Spatial interactions in agent-based modeling

Agent Based Modeling (ABM) has become a widespread approach to model complex interactions. In this chapter after briefly summarizing some features of ABM the different approaches in modeling spatial interactions are discussed. It is stressed that agents can interact either indirectly through a shared environment and/or directly with each other. In such an approach, higher-order variables such as commodity prices, population dynamics or even institutions, are not exogenously specified but instead are seen as the results of interactions. It is highlighted in the chapter that the understanding of patterns emerging from such spatial interaction between agents is a key problem as much as their description through analytical or simulation means. The chapter reviews different approaches for modeling agents' behavior, taking into account either explicit spatial (lattice based) structures or networks. Some emphasis is placed on recent ABM as applied to the description of the dynamics of the geographical distribution of economic activities, - out of equilibrium. The Eurace@Unibi Model, an agent-based macroeconomic model with spatial structure, is used to illustrate the potential of such an approach for spatial policy analysis.Comment: 26 pages, 5 figures, 105 references; a chapter prepared for the book "Complexity and Geographical Economics - Topics and Tools", P. Commendatore, S.S. Kayam and I. Kubin, Eds. (Springer, in press, 2014

Growing Smart Cities

As the world’s population becomes increasingly urbanised the problems of building sustainable cities also grows. Using Susan Stepney’s response, “Mighty Oaks from Little Acorns Grow”, to a science fiction story by Adam Marek titled “Growing Skyscrapers”, this chapter looks at what a living city of the future might look like, and how that might solve some of the problems of the control and development of cities. There is a long history of the application of systems thinking, cybernetics, and complex systems and the growth and control of cities. However, many problems still remain in the deployment and applications of these frameworks and methodologies, and in the potential consequences of their use. However, perhaps many of these could be solved by the development of a living city

The epidemic of innovation - playing around with an agent-based model

The artificial units of an agent-based model can be played around to diffuse innovation and new ideas or act to conserve the status quo, escaping from the advances in technology or organizational methods or new ideas and proposals, exactly as the agents in an epidemic situation can act to diffuse or to avoid the contagion. The emerging structure is obviously a function of the density of the agents, but its behavior can vary in a dramatic way if a few agents are able to evolve some form of intelligent behavior. In our case, intelligent behavior is developed allowing the agents to plan actions using artificial neural networks or, as an alternative, reinforcement learning techniques. The proposed structure of the neural networks is self-developed via a trial and error process: the reinforcement learning model is built upon the Swarm-like agent protocol in Python (SLAPP) tool, a recent implementation of the standard Swarm function library for an agent-based simulation (www.swarm.org), written using Python (www.python.org), a powerful and simple language: the result is also very useful from a didactic perspective. A more powerful tool, the cross targets (CTs) algorithm, is also introduced as a key interpretation and as a perspective methodology; at present, CTs are running only in Swarm; a SLAPP version is under development. A control implementation of the reinforcement learning model has also been developed and placed on-line as an applet, using NetLogo (http://ccl.northwestern.edu/netlogo/).artificial neural networks, reinforcement learning, innovation, agent-based simulation, Swarm protocol, NetLogo,