Agent- and Individual-based Modeling with NetLogo: Introduction and new NetLogo Extensions

Agent-based models (ABM) or individual-based models (IBM), as they are called in ecology and biology, are a widely used modeling approach when local interactions on the micro level are essential for the description of patterns on the macro level. This chapter is divided into four sections. In the first section, the history and definitions of ABMs in various research disciplines, namely computer science, social science, economics and ecology, are reviewed. This section closes with a discussion of similarities and differences in the different research fields and a discussion of current challenges in agent-based modeling. One of these difficulties is the lack of accepted standards for communication and programming. The second section refers to this point by a presentation of some widely used ABM libraries, namely Swarm, Mason, Repast and NetLogo and is followed by a more detailed description of NetLogo as a potential standard tool in ABM communication. In the last section extensions to NetLogo, developed by the authors of this chapter, are presented. This includes the MultiView-Extension, the R-Extension and the NetLogo plug-In for the Pygments syntax highlighter. The chapter closes with an outlook to further tools for NetLogo which aim at making NetLogo even more relevant as a standard tool in ABM. Introduction Building and using models is part of everybody's life. For example, if we wait for the train, we decide where to stay so that we can get into the train fast and get a good seat. We include our former experience about where it is best to find a seat: in the middle, the front or the back of the train. Furthermore we check the other passengers waiting on the track. People carrying heavy luggage are slower than others and so on. But it will be impossible to include all information and details. Therefore, simplification and aggregation of the real system (abstraction) is the key in modeling. STARFIELD et al. (1990) called this " purposeful representation " , which means that the real system is represented only by those elements which are important for answering the question the model is designed for. Thus, the problem to solve should stand at the beginning of the model building process. The second step is the definition of the system and its boundaries which leads to a verbal, conceptual model (BOSSEL 1994) followed by the selection of the formal model structure with scales, state variables, processes and parameters. If …