Simplicity in Complexity: An Introduction to Complex Systems
How do scientists model crowd behaviour, epidemics, earthquakes or the internet?
What can we learn from the collective intelligence and adaptability
of an ant colony?
This book answers such questions by highlighting common themes in
the study of complex systems.
Topics covered include self-organisation, emergence, agent-based
simulations, complex networks, phase plane plots, fractals, chaos,
measures of complexity, model building, and the scientific method.
Explanations are simple and concise, with common misconceptions
clarified. Numerous exercises help enthusiasts consolidate their
understanding through peer learning.
Supplementary resources are at the companion websites
www.simplicitysg.net/books and www.facebook.com/simcomty.
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agent-based models agents algorithmic complexity ants attractor average path length behaviour boid Cantor set cells chaos chaotic chapter clustering coefficient colony complex networks complex systems degree distribution described deterministic differential equations Discuss disorder dynamical emergent laws entropy equilibrium example of emergence Exercises experiment Explain Figure fixed points food source Further Reading Game human illustrates infected initial conditions interaction Koch curve Koch snowflake line segments linear logistic equation Menger Sponge molecules natural fractals NetLogo Newton’s nodes Nonlinear nullclines observed organisation parameters particle Parwani patterns phase plane plot phase space phenomena phenomenon physical population positive constants positive feedback power law predict problem qualitatively queue rabbits random networks real networks real-world References and Further represents Rossler system scale scale-free networks Science scientific method scientists Second Law self-organised criticality self-similar Sierpinski carpet Simplicity simulation small-fish social solution stable Strogatz symmetry termite Thermodynamics tion topological dimension trajectories Turing variables