Adaptive system

The term adaptation arises mainly in the biological scope as a trial to study the relationship between the characteristics (anatomic structure, physiological processes or behavior) of living beings and their environments. Currently, in biology, the term adaptation has a clear and concise meaning: a biological adaptation is an anatomic structure, a physiological process or a behavior's trait of an organism that has been selected by the natural evolution in such a way that this characteristic increase the probability of reproduction of an organism.

An adaptive system is a set of interacting or interdependent entities, real or abstract, forming an integrated whole that together are able to respond to environmental changes or changes in the interacting parts. Feedback loops represent a key feature of adaptive systems, allowing the response to changes; examples of adaptive systems include: natural ecosystems, individual organisms, human communities, human organizations, and human families.

Some artificial systems can be adaptive as well; for instance, robots employ control systems that utilize feedback loops to sense new conditions in their environment and adapt accordingly.

Contents

The Law of Adaptation

Every adaptive system converges to a state in which all kind of stimulation ceases.[1]

A formal definition of the Law of Adaptation is as follows:

Given a system S, we say that a physical event E is a stimulus for the system S if and only if the probability P(S \rightarrow S'|E) that the system suffers a change or be perturbed (in its elements or in its processes) when the event E occurs is strictly greater than the prior probability that S suffers a change independently of E:

P(S \rightarrow S'|E)>P(S \rightarrow S')

Let S be an arbitrary system subject to changes in time t and let E be an arbitrary event that is a stimulus for the system S: we say that S is an adaptive system if and only if when t tends to infinity (t\rightarrow \infty) the probability that the system S change its behavior (S\rightarrow S') in a time step t_0 given the event E is equal to the probability that the system change its behavior independently of the occurrence of the event E. In mathematical terms:

  1. - P_{t_0}(S\rightarrow S'|E) > P_{t_0}(S\rightarrow S') > 0
  2. - \lim_{t\rightarrow \infty} P_t(S\rightarrow S' | E) = P_t(S\rightarrow S')

Thus, for each instant t will exist a temporal interval h such that:

P_{t%2Bh}(S\rightarrow S' | E) - P_{t%2Bh}(S\rightarrow S') < P_t(S\rightarrow S' | E) - P_t(S\rightarrow S')

Benefit of Self-Adjusting Systems

In an adaptive system, a parameter changes slowly and has no preferred value. In a self-adjusting system though, the parameter value “depends on the history of the system dynamics”. One of the most important qualities of self-adjusting systems is its “adaption to the edge of chaos” or ability to avoid chaos. Practically speaking, by heading to the edge of chaos without going further, a leader may act spontaneously yet without disaster. A March/April 2009 Complexity article further explains the self-adjusting systems used and the realistic implications.[2]

See also

References

  1. ^ José Antonio Martín H., Javier de Lope and Darío Maravall: "Adaptation, Anticipation and Rationality in Natural and Artificial Systems: Computational Paradigms Mimicking Nature" Natural Computing, December, 2009. Vol. 8(4), pp. 757-775. doi
  2. ^ Hübler, A. & Wotherspoon, T.: "Self-Adjusting Systems Avoid Chaos". Complexity. 14(4), 8 – 11. 2008