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Agent-Based Models in Economics

Editor

Listed:
  • Delli Gatti,Domenico
  • Fagiolo,Giorgio
  • Gallegati,Mauro
  • Richiardi,Matteo
  • Russo,Alberto

Abstract

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.

Suggested Citation

  • Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108414999, January.
    • Handle: RePEc:cup:cbooks:9781108414999
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    Citations

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    Cited by:

    1. Neugart, Michael & Zaharieva, Anna, 2018. "Social Networks, Promotions, and the Glass-Ceiling Effect," Center for Mathematical Economics Working Papers 601, Center for Mathematical Economics, Bielefeld University.
    2. Chen, Siyan & Desiderio, Saul, 2020. "Job duration and inequality," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 14, pages 1-27.
    3. Cathal O'Donoghue & Gijs Dekkers, 2018. "Increasing the Impact of Dynamic Microsimulation Modelling," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 61-96.
    4. Damdinsuren, Erdenebulgan & Zaharieva, Anna, 2023. "Expectation formation and learning in the labour market with on-the-job search and Nash bargaining," Labour Economics, Elsevier, vol. 81(C).
    5. Caner Ates & Dietmar Maringer, 2021. "A Parsimonious Macroeconomic ABM for Labor Market Regulations," LEM Papers Series 2021/46, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    6. Rolf Aaberge & Ugo Colombino, 2018. "Structural Labour Supply Models and Microsimulation," International Journal of Microsimulation, International Microsimulation Association, vol. 11(1), pages 162-197.
    7. Siyan Chen & Saul Desiderio, 2022. "A Regression-Based Calibration Method for Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 59(2), pages 687-700, February.
    8. Noemi Schmitt & Ivonne Schwartz & Frank Westerhoff, 2022. "Heterogeneous speculators and stock market dynamics: a simple agent-based computational model," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1263-1282, October.
    9. Terranova, Roberta & Turco, Enrico M., 2022. "Concentration, stagnation and inequality: An agent-based approach," Journal of Economic Behavior & Organization, Elsevier, vol. 193(C), pages 569-595.
    10. Riccardo Pariboni & Pasquale Tridico, 2020. "Structural change, institutions and the dynamics of labor productivity in Europe," Journal of Evolutionary Economics, Springer, vol. 30(5), pages 1275-1300, November.
    11. Filippo Gusella & Anna Maria Variato, 2021. "Financial Instability and Income Inequality: why the connection Minsky-Piketty matters for Macroeconomics," Working Papers - Economics wp2021_15.rdf, Universita' degli Studi di Firenze, Dipartimento di Scienze per l'Economia e l'Impresa.
    12. Michael Paul Kramer & Linda Bitsch & Jon Hanf, 2021. "Blockchain and Its Impacts on Agri-Food Supply Chain Network Management," Sustainability, MDPI, vol. 13(4), pages 1-22, February.

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