Description
Introduction to complex systems. Basic concepts of self-organization: positive and negative feedback, symmetry breaking (how random fluctuations create structure), emergence (how simple components can create complex behaviors). Network models: information cascades, epidemics as an information propagation model. Consensus-reaching in multi-agent systems via voting. Examples of self-organized systems from physics, biology, finance. Swarm robotics and swarm algorithms. Decentralized task allocation. Collective motion (flocking, shepherding, milling). Multi-agent consensus and collective decision-making. Optimization by swarm intelligence: ant colony optimization, particle swarm optimization.