Overview
First comprehensive guidebook for the parameterisation and characterisation of empirical agent-based modelling
This book distinguishes specific modelling situations and provides for each situation a clear sequence of steps
Includes contributions from such well-known academics and ABM experts as Andreas Ernst, Marco Janssen, and Armando Geller
Includes supplementary material: sn.pub/extras
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Table of contents (13 chapters)
Keywords
About this book
This instructional book showcases techniques to parameterise human agents in empirical agent-based models (ABM). In doing so, it provides a timely overview of key ABM methodologies and the most innovative approaches through a variety of empirical applications. It features cutting-edge research from leading academics and practitioners, and will provide a guide for characterising and parameterising human agents in empirical ABM. In order to facilitate learning, this text shares the valuable experiences of other modellers in particular modelling situations. Very little has been published in the area of empirical ABM, and this contributed volume will appeal to graduate-level students and researchers studying simulation modeling in economics, sociology, ecology, and trans-disciplinary studies, such as topics related to sustainability. In a similar vein to the instruction found in a cookbook, this text provides the empirical modeller with a set of 'recipes' ready to be implemented.
Agent-based modeling (ABM) is a powerful, simulation-modeling technique that has seen a dramatic increase in real-world applications in recent years. In ABM, a system is modeled as a collection of autonomous decision-making entities called “agents.” Each agent individually assesses its situation and makes decisions on the basis of a set of rules. Agents may execute various behaviors appropriate for the system they represent—for example, producing, consuming, or selling. ABM is increasingly used for simulating real-world systems, such as natural resource use, transportation, public health, and conflict. Decision makers increasingly demand support that covers a multitude of indicators that can be effectively addressed using ABM. This is especially the case in situations where human behavior is identified as a critical element. As a result, ABM will only continue its rapid growth.
This is the first volume in a series of books that aims to contribute to a cultural change in the community of empirical agent-based modelling. This series will bring together representational experiences and solutions in empirical agent-based modelling. Creating a platform to exchange such experiences allows comparison of solutions and facilitates learning in the empirical agent-based modelling community. Ultimately, the community requires such exchange and learning to test approaches and, thereby, to develop a robust set of techniques within the domain of empirical agent-based modelling. Based on robust and defendable methods, agent-based modelling will become a critical tool for research agencies, decision making and decision supporting agencies, and funding agencies. This series will contribute to more robust and defendable empirical agent-based modelling.
Reviews
From the reviews:
“This book addresses this challenge by providing a comprehensive framework that helps modellers in the characterisation and parametrisation of agents … . a nice handbook both for newcomers and expert modellers. … it is a progress towards a greater standardisation of the procedures adopted in the model design and parametrisation.” (Giangiacomo Bravo, Journal of Artificial Societies and Social Simulation, Vol. 17 (2), 2014)
Editors and Affiliations
About the editors
Alexander Smajgl works at CSIRO Ecosystem Sciences, Australia. Olivier Barreteau is senior water scientist with Cemagref Montpellier.
Bibliographic Information
Book Title: Empirical Agent-Based Modelling - Challenges and Solutions
Book Subtitle: Volume 1, The Characterisation and Parameterisation of Empirical Agent-Based Models
Editors: Alexander Smajgl, Olivier Barreteau
DOI: https://doi.org/10.1007/978-1-4614-6134-0
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media New York 2014
Hardcover ISBN: 978-1-4614-6133-3Published: 13 September 2013
Softcover ISBN: 978-1-4939-5252-6Published: 23 August 2016
eBook ISBN: 978-1-4614-6134-0Published: 12 September 2013
Edition Number: 1
Number of Pages: XIII, 249
Number of Illustrations: 10 b/w illustrations, 20 illustrations in colour
Topics: Statistical Theory and Methods, Simulation and Modeling, Statistics, general