Mechanistic models constituted the first major scientific discourse and paved the way for the future development of science. With the core ideas being the Newtonian laws of motion, the notions of gravity and mass and the perception of time as an arrow the metaphor of the world as a machine took hold.
We lived in a stable clockwork universe just waiting to be described and understood in order to replace chaos and uncertainty with order and predictability. This drive for predictability and control manifested itself in a science which focused its attention on linear phenomena since those mathematical functions could be expressed and used in ways easy to understand and solve.
These linear models focused their attention to negative feedback, or homeostasis, where the product of a reaction leads to a decrease in that reaction. And while this is an essential condition for stability of a system, and thus well suited for dealing with engineering problems and machine design, it does a poor job of describing growth, self-organization and the non-linear relationships where the initial change to a parameter of a system results in an amplification of change elsewhere in the system.
Most sciences now holds the reverse to be true, that linear processes are the exception and not the rule and that nature is fundamentally non-linear.
”Whenever you look at very complicated systems in physics or in biology, you generally find that the basic components and the basic laws are quite simple; the complexity arises because you have a great many of these simple components interacting simultaneously. The complexity is actually in the organisation – the myriad possible ways that the components can interact.” (Stephen Wolfram)
This view is in direct opposition to reductionist approaches where the properties of the system are the mere aggregation of their constituent parts. Complex systems are a dynamic network of many agents acting and reacting to what other agents are doing. The competition and cooperation between those agents produces an overall behavior of the system, which can be said to be emergent. The emergent properties of complex systems are therefore properties that cannot be deduced from the properties of the individual parts. The system is larger than its parts.
And as complex adaptive systems include all living organisms including the social manifestations between them, it also include the adaptions to training. To me this explain why I have almost never seem adaptations to slowly go in one direction only, but to vary around a stable state which then suddenly might be shifted and become the new stable state that physical performance varies around.
The exploration of non-linear functions revealed the phenomena of bifurcations in dynamical systems. Bifurcations are when a small change made to a parameter of a system causes a sudden qualitative change in the systems long-run behavior.
”Systems reach points of bifurcation when their behavior and future pathways becomes unpredictable and new higher order structures may emerge” (John Urry)
For certain inputs the system will respond to all perturbations by settling back to an established steady state. And all of a sudden, when the system reaches a point of bifurcation the system will develop two alternative states that it will settle into depending on the perturbations applied to it. This can also be described as a phase-shift within the system, producing a new behavior, but one cannot tell what stimuli and to what part of the system that will cause such a shift.
In my experience, when it comes to adaptation to training, these shifts appear suddenly, and sometimes from what seems very random and unexpected. But they do not appear to be linear at all, and if anything to be the result of consistent small ”hits” to the various parts of system (in this context this would mean different stimuli to the trainee inside and outside of the training hall). And certainly seldom manifests themselves as slowly advancing from disorder to order, as in the mechanistic worldview that biology and psychology in large have moved past, but exercise science in general still succumb to.
- “The Web of Life: A New Scientific Understanding of Living Systems”, Fritjof Capra, https://books.google.com/books/about/The_Web_of_Life.html…
- “The Scientific Way of Warfare_ Order and Chaos on the Battlefields of Modernity”, Antoine J. Bousquet, https://books.google.com/…/The_Scientific_Way_of_Warfare.ht…
- “Complexity: The Emerging Science at the Edge of Order and Chaos”, Mitchell M. Waldrop, https://books.google.com/books/about/Complexity.html…
- “Global complexity”, John Urry, https://books.google.com/books/about/Global_Complexity.html…