Abstract
Macroeconomic modelling has been under intense scrutiny since the Great Financial Crisis, when serious shortcomings were exposed in the methodology used to understand the economy as a whole. Criticism has been levelled at the assumptions employed in the dominant models, particularly that economic agents are homogenous and optimising and that the economy is equilibrating. In a related paper (Haldane and Turrell Oxford Rev Econ Polic 34(1–2):219–251 2018), we argue that an interdisciplinary approach to modelling in macroeconomics is beneficial. Here we focus on what one such approach - agent-based modelling, which has been extensively used across a wide range of disciplines - could do for macroeconomics. Agent-based models are complementary to existing approaches to macroeconomics and are particularly well-suited to answering questions where complexity, heterogeneity, networks, and heuristics play an important role.
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Notes
Every agent-based model is computable by a Turing machine, and every algorithm computable by a Turing machine may be expressed via sets of partial recursive functions.
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Acknowledgements
We are grateful to attendees of a seminar at the University of Oxford, to an anonymous referee, and to John Barrdear, James Barker, David Bholat, Shiv Chowla, Giovanni Dosi, Jeremy Franklin, Simon Hayes, Sujit Kapadia, Francesca Monti, Mauro Napoletano, Paul Robinson and Andrea Roventini for their help and comments.
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The views expressed in this paper are solely those of the authors, and do not necessarily represent those of the Bank of England or its policy committees and should not be reported as such. This paper was initially prepared for a special edition of The Oxford Review of Economic Policy. A modified version is reproduced here with an additional section which examines the barriers to the more widespread adoption of macroeconomic agent-based models.
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Haldane, A.G., Turrell, A.E. Drawing on different disciplines: macroeconomic agent-based models. J Evol Econ 29, 39–66 (2019). https://doi.org/10.1007/s00191-018-0557-5
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DOI: https://doi.org/10.1007/s00191-018-0557-5