Ramblemuse^SM Annotated General Bibliography

As urban planning moves from a centralized, top-down approach to a decentralized, bottom-up perspective, our conception of urban systems is changing. In "Cities and Complexity", Michael Batty offers a comprehensive view of urban dynamics in the context of complexity theory, presenting models that demonstrate how complexity theory can embrace a myriad of processes and elements that combine into organic wholes. He argues that bottom-up processes -- in which the outcomes are always uncertain -- can combine with new forms of geometry associated with fractal patterns and chaotic dynamics to provide theories that are applicable to highly complex systems such as cities. Batty begins with models based on cellular automata (CA), simulating urban dynamics through the local actions of automata. He then introduces agent-based models (ABM), in which agents are mobile and move between locations. These models relate to many scales, from the scale of the street to patterns and structure at the scale of the urban region. Finally, Batty develops applications of all these models to specific urban situations, discussing concepts of criticality, threshold, surprise, novelty, and phase transition in the context of spatial developments. Every theory and model presented in the book is developed through examples that range from the simplified and hypothetical to the actual. Deploying extensive visual, mathematical, and textual material, Cities and Complexity will be read both by urban researchers and by complexity theorists with an interest in new kinds of computational models.

An invaluable guide to the complex and increasingly vital study of social simulation. This is a revised, completely updated edition of the practical textbook that examines the techniques of building computer simulations to assist understanding of social and economic issues and problems. Interest in social simulation has been growing rapidly worldwide as a result of increasingly powerful hardware capabilities and software programs. The focus on this area of expertise is also influenced by a rising interest in the application of ideas of complexity, evolution, adaptation, and chaos in the social sciences. This authoritative book details all of the common approaches to social simulation, to provide social scientists with an appreciation of the literature and allow those with some programming skills to create their own simulations. New for this edition are a chapter on how to use simulation as a tool, as well as a chapter on multi-agent systems to explain why and how multi-agent modeling has become the preferred approach to simulation.

Agent-based modeling (ABM) is a technique increasingly used in a broad range of social sciences. It involves building a computational model consisting of "agents", each of which represents an actor in the social world, and an "environment" in which the agents act. Agents are able to interact with each other and are programmed to be pro-active, autonomous and able to perceive their virtual world. The techniques of ABM are derived from artificial intelligence and computer science, but are now being developed independently in research centers throughout the world. In "Agent-Based Models", Nigel Gilbert reviews a range of examples of agent-based modeling, describes how to design and build your own models, and considers practical issues such as verification, validation, planning a modeling project, and how to structure a scholarly article reporting the results of agent-based modeling. It includes a glossary, an annotated list of resources, advice on which programming environment to use when creating agent-based models, and a worked, step-by-step example of the development of an ABM.