Behavior-driven agent-based models of spatial systems

Xinyue Ye, Yuri Mansury
2016 The annals of regional science  
Human behavior can produce complex patterns in spatial systems made up of large numbers of interacting individuals. An inquiry into the nature of spatial patterns is therefore incomplete without an understanding of the human behavior that gives rise to these patterns (Xie et al. in Ann Assoc Am Geogr 97(3): [477][478][479][480][481][482][483][484][485][486][487][488][489][490][491][492][493][494][495] 2007). The treatment of pattern formation in space with interacting individuals, however, is
more » ... emendously difficult using purely mathematical abstractions. This special issue is an effort to promote new behavioral approaches to the class of computational platforms known as agent-based models. JEL Classification R1 General Regional Economics · C63 Computational Techniques; Simulation Modeling · D010 Microeconomic Behavior; Underlying Principles Computational agent-based models (ABMs) since inception have been recognized as a powerful tool to help understand the behavior of complex spatial systems (Batty 2007) . This class of models is "agent based" because it seeks to explain emerging spatial orders (the systems behavior) in terms of local interactions among decisionmakers (agents) as well as between them and the environment. Cities and regions are quintessential examples of complex systems made up of interacting agents from B Yuri Mansury
doi:10.1007/s00168-016-0792-3 fatcat:2gjt6x5rhbgfzn6wingg25dy4e