Modeling complex systems
This nice book can serve as an introduction to modeling complex systems, but it can also be used both as a general presentation and as a reference book for anyone wanting to find in a single book on complex systems simultaneously models, modeling tools, examples, theoretical results and interesting applications. The book begins with an introduction explaining what complex systems are, insisting on emergent behavior arising for local interactions of agents and not from the action of a central controller: no central decision but rather self-organization. Two examples are the (global) behavior of ants in an ant colony and the Game of Life of Conway. Complex systems can be modeled by dynamical systems (smooth or discrete), and the rest of the book is devoted first to explaining how to construct a model, then to describe several categories of models: mean-field type models (differential equations, recurrence equations, approaches to chaos), and agent-based models (cellular automata, networks, power-law distributions). For each model, examples of applications are given, from predator-prey to single-population models, from fluid convection to pedestrian traffic, from the small world phenomenon to the prediction of financial markets. Each chapter ends with exercises (and solutions). The book ends with a bibliography of more than 350 items, adding to its very good quality.
scientific article; zbMATH DE number 2134818