Simple Rules, Complex Dynamics – Part I: Foundations & Intuition
Table of Contents Introduction and how to read this post Some simple ideas and baselines - growth, local linearity and equilibrium Exponential and logistic growth Oscillators and local linearity Invariants and conservation A quick primer on bifurcations Oscillations from feedback and delay Lotka-Volterra predator-prey model SEIR model with vital dynamics Thresholds, alignment, and phase transitions Granovetter’s threshold model for collective behavior The Ising model: alignment, noise and criticality Schelling’s model of segregation Reinforcement, herding, and heavy tails Polya’s urn: reinforcement and path dependence Kirman-Folmer herding: bistability Sandpiles: self-organized criticality and avalanches Multiplicative growth and Kesten tails Swarms and distributed coordination Vicsek alignment: headings, noise, and a mean-field self-consistency Cucker-Smale: continuous-time velocity alignment and convergence Hydrodynamics and long-range order Selection dynamics Replicator dynamics Replicator-mutator dynamics Ricardian trade as selection of specializations Spatial structure: patterns from local rules Reaction-diffusion and diffusion-driven instability Traveling fronts: Fisher-KPP Other patterns Some common threads A toolbox Main objects of study Linearization and local analysis 2D phase-plane portraits Lyapunov functions and LaSalle’s invariance Bifurcations Discrete-time systems and chaos Delays: compartments vs true delay differential equations Invariants, positivity, comparison, monotone structure Networks and coupling Non-dimensionalization and scaling Stochastic dynamics and metastability Traveling fronts Contraction analysis Singular perturbations and slow-fast decompositions Koopman operator and data-driven surrogates Ergodic theory and mixing Non-smooth and hybrid dynamics (thresholds and edges) Control and feedback Numerical methods that respect structure References and Further Reading Acknowledgements Introduction and how to read this post Simple local interactions can lead to a large variety of global behavior. Many examples come from physics, biology, economics, and ML, but the machinery used to look at them remains essentially the same. Looking at things through a certain lens - asking questions such as “What’s the feedback?”, “How are things coupled?”, “Is there noise?”, “What are the timescales?” - can help intuit the macroscopic outcomes of these systems: growth, cycles, alignment and discontinuities, swarms, heavy tails, and different spatio-temporal patterns. ...