Agent-Based Modelling in Economics

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Agent-based modelling in economics

Lynne Hamill and Nigel Gilbert, Centre for Research in Social Simulation (CRESS), University of Surrey, UK

New methods of economic modelling have been sought as a result of the global economic downturn in 2008.This unique book highlights the benefits of an agent-based modelling (ABM) approach. It demonstrates how ABM can easily handle complexity: heterogeneous people, households and firms interacting dynamically. Unlike traditional methods, ABM does not require people or firms to optimise or economic systems to reach equilibrium. ABM offers a way to link micro foundations directly to the macro situation.

Key features:

  • Introduces the concept of agent-based modelling and shows how it differs from existing approaches.
  • Provides a theoretical and methodological rationale for using ABM in economics, along with practical advice on how to design and create the models.
  • Each chapter starts with a short summary of the relevant economic theory and then shows how to apply ABM.
  • Explores both topics covered in basic economics textbooks and current important policy themes; unemployment, exchange rates, banking and environmental issues.
  • Describes the models in pseudocode, enabling the reader to develop programs in their chosen language.
  • Supported by a website featuring the NetLogo models described in the book.

Agent-based Modelling in Economics provides students and researchers with the skills to design, implement, and analyze agent-based models. Third year undergraduate, master and doctoral students, faculty and professional economists will find this book an invaluable resource.

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About the author

Nigel Gilbert, Professor of Sociology and Director of CRESS, University of Surrey, UK.

Lynne Hamill, Centre of Research in Social Simulation (CRESS), University of Surrey, UK.

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Additional Information

Publisher
John Wiley & Sons
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Published on
Nov 2, 2015
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Pages
256
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ISBN
9781118945506
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Language
English
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Genres
Mathematics / Probability & Statistics / Stochastic Processes
Social Science / Statistics
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Content protection
This content is DRM protected.
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Read aloud
Available on Android devices
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