1. Abbott, A. (1988). Transcending general linear reality. Sociological theory, 6(2), 169-186. DOI:
10.2307/202114.
2. Bahrke, M. S. (2012). Performance-enhancing substance misuse in sport: Risk factors and considerations for success and failure in intervention programs. Substance Use & Misuse, 47(13-14), 1505-1516. DOI:
10.3109/10826084.2012.705674.
3. Barabási, A. L., & Albert, R. (1999). Emergence of scaling in random networks. science, 286(5439), 509-512. DOI:
10.1126/science.286.5439.509.
4. Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1), 173-215. DOI:
10.1016/0004-3702(94)00005-L.
5. Bernstein, N. (1967). The co-ordination and regulation of movements. Pergamon Press, Oxford.
6. Bittencourt, N. F. N., Meeuwisse, W. H., Mendonça, L. D., Nettel-Aguirre, A., Ocarino, J. M., & Fonseca, S. T. (2016). Complex systems approach for sports injuries: moving from risk factor identification to injury pattern recognition—narrative review and new concept. British journal of sports medicine, 50(21), 1309-1314. DOI:
10.1136/bjsports-2015-095850.
7. Bjørnstad, O. N., Finkenstädt, B. F., & Grenfell, B. T. (2002). Dynamics of measles epidemics: estimating scaling of transmission rates using a time series SIR model. Ecological monographs, 72(2), 169-184.
8. Bohm, D. (1969). Some remarks on the notion of order. In C. H. Waddington (Ed.), Towards a theoretical biology, Volume 2 (pp. 18-40). Chicago: Aldine Publishing Company.
9. Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99 (suppl. 3), 7280-7287. DOI:
10.1073/pnas.082080899.
10. Boza, G., Worsley, S. F., Douglas, W. Y., & Scheuring, I. (2019). Efficient assembly and long-term stability of defensive microbiomes via private resources and community bistability. PLoS computational biology, 15(5), e1007109. DOI:
10.1371/journal.pcbi.1007109.
11. Bruch, E., & Atwell, J. (2015). Agent-based models in empirical social research. Sociological Methods & Research, 44(2), 186-221. DOI:
10.1177/0049124113506405.
12. Casey, M. M., Payne, W. R., & Eime, R. M. (2012). Organisational readiness and capacity building strategies of sporting organisations to promote health. Sport management review, 15(1), 109-124. DOI:
10.1016/j.smr.2011.01.001.
13. Cassidy, T. G., Jones, R. L., & Potrac, P. (2008). Understanding sports coaching: The social, cultural and pedagogical foundations of coaching practice. Routledge. DOI:
10.4324/9780203892923.
14. Cederman, L. E. (2005). Computational models of social forms: Advancing generative process theory. American Journal of Sociology, 110(4), 864-893. DOI:
10.1086/426412.
15. Choi, S. B., Kang, C. W., Choi, H. J., & Kang, B. Y. (2011). Social network analysis for a soccer game. Journal of the Korean Data and Information Science Society, 22(6), 1053-1063.
16. Clemente, F. M., Martins, F. M. L., & Mendes, R. S. (2016). Social network analysis applied to team sports analysis. Netherlands: Springer International Publishing. DOI:
10.1007/978-3-319-25855-3.
17. Colander, D., Föllmer, H., Haas, A., Goldberg, M. D., Juselius, K., Kirman, A., ... & Sloth, B. (2009). The financial crisis and the systemic failure of academic economics. Univ. of Copenhagen Dept. of Economics Discussion Paper, (09-03). DOI:
10.2139/ssrn.1355882.
18. Collins, J. J., & Stewart, I. N. (1993). Coupled nonlinear oscillators and the symmetries of animal gaits. Journal of Nonlinear science, 3, 349-392. DOI:
10.1007/BF02429870.
19. Davids, K., Handford, C., & Williams, M. (1994). The natural physical alternative to cognitive theories of motor behaviour: An invitation for interdisciplinary research in sports science?. Journal of sports Sciences, 12(6), 495-528. DOI:
10.1080/02640419408732202.
20. Dehmamy, N., Milanlouei, S., & Barabási, A. L. (2018). A structural transition in physical networks. Nature, 563(7733), 676. DOI:
10.1038/s41586-018-0726-6.
21. Dunning, E. (1999). Sport matters: Sociological studies of sport, violence, and civilization. Psychology Press.
23. Erdos, P., & Rényi, A. (1959). On Cantor’s series with convergent∑ 1/qn. Ann. Univ. Sci. Budapest. Eötvös. Sect. Math, 2, 93-109.
24. Fawcett, T. W., Fallenstein, B., Higginson, A. D., Houston, A. I., Mallpress, D. E., Trimmer, P. C., & McNamara, J. M. (2014). The evolution of decision rules in complex environments. Trends in cognitive sciences, 18(3), 153-161. DOI:
10.1016/j.tics.2013.12.012.
25. Fawcett, T. W., Hamblin, S., & Giraldeau, L. A. (2012). Exposing the behavioral gambit: the evolution of learning and decision rules. Behavioral Ecology, 24(1), 2-11. DOI:
10.1093/beheco/ars085.
26. Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 117(1), 39. DOI:
10.1037/0033-2909.117.1.39.
28. Gilsing, V., Nooteboom, B., Vanhaverbeke, W., Duysters, G., & van den Oord, A. (2008). Network embeddedness and the exploration of novel technologies: Technological distance, betweenness centrality and density. Research policy, 37(10), 1717-1731. DOI:
10.1016/j.respol.2008.08.010.
29. Goldstone, R. L., & Janssen, M. A. (2005). Computational models of collective behavior. Trends in cognitive sciences, 9(9), 424-430. DOI:
10.1016/j.tics.2005.07.009.
30. Gréhaigne, J. F., Godbout, P., & Bouthier, D. (2001). The teaching and learning of decision making in team sports. Quest, 53(1), 59-76. DOI:
10.1080/00336297.2001.10491730.
31. Grillner, S. (1975). Locomotion in vertebrates: Central mechanisms and reflex interaction. Physiological Reviews, 55, 247-304. DOI:
10.1152/physrev.1975.55.2.247.
32. Grove, J. R., Hanrahan, S. J., & McInman, A. (1991). Success/failure bias in attributions across involvement categories in sport. Personality and Social Psychology Bulletin, 17(1), 93-97. DOI:
10.1177/0146167291171014.
33. Guardiola, X., Guimera, R., Arenas, A., Diaz-Guilera, A., Streib, D., & Amaral, L. A. N. (2002). Macro-and micro-structure of trust networks. arXiv preprint cond-mat/0206240.
34. Haken, H. (2012). Advanced synergetics: instability hierarchies of self-organizing systems and devices. Springer, Berlin.
35. Hajihashemi, M., & Samani, K. A. (2019). Fixation time in evolutionary graphs: A mean-field approach. Physical Review E, 99(4), 042304. DOI:
10.1103/PhysRevE.99.042304.
36. Helbing, D. (2013). Globally networked risks and how to respond. Nature, 497(7447), 51. DOI:
10.1038/nature12047.
37. Hoang, H., & Antoncic, B. (2003). Network-based research in entrepreneurship: A critical review. Journal of business venturing, 18(2), 165-187. DOI:
10.1016/S0883-9026(02)00081-2.
38. Horn, T. S. (2015). Social psychological and developmental perspectives on early sport specialization. Kinesiology Review, 4, 248-266. DOI:
10.1123/kr.2015-0025.
39. Horn, T. S., & Newton, J.L. (2019). Developmentally based perspectives on motivated behavior in sport and physical activity contexts. In T.S. Horn & A.L. Smith (Eds.), Advances in sport and exercise psychology (4th ed., pp. 313-331). Champaign, IL: Human Kinetics.
40. Houston, A. I., McNamara, J. M., & Steer, M. D. (2007). Do we expect natural selection to produce rational behaviour?. Philosophical Transactions of the Royal Society B: Biological Sciences, 362(1485), 1531. DOI:
10.1098/rstb.2007.2051.
41. Hulme, A., Thompson, J., Nielsen, R. O., Read, G. J., & Salmon, P. M. (2019). Towards a complex systems approach in sports injury research: simulating running-related injury development with agent-based modelling. Br J Sports Med, 53(9), 560-569. DOI:
10.1136/bjsports-2017-098871.
42. Hulme, A., & Finch, C. F. (2015). From monocausality to systems thinking: a complementary and alternative conceptual approach for better understanding the development and prevention of sports injury. Injury epidemiology, 2(1), 1-12. DOI:
10.1186/s40621-015-0064-1.
43. Hutchinson, J. M., & Gigerenzer, G. (2005). Simple heuristics and rules of thumb: Where psychologists and behavioural biologists might meet. Behavioural processes, 69(2), 97-124. DOI:
10.1016/j.beproc.2005.02.019.
44. Iberall, A. S., & Soodak, H. (1987). A physics for complex systems. In Self-organizing systems (pp. 499-520). Springer US. DOI:
10.1007/978-1-4613-0883-6_28.
45. Jackson, J. C., Rand, D., Lewis, K., Norton, M. I., & Gray, K. (2017). Agent-based modeling: A guide for social psychologists. Social Psychological and Personality Science, 8(4), 387-395. DOI:
10.1177/1948550617691100.
46. Jarvie, G. (2013). Sport, culture and society: an introduction. Routledge. DOI:
10.4324/9780203883808.
47. Johnson, D. D., & Fowler, J. H. (2011). The evolution of overconfidence. Nature, 477(7364), 317. DOI:
10.1038/nature10384.
48. Kahneman, D. (2003). A perspective on judgment and choice: mapping bounded rationality. American psychologist, 58(9), 697. DOI:
10.1037/0003-066X.58.9.697.
49. King, A. C., Whitt-Glover, M. C., Marquez, D. X., Buman, M. P., Napolitano, M. A., Jakicic, J., ... & Tennant, B. L. (2019). Physical activity promotion: highlights from the 2018 physical activity guidelines advisory committee systematic review. Medicine & Science in Sports & Exercise, 51(6), 1340-1353. DOI:
10.1249/MSS.0000000000001945.
50. Kugler PN, Kelso JS, Turvey MT. (1980). On the concept of coordinative structures as dissipative structures: I. Theoretical lines of convergence. Adv Psychol 1:3-47. DOI:
10.1016/S0166-4115(08)61936-6.
51. Kugler, P. N., & Turvey, M. T. (1987). Information, natural law and the self-assembly of rhythmic movement. Hillsdale, NJ: Erlbaum.
52. Latash, M. L., Scholz, J. P., & Schöner, G. (2007). Toward a new theory of motor synergies. Motor control, 11(3), 276-308. DOI:
10.1123/mcj.11.3.276.
53. Leduc, M., Poledna, S., & Thurner, S. (2017). Systemic risk management in financial networks with credit default swaps. DOI:
10.21314/JNTF.2017.034.
54. Lewis, K. (2015). Three fallacies of digital footprints. Big Data & Society, 2(2), 1-4. DOI:
10.1177/2053951715602496.
55. Lin, N. (2017). Building a network theory of social capital. In Social capital (pp. 3-28). Routledge. DOI:
10.4324/9781315129457-1.
56. Lusher, D., Robins, G., & Kremer, P. (2010). The application of social network analysis to team sports. Measurement in physical education and exercise science, 14(4), 211-224. DOI:
10.1080/1091367X.2010.495559.
57. Marshall, J. A., Trimmer, P. C., Houston, A. I., & McNamara, J. M. (2013). On evolutionary explanations of cognitive biases. Trends in ecology & evolution, 28(8), 469-473. DOI:
10.1016/j.tree.2013.05.013.
58. Miller, N. E., & Dollard, J. (1941). Social learning and imitation, (Yale University, New haven, 1941).
59. Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American sociological review, 69(2), 213-238. DOI:
10.1177/000312240406900204.
60. Narizuka, T., & Yamazaki, Y. (2018). Characterization of the formation structure in team sports. arXiv preprint arXiv:1802.06766.
61. Newell, K. M., & Ranganathan, R. (2010). Instructions as constraints in motor skill acquisition. In Motor learning in practice (pp. 37-52). Routledge.
62. Noteboom, B. (2002). Forms of trust. Trust; Forms, Foundations, Functions, Failures and Figures, Cheltenham, Northampton, 36-54.
63. Oliva, R. (2016). Structural dominance analysis of large and stochastic models. System dynamics review, 32, 26-51.
64. Pacheco, J. M., Traulsen, A., & Nowak, M. A. (2006). Coevolution of strategy and structure in complex networks with dynamical linking. Physical review letters, 97(25), 258103. DOI:
10.1103/PhysRevLett.97.258103.
65. Pain, M. A., & Harwood, C. (2007). The performance environment of the England youth soccer teams. Journal of Sports Sciences, 25(12), 1307-1324. DOI:
10.1080/02640410601059622.
66. Park, C. (2018). Biological autonomy and control of function in circadian cycle. Korean Journal of Sport Science, 29(3), 443-455. DOI:
10.24985/kjss.2018.29.3.443.
67. Park, C. (2020). Evolutionary understanding of the conditions leading to estimation of behavioral properties through system dynamics. Complex Adaptive Systems Modeling, 8(1), 2. DOI:
10.1186/s40294-019-0066-x.
68. Park, C. (2020). Network and Agent Dynamics with Evolving Protection against Systemic Risk. Complexity, 2020. DOI:
10.1155/2020/2989242.
69. Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of modern physics, 87(3), 925. DOI:
10.1103/RevModPhys.87.925.
70. Powell, W. W., White, D. R., Koput, K. W., & Owen-Smith, J. (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American journal of sociology, 110(4), 1132-1205. DOI:
10.1086/421508.
71. Putnam, R. D. (2000). Bowling alone: America’s declining social capital. In Culture and politics (pp. 223-234). Palgrave Macmillan, New York. DOI:
10.1007/978-1-349-62397-6_12.
72. Purves, D., Augustine, G. J., Fitzpatrick, D., Katz, L. C., Lamantia, A. S., McNamara, J. O., & Williams, S. M. (2001). Neuroscience. 2nd. Sunderland: Sinauer.
73. Quatman, C., & Chelladurai, P. (2008). The social construction of knowledge in the field of sport management: A social network perspective. Journal of Sport Management, 22(6), 651-676. DOI:
10.1123/jsm.22.6.651.
74. Railsback, S. F., & Grimm, V. (2019). Agent-based and individual-based modeling: a practical introduction. Princeton university press.
75. Reynolds, C. W. (1987). Flocks, herds and schools: A distributed behavioral model. In ACM SIGGRAPH computer graphics, 21, 25-34. DOI:
10.1145/37402.37406.
76. Ribeiro, J., Silva, P., Duarte, R., Davids, K., & Garganta, J. (2017). Team sports performance analysed through the lens of social network theory: implications for research and practice. Sports medicine, 47(9), 1689-1696. DOI:
10.1007/s40279-017-0695-1.
77. Rinehart, R. E. (2008). Exploiting a new generation. Youth culture and sport: Identity, power, and politics, 71.
78. Robins, G., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social networks, 29(2), 173-191. DOI:
10.1016/j.socnet.2006.08.002.
79. Rosen, R. (1987). Some epistemological issues in physics and biology. In B. J. Hilley & F. D. Platt (Eds.), Quantum implications: Essays in honor of David Bohm (pp. 315-327). New York: Routlege & Kegan.
80. Rosen, S., & Sanderson, A. (2001). Labour markets in professional sports. The economic journal, 111(469), F47-F68. DOI:
10.1111/1468-0297.00598.
81. Schelling, T. C. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143-186. DOI:
10.1080/0022250X.1971.9989794.
82. Shariff, A. F., Willard, A. K., Andersen, T., & Norenzayan, A. (2016). Religious priming a meta-analysis with a focus on prosociality. Personality and Social Psychology Review, 20(1), 27-48. DOI:
10.1177/1088868314568811.
83. Shoham, D. A., Tong, L., Lamberson, P. J., Auchincloss, A. H., Zhang, J., Dugas, L., ... & Luke, A. (2012). An actor-based model of social network influence on adolescent body size, screen time, and playing sports. PloS one, 7(6), e39795. DOI:
10.1371/journal.pone.0039795.
85. Smith, A. L. (2003). Peer relationships in physical activity contexts: A road less traveled in youth sport and exercise psychology research. Psychology of sport and Exercise, 4(1), 25-39. DOI:
10.1016/S1469-0292(02)00015-8.
86. Stevenson, S. J., & Lochbaum, M. R. (2008). Understanding Exercise Motivation: Examining the Revised Social-Cognitive Model of Achievement Motivation. Journal of Sport Behavior, 31(4).
87. Sussillo, D., Churchland, M. M., Kaufman, M. T., & Shenoy, K. V. (2015). A neural network that finds a naturalistic solution for the production of muscle activity. Nature neuroscience, 18(7), 1025-1033. DOI:
10.1038/nn.4042.
88. Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of computational economics: agent-based computational economics (Vol. 2). Amsterdam: Elsevier.
89. Thacker, S. B., Stroup, D. F., Branche, C. M., & Gilchrist, J. (2003). Prevention of knee injuries in sports: A systemic review of the literature. Journal of Sports Medicine and Physical Fitness, 43(2), 165.
90. Trimmer, P. C., Houston, A. I., Marshall, J. A., Mendl, M. T., Paul, E. S., & McNamara, J. M. (2011). Decision-making under uncertainty: biases and Bayesians. Animal cognition, 14(4), 465-476. DOI:
10.1007/s10071-011-0387-4.
91. Turvey, M. T., Fonseca, S. T., & Sternad, D. (2008). Progress in motor control: A multidisciplinary perspective. Progress in motor control: A multidisciplinary perspective.
92. Tversky, A., & Kahneman, D. (1985). The framing of decisions and the psychology of choice. In Environmental Impact Assessment, Technology Assessment, and Risk Analysis (pp. 107-129). Springer Berlin Heidelberg. DOI:
10.1007/978-3-642-70634-9_6.
94. Vilar, L., Araújo, D., Davids, K., & Bar-Yam, Y. (2013). Science of winning soccer: Emergent pattern-forming dynamics in association football. Journal of systems science and complexity, 26(1), 73-84. DOI:
10.1007/s11424-013-2286-z.
95. World Health Organization. (2020). Coronavirus disease 2019 (COVID-19): situation report, 72.
96. Wooldridge, J. M. (2003). Cluster-sample methods in applied econometrics. The American Economic Review, 93(2), 133-138. DOI:
10.1257/000282803321946930.