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Opis

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.

Szczegóły książki

Kategoria Książki po angielsku Economics, finance, business & management Economics Economic theory & philosophy

• Pełny tytuł: Agent-Based Modelling in Economics
• Autor: Lynne Hamill, Nigel Gilbert
• Język: Angielski
• Oprawa: Twarda
• Liczba stron: 256
• EAN: 9781118456071
• ISBN: 1118456076
• ID: 09268757
• Wydawca: John Wiley & Sons Inc
• Waga: 486 g
• Wymiary: 238 × 157 × 17 mm
• Data wydania: 12. January 2016

Ulubione w innej kategorii

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