Systems thinking

Collective intelligence Collective action Self-organized criticality Herd mentality Phase transition Agent-based modelling Synchronization Ant colony optimization Particle swarm optimization Swarm behaviour Social network analysis Small-world networks Centrality Motifs Graph theory Scaling Robustness Systems biology Dynamic networks Evolutionary computation Genetic algorithms Genetic programming Artificial life Machine learning Evolutionary developmental biology Artificial intelligence Evolutionary robotics Reaction–diffusion systems Partial differential equations Dissipative structures Percolation Cellular automata Spatial ecology Self-replication Conversation theory Entropy Feedback Goal-oriented Homeostasis Information theory Operationalization Second-order cybernetics Self-reference System dynamics Systems science Systems thinking Sensemaking Variety Ordinary differential equations Phase space Attractors Population dynamics Chaos Multistability Bifurcation Rational choice theory Bounded rationality Systems thinking is a way of making sense of the complexity of the world by looking at it in terms of wholes and relationships rather than by splitting it down into its parts.

[1][2] It has been used as a way of exploring and developing effective action in complex contexts,[3] enabling systems change.

By 1824 the Carnot cycle presented an engineering challenge, which was how to maintain the operating temperatures of the hot and cold working fluids of the physical plant.

[12] In 1868 James Clerk Maxwell presented a framework for, and a limited solution to the problem of controlling the rotational speed of a physical plant.

[14][a] Maxwell's approach, which linearized the equations of motion of the system, produced a tractable method of solution.