H. Murakami, T. Tomaru, T. Hoshika, and T. Fukushima, “Four-types of IIT-induced group integrity of Plecoglossus altivelis,” Entropy, Vol.22, No.7, Article No.726, 2020. https://doi.org/10.3390/e22070726
88. [88] H. Murakami, T. Niizato, and Y. P. Gunji, “A model of scale-free proportion based on mutual anticipation,” Int. J. of Artificial Life Research (IJALR), Vol.3, No.1, pp. 34-44, 2012. https://doi.org/10.4018/jalr.2012010104
89. [89] H. Murakami, T. Niizato, and Y. P. Gunji, “Emergence of a coherent and cohesive swarm based on mutual anticipation,” Sci. Rep., Vol.7, Article No.46447, 2017. https://doi.org/10.1038/srep46447
90. [90] P. Gerlee, K. Tunstrøm, T. Lundh, and B. Wennberg, “Impact of anticipation in dynamical systems,” Phys. Rev. E, Vol.96, No.6, Article No.062413, 2017. https://doi.org/10.1103/PhysRevE.96.062413
91. [91] A. Morin, J. B. Caussin, C. Eloy, and D. Bartolo, “Collective motion with anticipation: Flocking, spinning, and swarming,” Phys. Rev. E, Vol.91, No.1, Article No.012134, 2015. https://doi.org/10.1103/PhysRevE.91.012134
92. [92] D. Strömbom and A. Antia, “Anticipation Induces Polarized Collective Motion in Attraction Based Models,” Northeast J. of Complex Systems, Vol.3, No.1, Article No.2, 2021. https://doi.org/10.22191/nejcs/vol3/iss1/2
93. [93] H. Charlesworth and M. Turner, “Intrinsically motivated collective motion,” Proc. of the National Academy of Sciences, Vol.116, No.31, pp. 15362-15367, 2019. https://doi.org/10.1073/pnas.1822069116
94. [94] H. Murakami, C. Feliciani, and K. Nishinari, “Lévy walk process in self-organization of pedestrian crowds,” J. R. Soc. Interface, Vol.16, No.153, Article No.20180939, 2019. https://doi.org/10.1098/rsif.2018.0939
95. [95] H. Murakami, Y. Nishiyama, C. Feliciani, and K. Nishinari, “Mutual anticipation can contribute to self-organization in human crowds,” Science Advances, Vol.7, No.12, Article No.eabe7758, 2021. https://doi.org/10.1126/sciadv.abe7758
96. [96] H. Murakami, T. Tomaru, C. Feliciani, and Y. Nishiyama, “Spontaneous behavioral coordination between avoiding pedestrians requires mutual anticipation rather than mutual gaze,” iScience, Vol.25, No.11, Article No.105474, 2022. https://doi.org/10.1016/j.isci.2022.105474
97. [97] V. Hladký and J. Havlíček, “Was Tinbergen an Aristotelian? Comparison of Tinbergen’s four whys and Aristotle’s four causes,” Human Ethology Bulletin, Vol.28, No.4, pp. 3-11, 2013.
98. [98] T. Sakiyama and Y. P. Gunji, “The Müller-Lyer illusion in ant foraging,” PLoS One, Vol.8, No.12, Article No.81714, 2013. https://doi.org/10.1371/journal.pone.0081714
99. [99] Y. P. Gunji, Y. Nishiyama, and A. Adamatzky, “Robust soldier crab ball gate,” Complex Systems, Vol.20, No.2, pp. 94-104, 2011. https://doi.org/10.25088/ComplexSystems.20.2.93
100. [100] Y. Nishiyama, Y. P. Gunji, and A. Adamatzky, “Collision-based computing implemented by soldier crab swarms,” Int. J. of Parallel, Emergent and Distributed Systems, Vol.28, No.1, pp. 67-74, 2013. https://doi.org/10.1080/17445760.2012.662682