Regulating Under Uncertainty About Rationality: From Decision Theory to Machine Learning and Complexity Theory

Forthcoming, Stefan Grundmann and Philipp Hacker (eds.), Theories of Choice. The Social Science and the Law of Decision Making (Oxford University Press, 2020)

23 Pages Posted: 12 Jul 2019 Last revised: 11 Sep 2019

Date Written: July 11, 2019

Abstract

Theories of choice, and their legal consequences, dramatically differ based on whether they are premised on rational or boundedly rational actors. This chapter describes the interactions between, and the regulatory implications of, three types of uncertainties that the selection of an adequate theory of choice (a more behavioral or a more rational one) is exposed to. First, I suggest that Knightian uncertainty obtains in many regulatory areas concerning the distribution of degrees of rationality between regulatees. In recent years, this has been described as the “knowledge problem” of behavioral law and economics. This chapter argues that the best regulatory response to the knowledge problem is to frame regulation as a problem of decision making under uncertainty. Second, with the rise of machine learning, it is arguably becoming ever more possible to estimate the level of bias, or even entire rationality quotients, of individual regulatees. This may convert uncertainty into risk and opens the potential for, but also the pitfalls of, personalized law. Third, however, even Big Data analytics only offers a snapshot of a distribution of rationality at one moment in time. As recent economic analyses have suggested, however, behavioral heterogeneity can evolve over time in unpredicted ways that may lead to unforeseen consequences, leading to economic complexity. Arguably, this calls for a greater role of standards, as opposed to rules, in regulating environments with dynamic behavioral heterogeneity. Examples discussed in the three types of uncertainty include first-degree price discrimination in the digital economy; usurious lending; and blockchain environments. Across all these examples, the chapter focuses on the normative implications of different types of uncertainty. In the end, theories of choice can help us make normative trade-offs more transparent, but they cannot replace the value judgments, and normative discourses, that guide the balancing of the involved interests.

Keywords: price discrimination, decision theory, regulation, usury, lending, blockchain, smart contracts

JEL Classification: K12, K20

Suggested Citation

Hacker, Philipp, Regulating Under Uncertainty About Rationality: From Decision Theory to Machine Learning and Complexity Theory (July 11, 2019). Forthcoming, Stefan Grundmann and Philipp Hacker (eds.), Theories of Choice. The Social Science and the Law of Decision Making (Oxford University Press, 2020). Available at SSRN: https://ssrn.com/abstract=3418242

Philipp Hacker (Contact Author)

Humboldt University of Berlin ( email )

Unter den Linden 6
Berlin, Berlin 10099
Germany
+49 30 2093 3498 (Phone)

HOME PAGE: http://hu-berlin.academia.edu/PhilippHacker

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