Methodological Issues of Spatial Agent-Based Models

Methodological Issues of Agent-Based Modeling in Social Sciences

Agent-based modeling is used in many areas of science. Agent-based modeling has originated in AI, and its initial purpose of use differs from many applications it has nowadays. This shift in the usage of agent-based thinking causes a discontinuation point in the philosophical background of the method. This paper discusses two issues arising from this setting: (2) the history of agentbased model...

Methodological Comparison of Agent Models

The complexity of agent-based spatial models

models aim to demonstrate some basic social process that may lie behind many areas of social life. A good example is Epstein and Axtell’s pioneering book on Growing Artificial Societies (Epstein et al., 1996), which presents a series of successively more complex models of the economics of an artificial society. ... With these models, there is no intention to model any particular empirical case ...

Issues of Representation and Interpretation for Agent-based Models of Complex Adaptive Spatial Systems

What we know of the world around us is, in large measure, the product of reductionist science. The basic tenets of this approach tell us that truth can be found through an understanding of individual system components; a system is the sum of its parts. While this approach served us well through much of the 20 century, many scientists now believe that a reductionist approach alone is insufficien...

Agent-based Models and the Spatial Sciences

Agent-based models (ABMs) are used in the spatial sciences as building-blocks for computer simulation. ABMs have a range of advantageous attributes, not least of which is their flexibility in representing dynamic and highly adaptive physical or human phenomena. ABMs facilitate the exploration of ideas about the myriad of ways that geographical systems develop, behave, interact and evolve, often...