Editorial Reviews. Review. This book eloquently captures the excitement of understanding natural and social phenomena by recreating them in computer. terney.info: Agent-Based and Individual-Based Modeling: A Practical Introduction (): Steven F. Railsback, Volker Grimm: Books. I recommend the following if you are looking to build agent-based models (as opposed to simply reading about them): * Complex Adaptive.
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This book eloquently captures the excitement of understanding natural and social phenomena by recreating them in computer simulations. The agent-based. The following are some relevant books about ABM. The first three have examples in NetLogo. Gilbert, N. & Troitsch, K.G. () Simulation for the Social. Read 11 answers by scientists with 16 recommendations from their colleagues to the question asked by Mahmoud M. Abdelrahman on Mar 20,
Steven F. Railsback is adjunct professor of mathematics at Humboldt State University and a consulting environmental scientist.
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Table of Contents. Chapter 1 [PDF]. If we need to include collectives in a model, then how do we do it? One way is to represent collectives as a completely emergent characteristic of the agents: Now that we have introduced you to agent-based modeling, NetLogo, and important concepts for designing ABMs, part III will focus on a more strategic level.
Now, in part III, we leave the engine room and head for the bridge to work on getting somewhere with our vessel: The first very important decision in designing a model is selecting the set of entities and their state variables and attributes that represent the system. We could think of endless things that are in any system we want to model, and infinite variables that characterize each.
But in models, we usually—wisely—include only a very few kinds of entities and only a few variables size, location, etc.
Once we have chosen the entities and state variables, we also know what processes need to be in the model: In the previous chapter we focused on model structure. Now we will turn to processes and how to model them. For example, we often just assume that agents make decisions randomly.
However, after we have a first implementation of the entire model, we need to unsimplify and come up Parameters are the constants in the equations and algorithms we use to represent the processes in an ABM. In our first model, which described butterfly hilltopping behavior and virtual corridors, the parameter q represented the probability that a butterfly would deliberately move uphill at a given time step section 3.
Testing and analyzing are, as you certainly have learned by now, integral parts of agent-based modeling and of modeling in general. By testing we refer to checking whether a model, or a certain submodel, is correctly implemented and does what it is supposed to do. Throughout this book we have been testing both models and their software.
Analyzing models refers to something different: Imagine that you just installed new software for manipulating digital photos. However, the new software might use terminology and a philosophy that you are not familiar with. What would you do? You would try it out! You would try one command or tool at a time and see how your photo changes.
Once you got an idea how basic tools work, you would start using them in combination. You would base your attempts on your current, preliminary understanding: This book is an introduction to agent-based modeling. From it, you should have learned the basic principles of modeling in general and agent-based modeling in particular. You should also now be familiar with one specific platform for agent-based modeling, NetLogo.
If you took your time, worked through the exercises, and discussed your questions with colleagues or instructors, or in a user forum, you are ready to do science using agent-based models. After finishing this course, you are on your own and have to decide Have library access? Log in through your library. About Support. Log in Register. Agent-Based and Individual-Based Modeling: A Practical Introduction Steven F. Copyright Date: October Published by: Princeton University Press.
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Complex systems modelling[ edit ] Mathematical models of complex systems are of three types: black-box phenomenological , white-box mechanistic, based on the first principles and grey-box mixtures of phenomenological and mechanistic models. Mathematical models for complex systems Black-box models are completely nonmechanistic. They are phenomenological and ignore a composition and internal structure of a complex system. We cannot investigate interactions of subsystems of such a non-transparent model.
All events at micro-, meso- and macro-levels of a dynamic system are directly visible at all stages of its white-box model evolution.
In most cases mathematical modelers use the heavy black-box mathematical methods, which cannot produce mechanistic models of complex dynamic systems. Grey-box models are intermediate and combine black-box and white-box approaches.
Logical deterministic individual-based cellular automata model of single species population growth Creation of a white-box model of complex system is associated with the problem of the necessity of an a priori basic knowledge of the modeling subject. The deterministic logical cellular automata are necessary but not sufficient condition of a white-box model.
The second necessary prerequisite of a white-box model is the presence of the physical ontology of the object under study.