Case Study: Agent-based simulations

Jun 21st, 2015
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The term “agent-based” refers to a particular type of simulation. Agent-based simulations have two essential components, agents and environment. An agent’s behavior is the result of simple rules based on local interactions. The environment has certain autonomy, i.e. it has a certain level of independence from what the agents do, but it can also be influenced by the agents’ behavior.

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Case Study: Agent-based simulationsThe term agent-based refers to a particular type of simulation. Agent-based simulations have two essential components, agents and environment. An agents behavior is the result of simple rules based on local interactions. The environment has certain autonomy, i.e. it has a certain level of independence from what the agents do, but it can also be influenced by the agents behavior. The interaction of agents among each other, as well as the interaction of agents with the surrounding environment is modeled. This is in contrast to more traditional simulations where often systems of differential equations are integrated.Examples of agent-based simulations are Josh Epstein and Bob Axtells Sugarscape model (Epstein and Axtell 1996), John Castis Would-be worlds (Casti, 1997), Hemelrijks simulations of artificial monkeys (Hemelrijk, 1998a, 1998b, 1999), and, of course, Segregation model simulations that were described in the following).1. The Sugarscape modelThe Sugarscape (SSC) model consists of an environment, a landscape, with a particular distribution of various resources (e.g. sugar) that the agents need for their survival. Some regions are rich in sugar, some poor. Agents have certain traits like vision (how far they can see), or metabolism (how fast they consume resources). Agents can sense their surrounding (local) environment in order to decide in which direction to move, and they can eat

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