By Uri Wilensky
The creation of common quickly computing has enabled us to paintings on extra advanced difficulties and to construct and research extra complicated versions. This publication presents an creation to at least one of the first methodologies for study during this new box of wisdom. Agent-based modeling (ABM) bargains a brand new approach of doing technological know-how: by way of engaging in computer-based experiments. ABM is acceptable to complicated platforms embedded in usual, social, and engineered contexts, throughout domain names that diversity from engineering to ecology. An creation to Agent-Based Modeling bargains a accomplished description of the middle options, equipment, and functions of ABM. Its hands-on procedure -- with 1000's of examples and workouts utilizing NetLogo -- permits readers to start developing types instantly, despite event or discipline.
The e-book first describes the character and reason of agent-based modeling, then provides the method for designing and construction ABMs, and eventually discusses how you can make the most of ABMs to reply to advanced questions. positive factors in each one bankruptcy comprise step by step courses to constructing versions ordinarily textual content; textual content containers with more information and ideas; end-of-chapter explorations; and references and lists of suitable analyzing. there's additionally an accompanying web site with the entire versions and code.
Read or Download An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo PDF
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Extra resources for An Introduction to Agent-Based Modeling: Modeling Natural, Social, and Engineered Complex Systems with NetLogo
However, though ants do have some methods of communicating information directly (Hölldobler & Wilson, 1998) most species do not communicate the exact route to a food source in this way. In fact, scientists rarely observe any difference in behavior between an ant returning to the nest with food and an ant returning without food. Most ants returning without food simply leave the nest again and recommence their search for food; the ants act almost exactly like they had before they found food. From this it is possible to reason that there must be some communication method between the ants or the colony could not efficiently gather food.
Moreover, when the equation is solvable, it enables the direct calculation of results without the need to run a model. When a model requires a large numbers of agents, its execution time can be so long as to be impractical as a way of calculating results. The corresponding equation-based models must often make many simplifying assumptions to gain this increase in speed. These simplifications are most justifiable when the agents are sufficiently homogenous that it can be advantageous to treat them as average quantities as opposed to the heterogeneous individuals often used in ABM.
This optimal exploitation of food sources could be placed within a larger context. In many ways, the colony of ants seems to balance exploration and exploitation (Dubins & Savage, 1976. In any situation in which an entity is operating in an unknown environment, the entity must spend some time exploring the environment to understand how its actions affect its rewards, and some time exploiting the environment, that is taking actions that it knows have produced the best rewards in the past. By allocating a large number of ants to exploit the current nearest food source while other ants continue to explore, the ant colony as a whole successfully balances exploration and exploitation.