By Uri Wilensky
The creation of frequent quick computing has enabled us to paintings on extra advanced difficulties and to construct and study extra advanced versions. This booklet presents an advent to at least one of the first methodologies for study during this new box of wisdom. Agent-based modeling (ABM) deals a brand new manner of doing technological know-how: through carrying out computer-based experiments. ABM is appropriate to complicated platforms embedded in traditional, social, and engineered contexts, throughout domain names that diversity from engineering to ecology. An advent to Agent-Based Modeling bargains a accomplished description of the middle options, tools, and functions of ABM. Its hands-on strategy -- with countless numbers of examples and workouts utilizing NetLogo -- permits readers to start developing types instantly, despite adventure or discipline.
The booklet first describes the character and reason of agent-based modeling, then offers the method for designing and development ABMs, and eventually discusses how you can make the most of ABMs to reply to advanced questions. positive aspects in each one bankruptcy contain step by step courses to constructing types primarily textual content; textual content containers with additional info and ideas; end-of-chapter explorations; and references and lists of suitable analyzing. there's additionally an accompanying site with the entire types and code.
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Extra info for An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo
There are many advantages to agent-based representations. First, it does not require knowing calculus. Requiring calculus to be able to reason about predator-prey interactions sets the entry threshold quite high. In the United States, only a small percentage of adults have ever taken calculus, and far fewer are familiar with differential equations. So calculus serves as a gatekeeper limiting access to important content to a small minority of adults and to almost all children. Yet the content of the predator-prey model can be made quite accessible to a non-calculus audience through ABM.
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If we look closely at the model running, we observe that initially the ants wander around at random. Then some ants will wander into a nearby food source. Once they find that food source, they will start to bring food back to the nest, laying a pheromone trail beneath them. If just one lone ant finds the food source, the pheromone trail will not be strong enough for other ants to follow it; but as more and more ants find the food source, the trail will become stronger and stronger. Eventually, the actions of many ants will create a strong pheromone trail from the nest to the food source, and so any ant can easily find the trail to the food source.
An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo by Uri Wilensky