Facing Complexity: Prediction vs. Adaptation

TL;DR

This paper discusses the limitations of predictability in complex systems and explores adaptation as a crucial alternative for handling unforeseen and rapidly changing problem environments.

Contribution

It highlights the contrast between prediction and adaptation in complex systems, emphasizing the importance of adaptive approaches when predictability fails.

Findings

Predictability is limited in chaotic and complex systems.

Optimization may become obsolete in non-predictable environments.

Adaptive systems can find new solutions for unforeseen situations.

Abstract

One of the presuppositions of science since the times of Galileo, Newton, Laplace, and Descartes has been the predictability of the world. This idea has strongly influenced scientific and technological models. However, in recent decades, chaos and complexity have shown that not every phenomenon is predictable, even if it is deterministic. If a problem space is predictable, in theory we can find a solution via optimization. Nevertheless, if a problem space is not predictable, or it changes too fast, very probably optimization will offer obsolete solutions. This occurs often when the immediate solution affects the problem itself. An alternative is found in adaptation. An adaptive system will be able to find by itself new solutions for unforeseen situations.