kompleksiteten

Kompleksiteten refers to the characteristic of systems composed of many interacting parts whose collective behavior cannot be easily inferred from the properties of the individual components. Such systems exhibit nonlinearity, feedback, emergence, and adaptation, which can lead to unpredictable outcomes and sustained change over time. The concept is used across disciplines, including physics, biology, economics, sociology, and computer science, to analyze natural and engineered phenomena such as ecosystems, markets, urban networks, and software architectures.

Formal notions of complexity include several related ideas. Computational complexity studies the resources—time and space—required by algorithms as a function of input size. Kolmogorov or algorithmic complexity measures the length of the shortest description that generates a given object. In network and agent-based contexts, complexity emerges from the interactions among many units. Complexity science seeks to understand how simple rules at the micro level can produce rich, often unpredictable macro patterns.

Key methods in the field include nonlinear dynamics, chaos theory, network analysis, and agent-based modeling. Central ideas are emergence, self-organization, robustness, and adaptation. A common distinction is used between complex and complicated: a system can be highly complicated, yet predictable if its parts interact in known ways; a complex system displays novel behaviors arising from interactions.

Historically, the study of complex systems grew from systems theory and cybernetics in the 20th century and gained prominence with the development of complexity science in the 1980s and 1990s. Notable contributors include researchers associated with the Santa Fe Institute, as well as theorists such as Stuart Kauffman, John Holland, and Ilya Prigogine. Kompleksets of today continue to inform research on prediction, control, and resilience in diverse domains.