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HISTORY The model was developed through a simple conceptual form in the late 1940s , and it took the advent of the microcomputer to really get up to speed. The idea is to construct the computational devices (known as agents with some properties) and then, simulate them in parallel to model the real phenomena. The process is one of Emergence from the lower (micro) level of the social system to the higher level (macro). The history of the agent based model can be traced back to the Von Neumann Machine , a theoretical machine capable of reproduction. The device Von Neumann proposed would follow precisely detailed instructions to fashion a copy of itself. The concept was then improved by von Neumann's friend Stanislaw Ulam , also a mathematician; Ulam suggested that the machine be built on paper, as a collection of cells on a grid. The idea intrigued von Neumann, who drew it up—creating the first of the devices later termed Cellular Automata . Another improvement was brought by mathematician, John Conway . He constructed the well-known Game Of Life . Unlike the von Neumann's machine, Conway's Game of Life operated by tremendously simple rules in a virtual world in the form of a 2-dimensional Checkerboard . The birth of agent based model as a model for social systems was primarily brought by a computer scientist, Craig Reynold . He tried to model the reality of lively biological agents, known as the Artificial Life , a term coined by Christopher Langton . THEORY The predominant methodological approach to research involving computational modeling characterizes most systems as in Equilibrium or as moving between equilibria. But agent based modeling, using simple rules, can result in far more complex and interesting behavior The three ideas central to agent based models are social agents as objects, Emergence and Complexity . Agent based models consist of dynamically interacting rule based agents. The systems within which they interact can therefore create complexity like that which we see in the real world. These agents are:
On some levels, agent based models complement traditional analytic methods. Whereas, analytic methods enable us to characterize the equilibria of a system, agent based models allow us to explore the possibility of generating those equilibria. This generative contribution may be the most mainstream of the potential benefits of agent based modeling. Agent based models can explain the emergence of higher order patterns -- network structures of terrorist organizations and the Internet, Power Law Distributions in the sizes of traffic jams, wars, and stock market crashes, and segregation despite populations of tolerant people. Agent based models also can be used to identify lever points, moments in time in which interventions have extreme consequences, and to distinguish among types of path dependency. Rather than focus on an equilibrium's stability -- the idea that a process returns to that equilibrium – we consider instead a system's robustness -- the idea that the system adapts to internal and external pressures so as to maintain functionalities. The task of harnessing that complexity requires consideration of the agents themselves -- their diversity, connectedness, and level of interactions. METHODS A simple and accessible program for creating agent based models is NetLogo , originally designed for middle schoolers. Many colleges have used this as a tool to teach their students about agent based modeling. A similar program, StarLogo , has also been released with similar functionality. For more advanced programmers, Repast is a Java-based agent based modeling system. AnyLogic is a commercial tool that allows graphical development of agent based models, system dynamics, as well as discrete event models. Anylogic produces Java code that can be enhanced and extended. The graphical environment allows newcomers to get started easily while providing many capabilities for advanced developers. SEE ALSO
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