Musings, Ideas and Possible Insights From Behavioral and Complexity Economics And Their Implications for Legal and Regulatory Regimes in Artificial Intelligence/Machine Learning (AI/ML) Digital Space and the Post-COVID World Working Paper Prepared by Dr. Derek Ireland Economic Policy Consultant And Fellow at

Abstract

This working paper explores, discusses, and shares views and perspectives on four major, interrelated, and overlapping themes and challenges for the current and future AI/ML digital economy: (i) the many complexities and uncertainties of the post-COVID recovery period for sub-national, national, multi-country regional and global economies; (ii) with artificial intelligence (AI), machine learning (ML), deep learning (DL), large language models (LLMs), and generative AI in digital space adding yet another layer of complexity, uncertainty and ambiguity, to the global and other economies, with major attention to (iii) the illegal, antisocial, non-compliant and other decision errors of digital firms and more complex regulated entities in digital markets and spaces; and (iv) how the complexities, uncertainties and ambiguities associated with AI/ML and the post-COVID recovery period are expected to result in more frequent, costly and consequential decision errors by governments and their policy making, legal and regulatory authorities as well as by non-state regulators.  The latter includes the compliance and social responsibility divisions of corporations and other more complex regulated entities such as digital platforms and ecosystems.

The major argument of the working paper is that: (a) the manifold two-way interactive effects and feedback loops between these four sources of AI/ML and related digital and non-digital complexity and uncertainty, will (b) make the decision making of AI/ML creators, users, and consumers, other digital market participants, governments and their legal and regulatory authorities, and non-state regulators, and therefore virtually all of us, (c) much more difficult, challenging, uncertain, and complicated, as (d) the global economy and other economies at national and sub-national spatial scales, move through the post-COVID recovery period and attempt to fully recover from the global pandemic and related crises and polycrises of recent years.  The common theme that cuts across and binds together all of the other AI/ML themes throughout the working paper is the importance of and interactions between AI/ML complexity, uncertainty, contingency, and ambiguity. 

The working paper builds on and extends to the AI/ML world the research, working papers and conference presentations of the author over more than two decades, which apply a behavioral law and economics and complexity lens to: the compliance and non-compliance of digital and more conventional regulated entities with laws, regulations and social norms; the decision errors and error-cost minimization efforts of state and non-state regulators, with emphasis on the interactions between excessive and under enforcement decision errors; and the two-way interactive/feedback effects between regulator and regulated entity decision errors.

Keywords: competition policy and law, consumer protection, privacy, innovation, artificial intelligence, digital markets and platforms,