1800s: Martineau · Tocqueville · Marx · Spencer · Le Bon · Ward · Pareto · Tönnies · Veblen · Simmel · Durkheim · Addams · Mead · Weber · Du Bois · Mannheim · Elias 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 Social dynamics (or sociodynamics) is the study of the behavior of groups and of the interactions of individual group members, aiming to understand the emergence of complex social behaviors among microorganisms, plants and animals, including humans.
[1] In the 1990s, social dynamics began being viewed as a separate scientific discipline[By whom?].
[2] Then, starting in the 2000s, sociodynamics took off as a discipline of its own, many papers were released in the field in this decade.
[3] Research in the field typically takes a behavioral approach, assuming that individuals are boundedly rational and act on local information.
Mathematical and computational modeling are important tools for studying social dynamics.