Discussion Paper

No. 2017-59 | September 11, 2017
Addressing the malaise in neoclassical economics: a call for partial models


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.

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Cite As

Ron Wallace (2017). Addressing the malaise in neoclassical economics: a call for partial models. Economics Discussion Papers, No 2017-59, Kiel Institute for the World Economy. http://www.economics-ejournal.org/economics/discussionpapers/2017-59

Comments and Questions

Wolfram Elsner - Invited Reader Comment
October 11, 2017 - 09:29

Assessment of Ron Wallace’s paper “Addressing the malaise …” Wolfram Elsner, University of Bremen, Germany

• The paper is relevant, topical and appealing, with its starting point and motive to deal with the “malaise in neoclassical economics”.
• The ...[more]

... paper is also well written and easy to understand (at the verbal level). (On formalisms below.)
• The paper is short as well, so some space left for some amendment through particular extensions (as suggested below).
• The “message” of the paper is that a certain approach, which did have some success in cell biology, can be transferred and applied in economics as well: A “partial modeling” using Boolean Networks (BN) analysis, which are somewhat reduced forms of more complex cardinally based (non-discrete/continuous; and usually agent-based) models often used in model simulation.
• BN analysis would then ideally provide some progress in discriminating among different theories and more complex models by better bridging or mediating verbal theory, modeling, and reality.
• The ideal thus would be some inductive “proof” of a theory or discrimination among theories.
• The approach therefore appears as some heuristic for scientific progress in economics.
• BN have been developed and applied with considerable success in gene and cell biology, biophysics, and biochemistry modeling. Stuart Kauffman’s work is a major instance here, which has spread indeed from genetic biology into all modern complexity sciences and their SNAs.

• But there is doubt, whether it was directly applied to test among rival theories even there.

• Even more so, the paper would need to reflect, whether the discriminatory capacity of such approach can be equally applied in a social science like economics, where the mere basis of measurement and statistical-econometric analysis already is much more contested that in physics.
• The author therefore, should address the debate about the inductive ideal of “falsification” in general epistemology, but also in individual sciences as economics. (I am not suggesting, that the author naively adheres to some outdated falsification ideal.)
• A broader epistemological issue which appears the paper has to deal with to justify its message, is the approach to scientific paradigms (in the traditions of Kuhn and Lakatos). If a contested discipline such as economics consists of competing paradigms, then there is Kuhnian incommensurability (and even relative losses) among paradigmatic theories and schools (one of them may explain something that the other one cannot equally well, and v.v.). Against this background, it seems even less feasible to generate fine-grained model comparison and discrimination.
• For instance, would adherents of (relatively simpler) models with predetermined equilibrium (paths), assumptions of high (perhaps perfect global) rationality, and clear optimality/welfare benchmarks really be convinced by partial modeling proofs of the existence of a “better” explanation (of what?), a more real-world explanation, a more complex explanation?

• Finally, the reader would be able to assess the value added of the paper much better, if it got somewhat more formal, by illustrating a case more formal. This should be done basically in section 2 or in the application case of HFT in section 3 (which I assume starts on p.9). (Rather than becoming more specific and formal, the paper seems to undergo some repetition and meandering on pp 9-11.)

• The conclusion appears rather short and lean, which often is most enjoyable to the reader, in this case, however, appears as somewhat of an inhibit of some broader final considerations.

In all, I have learned a lot by the paper that was really inspiring and informative re. the possible transfer of biophysical methods into economics. But the paper needs to be made “round” by addressing the above mentioned epistemological issues and provide some formal example.

Ron Wallace - Reply to reader comments by Wolfram Elsner
October 30, 2017 - 08:07

Ron Wallace: Reply to Wolfram Elsner’s comments on “Addressing the malaise in neoclassical economics: a call for partial models.”

I would like to thank Walfram Elsner for his valuable comments. His critique has posed an important question: how does my proposed ...[more]

... use of partial models in economics relate to falsification and paradigms?
Mary S. Morgan (2005) has proposed that economic experiments, much like the partial models that are used in theory-building, should have semi-autonomy---a creative life of their own---that permits them to yield unanticipated results. Her approach is bio-inspired: the “domesticated” preparations in molecular and cell biology (e.g., the membrane bilayer, bacteria cultures, and yeast cells) are neither fully the natural world nor are they totally artificial. Rather, they are samplings or extracts from nature which permit manipulation under controlled conditions. The approach is typically a discovery procedure in which the activity of the sample, i.e., of the ions, molecules and organelles, despite precise initial constraints, proceeds with its own dynamics, sometimes producing results that are unforeseen. In like manner, economics should expand its vigorous tradition of experimental studies (e.g., decision-making, gaming) which, in a controlled setting, simulate economic activity. Morgan notes that these strategies have historically yielded results inconsistent with dominant theories---for example of market behavior---thereby stimulating critical thought and the crafting of revisionist views. In short, following Morgan, I am suggesting a novel type of dialog between partial modeling and “domesticated” economic experiments. Only when there is convergence---and a pattern of replication---between the two approaches should a theory be applied to real economic behavior.
Touching briefly on the matter of paradigms, I would suggest that the above approach would most closely approximate Lakatos’ (1970) notion of “research program”: a professional network of scientists sharing methods, models and theories. I additionally agree with his view that a research program should be progressive: i.e., a theory should not merely resist falsification, but should display a superior ability---in contrast with competing theories---to explain observations and lead to new ideas. I diverge from Lakatos, however, in his treatment of heuristics. Consistent with the present concept of models and experiments that have ungoverned trajectories and thus may challenge established views, there is simply no such thing as a “hard core” of theories protected by a cordon of “auxiliary hypotheses”. On the contrary, every theory, to a greater or lesser likelihood, is vulnerable to falsification.
Again, I would like to thank Wolfram Elsner for a thoughtful critique.


Lakatos, I., 1970. Falsification and the methodology of scientific research
programmes. In Imré Lakatos and Alan Musgrave (eds.), Criticism and
the growth of knowledge. Cambridge: Cambridge University Press, pp. 91-195.
Morgan, M., 2005. Experiments versus models: new phenomena, inference, and surprise.
J. Econ. Methodol. 12:2, 317-329.

Wolfram Elsner - Ron Wallace on partial modeling
October 31, 2017 - 11:10

I understand Ron's argument well, and it sounds promising for the sciences case. the case of a social science and of a particuarly contested field like economics seems to be more difficult (check, e.g., Jakob Kapeller on "model platonism" in economics, http://www.jakob-kapeller.org/images/pubs/2013-Kapeller-JOIE.pdf.).

I fully agree that there SHOULD not ...[more]

... be such thing as a "hard core". And I would hope that partial modeling indeed becomes a method of addressing and contesting any "hard core" that exists in (mainstream) economics. I myself have argued similarly when using computational experiments to distinguish between different conceptions of certain institutions (for the case study of institutions of trust vs. institutions of social control).

looking forward to read more from Ron on that in the future.

Anonymous - Referee Report 1
November 09, 2017 - 11:56

see attached file

Ron Wallace - Reply to Referee Report 1
November 16, 2017 - 07:52

see attached file