Edited by several of the leading figures in the field, this is the first book to provide a state-of-the-art, accessibly written methodological introduction to the tools and techniques of agent-based modelling. Using these building blocks, readers will learn how to design, simulate, and validate agent-based models in economics.
In contrast to mainstream economics, complexity theory conceives the economy as a complex system of heterogeneous interacting agents characterised by limited information and bounded rationality. Agent Based Models (ABMs) are the analytical and computational tools developed by the proponents of this emerging methodology. Aimed at students and scholars of contemporary economics, this book includes a comprehensive toolkit for agent-based computational economics, now quickly becoming the new way to study evolving economic systems. Leading scholars in the field explain how ABMs can be applied fruitfully to many real-world economic examples and represent a great advancement over mainstream approaches. The essays discuss the methodological bases of agent-based approaches and demonstrate step-by-step how to build, simulate and analyse ABMs and how to validate their outputs empirically using the data. They also present a wide set of applications of these models to key economic topics, including the business cycle, labour markets, and economic growth.
Advance praise: 'The authors conceive of economies as complex systems of heterogeneous interacting agents with bounded rationality and limited information, and they view agent-based modeling as a necessary tool for the exploration of such systems. In this book the authors provide a comprehensive introduction to agent-based modeling. Although macroeconomic applications are stressed, the coverage of topics such as rationality, behavior, expectations, and learning will be of value for many other applications as well. A particularly welcome aspect of the book is its attention to historical antecedents and its inclusion of chapters devoted to empirical validation and estimation issues.' Leigh Tesfatsion, Iowa State University
