The Market as a Learning Algorithm: Consequences for Regulation and Antitrust

61 Pages Posted: 8 Sep 2020 Last revised: 15 Sep 2020

See all articles by Ramsi Woodcock

Ramsi Woodcock

University of Kentucky College of Law

Date Written: July 27, 2020

Abstract

The heart of the Chicago School’s attack on the antitrust laws was a skepticism about the ability of government to improve upon unregulated market outcomes. Although the attack failed to eliminate regulation or antitrust entirely, it has proven so enduringly devastating as an intellectual matter that virtually no proposal for government regulation or increased antitrust enforcement is put forward today without an attempt either to justify the proposed departure from an assumed-legitimate free market baseline or to dismiss Chicago School skepticism as an intellectual plot bankrolled by business elites. Chicago School skepticism has been so devastating because it draws sustenance from an inapt metaphor for the economy: that of evolution through natural selection. The free market is, for the Chicago School, nature itself, and all the glories of life suggest that evolution does just fine when left to its own devices, creating a powerful basis for skepticism regarding the need for government intervention in the economy. Except that evolution never did do anything to promote economic growth, so much as theft, a fact that human beings know well given their status as predators of unparalleled success. Humanity did not escape from the subsistence economics that characterizes all of evolved life until humanity started to exert control over the forces of evolution, which is to say: to regulate.

A better metaphor for the economy than natural selection is that of a computer running a machine learning algorithm engineered to channel evolutionary forces away from theft and toward growth. The first such algorithm embraced by humanity set evolution aside almost entirely, in favor of identifying optimal productive behaviors directly. That was central planning, which flourished throughout the ancient world and was practiced globally right up to the 19th century. The second such algorithm embraced evolution, but sought to improve upon it by imposing a rule against theft. That was the economic liberalism practiced in the West in the late 19th century. It is also the regime favored by the Chicago School. Approached from the metaphor of the algorithm, the Chicago School’s program appears retrograde, rather than foundational, because it amounts to the position that there should be no version 2.0, no further tweaks to the algorithm. But the antitrust laws, in prohibiting behavior that degrades competitors’ products, even when the behavior does not amount to theft, improves upon the algorithm that is economic liberalism, better channeling life’s evolutionary forces toward productivity and growth, rather than destructive forms of competition.

Keywords: Antitrust, Regulation, Chicago School, Machine Learning, Evolution, Central Planning, General Equilibrium, Liberalism

JEL Classification: N00, P00, K21, O00, B52

Suggested Citation

Woodcock, Ramsi, The Market as a Learning Algorithm: Consequences for Regulation and Antitrust (July 27, 2020). Available at SSRN: https://ssrn.com/abstract=3661971 or http://dx.doi.org/10.2139/ssrn.3661971

Ramsi Woodcock (Contact Author)

University of Kentucky College of Law ( email )

620 S. Limestone Street
Lexington, KY 40506-0048
United States

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