Optimizing Resilience in Sports Science Through an Integrated Random Network Structure: Harnessing the Power of Failure, Payoff, and Social Dynamics

被引：0
作者：
Park, Chulwook [1 ,2 ,3 ]
机构：
[1] Seoul Natl Univ, Inst Sport Sci, 71-1 Gwanak 1 Gwanak Ro, Seoul 08826, South Korea
[2] Int Inst Appl Syst Anal, Laxenburg, Austria
[3] Okinawa Inst Sci & Technol, Okinawa, Japan
来源：
SAGE OPEN | 2025年 / 15卷 / 01期
基金：
新加坡国家研究基金会;
关键词：
network structure; agent-based model; systemic risk; strategy; sports system dynamics; PERFORMANCE; KNOWLEDGE; EVOLUTION; INJURIES; FIELD;
D O I：
10.1177/21582440251316513
中图分类号：
C [社会科学总论];
学科分类号：
03 ; 0303 ;
摘要：
This study focuses on understanding risk-aversion behaviours in sports science by examining system dynamics and network structures. Various network models for real-world sports were analyzed, leading to the development of a comprehensive computational algorithm that captures the interactive properties of networked agents. This algorithm dynamically estimates the likelihood of systemic risk propagation while optimizing principles related to failure, reward, and social learning within the network. The findings suggest that despite the inherent risks in sports-centric network structures, the potential for protection can be enhanced through strategically developed, interconnected methods that emphasize appropriate investment. Strong social learning interactions were found to reduce the probability of failure, whereas weaker interactions resulted in a broader distribution of eigenvector centrality, increasing the risk of failure propagation. The study highlights key conceptual and methodological advancements in applying system dynamics to sports science. Furthermore, advanced agent-based network simulations offer deeper insights into the protective potential of interconnected management strategies, offering solutions to mitigate instability and cascading risks in sports.
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页数：15
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共 72 条
[11]  
Cote J., Baker J., Abernethy B., Practice and play in the development of sport expertise, Handbook of sport psychology, pp. 184-202, (2007)
[12]  
Cote J., Salmela J.H., Russell S.J., The knowledge of high-performance gymnastic coaches: Competition and training considerations, Sport Psychologist, 9, 1, pp. 76-95, (2007)
[13]  
Cui S., Gao M., Xun Y., Fung S.F., Tan Y., Zhang Y., Wang C., Wang H., Xiong Y., Research on the structure and characteristics of the overall social network of professional athletes, Complexity, 2021, 1, pp. 1-11, (2021)
[14]  
Dayan P., Abbott L.F., Theoretical neuroscience: Computational and mathematical modeling of neural systems, (2001)
[15]  
Dehmamy N., Milanlouei S., Barabasi A.L., A structural transition in physical networks, Nature, 563, 7733, pp. 676-680, (2018)
[16]  
Duarte R., Araujo D., Correia V., Davids K., Sports teams as superorganisms: Implications of sociobiological models of behaviour for research and practice in team sports performance analysis, Sports Medicine, 42, 8, pp. 633-642, (2012)
[17]  
Durham Y., Hirshleifer J., Smith V.L., Do the rich get richer and the poor poorer? Experimental tests of a model of power, American Economic Review, 88, 4, pp. 970-983, (1998)
[18]  
Erdos P., Renyi A., On Cantor’s series with convergent∑ 1/qn. Ann. Budapest: Universidad Sci Eötvös Sect, Math, 2, pp. 93-109, (1959)
[19]  
Eros P., Schweitzer F., Spatial aspects of information exchange, Advances in Complex Systems, 5, 1, pp. 37-52, (2002)
[20]  
Fawcett T.W., Hamblin S., Giraldeau L.A., Exposing the behavioral gambit: The evolution of learning and decision rules, Behavioral Ecology, 24, 1, pp. 2-11, (2013)
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