By Uri Wilensky
The introduction of frequent quick computing has enabled us to paintings on extra advanced difficulties and to construct and learn extra advanced versions. This ebook presents an advent to at least one of the first methodologies for study during this new box of wisdom. Agent-based modeling (ABM) bargains a brand new manner of doing technological know-how: by means of accomplishing computer-based experiments. ABM is appropriate to complicated structures embedded in typical, social, and engineered contexts, throughout domain names that diversity from engineering to ecology. An creation to Agent-Based Modeling deals a accomplished description of the middle innovations, equipment, and purposes of ABM. Its hands-on process -- with 1000's of examples and routines utilizing NetLogo -- permits readers to start developing types instantly, despite adventure or discipline.
The ebook first describes the character and reason of agent-based modeling, then provides the technique for designing and construction ABMs, and at last discusses how one can make the most of ABMs to respond to advanced questions. beneficial properties in every one bankruptcy comprise step by step courses to constructing versions usually textual content; textual content containers with additional info and ideas; end-of-chapter explorations; and references and lists of proper studying. there's additionally an accompanying site with the entire types and code.
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Additional resources for An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo
Results and Observations from the Ant Model When running the Ants model, the results are initially surprising for someone who has only seen the rules for the individual ants. The “aggregate” or “macro” behavior of the model shows apparently systematic food gathering behavior. It is as if the ant colony has a clear plan for how to gather the food. Yet, we have seen that the Ants model rules do not contain any systematic foraging plan. If we look closely at the model running, we observe that initially the ants wander around at random.
If your model includes all aspects of the real phenomenon, it is more efficient to simply observe reality, since it saves you the time of building the model. The function of a model is to help us to understand and examine phenomena that exist in the real world in more tractable and efficient ways than by simply observing reality. Even if you have never observed a real ant colony, the Ants model helps—it can help you know what to look for and generate hypotheses that you can confirm or disconfirm by observation.
Agent-based modeling is a computational methodology that enables one to model complex systems. ). Agents usually also have a graphical component so you can see them on the computer screen. An agent can represent any element of a system. A gas molecule agent, for instance, might have properties such as “mass” with value 30 atomic mass units, “speed” with value 10 meters per second, and “heading” with a value of the angle it is facing. , and fleece with a value of “full” (a discrete-textual rather than numerical value).
An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo by Uri Wilensky