Classical training theory is deeply infl uenced by a mechanical conception and a Cartesian view of athletes. Although the natural limitations of this classical approach are recognized, training methods are largely based on it. Nowa-days, Dynamic Systems Theory is offering new tools to explain the behavior of the neuromuscular system and very useful principles to be applied to sports training (Kelso, 1999; Kurz, Stergiou, 2004). Instead of being thought of as machines, athletes are considered as complex dynamic systems, self-organized and constrained by morphological, physiological, psychological and biomechanical factors, the properties of the task and the environment. Due to this complexity, they are noticeably dependant on their initial condition and the distribution of attractors, showing fl u-ctuations when passing from one attractor to another. The mechanism of adaptation to training, observed as a self-organization process, is transforming modern training stimuli and expected performance responses. Training loads should encourage the process of self-organization in an integrated, overall way, changing the environment and the conditions to constrain the subject in the desired direction of the training process. The principle of individuality not only focuses on inputs but also on the outputs promoting the variability of the athlete’s responses to each changing competition and training situation. In conclusion, Dynamic Systems Theory is changing the view of mechanisms of adaptation to training and introducing important changes into performance targets and training methods, challenging scientists and modern coaches to fi nd suitable solutions to optimize the training process.Keywords: self-organization, attractor, fluctuation, variability, stability.
Improving the Expected Performance of Self-Organization in a Collective Adaptive System of Drones using Stochastic Multiplayer Games
