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Agent-Based and Individual-Based Modeling

A Practical Introduction, Second Edition

Volker Grimm, Steven F. Railsback

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Princeton University Press img Link Publisher

Naturwissenschaften, Medizin, Informatik, Technik / ÷kologie

Beschreibung

The essential textbook on agent-based modeling—now fully updated and expanded

Agent-Based and Individual-Based Modeling has become the standard textbook on the subject for classroom use and self-instruction. Drawing on the latest version of NetLogo and fully updated with new examples, exercises, and an enhanced text for easier comprehension, this is the essential resource for anyone seeking to understand how the dynamics of biological, social, and other complex systems arise from the characteristics of the agents that make up these systems.

Steven Railsback and Volker Grimm lead students stepwise through the processes of designing, programming, documenting, and doing scientific research with agent-based models, focusing on the adaptive behaviors that make these models necessary. They cover the fundamentals of modeling and model analysis, introduce key modeling concepts, and demonstrate how to implement them using NetLogo. They also address pattern-oriented modeling, an invaluable strategy for modeling real-world problems and developing theory.

This accessible and authoritative book focuses on modeling as a tool for understanding real complex systems. It explains how to pose a specific question, use observations from actual systems to design models, write and test software, and more.

  • A hands-on introduction that guides students from conceptual design to computer implementation to analysis
  • Filled with new examples and exercises and compatible with the latest version of NetLogo
  • Ideal for students and researchers across the natural and social sciences
  • Written by two leading practitioners
  • Supported by extensive instructional materials at www.railsback-grimm-abm-book.com

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Schlagwörter

Disperser, Software development, Satisficing, State variable, Parameter (computer programming), Verification and validation, Management accounting, Statistical model, Statistical significance, Debugging, Stochastic process, Logistic function, Explanation, Scientific modelling, Version control, Ranking (information retrieval), Emergence, Analysis of variance, Simulation modeling, Network model, Probability, Search algorithm, Decision theory, Lookup table, Spatial analysis, Scale parameter, Computational model, Replication (statistics), Parameter, Influence diagram, Sensor, Level of detail, Simulation, Sensitivity analysis, Initialization (programming), Conceptual model, Stochastic Modeling, Collective, Curve fitting, Initial condition, Binomial distribution, Loss function, Multi-agent system, Standard deviation, Fractal dimension, Behavior model, Decision-making, Programming style, Path dependence, Variable (computer science), Likelihood function, Calculation, Logistic regression, Coefficient of determination, Software versioning, Expected value, Software testing, Environment variable, Investment fund, Model selection, Local variable, Source code, Debugger, Poisson point process, Adviser, Causality, Poisson distribution, Variable (mathematics), Estimation, Spreadsheet, Autocorrelation, Parameter space, Theoretical Value, NetLogo, Copying, Uncertainty analysis, Algorithm, Calibration, Programming model, Conditional (computer programming), Prediction, Test theory, Stylized fact, Dynamic programming, Brainstorming, Statistic, Collective behavior, Utility, Theory, Data set, Diagram, Reproducibility, Agent-based model, Observation, Result, Stochastic, Decision analysis, Heuristic, Determinism, Code review