Addressing the malaise in neoclassical economics: a call for partial models

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Abstract

Economics is currently experiencing a climate of uncertainty regarding the soundness of its theoretical framework and even its status as a science. Much of the criticism is within the discipline, and emphasizes the alleged failure of the neoclassical viewpoint. This article proposes the deployment of partial modeling, utilizing Boolean networks (BNs), as an inductive discovery procedure for the development of economic theory. The method is presented in detail and then linked to the Semantic View of Theories (SVT), closely identified with Bas van Fraassen and Patrick Suppes, in which models are construed as mediators creatively negotiating between theory and reality. It is suggested that this approach may be appropriate for economics and, by implication, for any science in which there is no consensus theory, and a wide range of viewpoints compete for acceptance.

Posted for comments on 21 Feb 2018, 12:09 pm.

Comments (4)

  • Herbert says:

    Ron, if you are still interested in this topic, look what I found about macroeconomics, author Sharov
    http://jedsnet.com/vol-6-no-3-september-2018-current-issue-jeds

  • Ron Wallace says:

    I appreciate Herbert’s suggestion that I take a look at Alexander Shavrov’s article in the Journal of Economics and Development Studies. The article emphasizes a systems-theory approach, but is confusingly written, and could have benefited from a strong editorial hand. On the positive side, it is possible that Shavrov’s model, emphasizing labor, activities, and resources, may indeed be valuable. This example thus serves to reinforce my view that a computational approach which synthesizes competing models is much needed in economics during this time of scientific uncertainty.

  • Prof Steve Keen says:

    I’m interested in this methodology to compare models/theories, but I’d like more detail on how it would work on an economic example, and particularly in the field of macroeconomics rather than finance. For instance, could it evaluate the Smets and Wouters 2007 DSGE model of the US economy (Smets, F. and R. Wouters (2007). “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97(3): 586-606) against my stylized model of Minsky’s Financial Instability Hypothesis? I can’t post either model here, but the former is available at http://dept.ku.edu/~empirics/Emp-Coffee/smets-wauters_aer07.pdf while the latter is detailed on slides 37-40 of https://www.patreon.com/file?h=24555362&i=3266457.

    The issues I’d like more information on are (1) whether the methodology works with aggregative models rather than agent-interaction ones; (2) how does it handle comparing two models where one uses discrete time (S&M here) and the other is in continuous time (my model).

    In terms of the impact of this comparison methodology itself, it would never shake the Neoclassical school, since they’re driven by internal consistency far more than they are by empirical relevance (even after 2008), but it might help to validate non-Neoclassical approaches to the growing cohort of new students who are critical of the mainstream.

  • Ron Wallace says:

    I would like to thank Steve Keen for his queries regarding partial modeling, and his comment regarding Neoclassical Economics.

    Regarding the two queries, I should probably first emphasize that my approach to economic modeling has been strongly influenced by systems theory in general, and by molecular biology in particular. It would be fair to describe my approach as bio-inspired. Viewed from that standpoint, the persisting problem of aggregation in macroeconomics strikes me as similar to the problems of trial models in Boolean network methodology (the method described in the article). When drafting a tentative BN model (or “wiring diagram”), it is common practice to combine components (nodes) based on available information or, sometimes—frankly—on guesswork. The model is then run and tested against empirical data to assess its predictive power. The combined nodes (“aggregates”) may be empirically justified, or it may be necessary to combine them into smaller (i.e., less encompassing) nodes. The important question that Professor Keen has posed thus translates, in my view, to a creative aspect of modeling strategy. On the issue of discrete versus continuous models—an issue which, at the moment, is being vigorously debated in computational molecular biology—it is essential to concede at the outset that BNs are comprised of discrete time and state components; thus, in their basic form they cannot represent continuous processes in the manner of ordinary differential equations (ODEs). However, the type of study that Professor Keen suggests should be possible through the use of hybrid models, for which toolkits are available, that convert selected BN nodes into ODEs.

    Addressing Keen’s final comment, I am of course uncertain, as is everyone else, regarding the fate of Neoclassical Economics. Perhaps Keen is right: proponents of the mainstream theory will remain impervious to critique. But I remain hopeful that the current heterodox turmoil will give rise to model-based strategies, and to a new body of theory more relevant to economic life.

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