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Studies in History and Philosophy of Science Part B Studies in History and Philosophy of Modern Physics

What econometrics cannot teach quantum mechanics

Joseph Berkovitz

Published Aug 1, 1995• 5 citations
Publisher
DOI
10.1016/1355-2198(95)00012-7

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Abstract This chapter gives a concrete example of a question of current scientific interest where capacities matter: ‘Do the Bell inequalities describing Einstein–Podolsky–Rosen (EPR) experiments show that causality is incompatible with quantum mechanics?’ The question cannot be answered if we rely on probabilistic theories of causality and laws of associations alone. It takes the concept of capacity and related notions of how capacities operate even to formulate the problem correctly. Econometrics models with correlated errors, it is shown, can explain the EPR results when the correlations are given a capacity interpretation.
This book examines a specific methodological gap: the absence of a benchmarking comparison between quantum machine learning (QML) and classical econometric methods applied to the problem of measuring and explaining AI governance readiness — a comparison already conducted in finance and, more distantly, in climate science, but not yet in this field. Building from foundational quantum formalism and the current econometric toolkit used in AI governance research, the book traces the genealogy of quantum econometrics from mathematical analogy to applied benchmarking, then runs an illustrative case study comparing a bootstrap mediation pipeline against a quantum-kernel regression on a 23-country synthetic dataset structured to mirror real cross-national governance data. It concludes that classical econometrics currently retains a demonstrated advantage for this specific research problem, and proposes a concrete, conditional research agenda for revisiting the comparison as AI-governance data matures.
This book examines a specific methodological gap: the absence of a benchmarking comparison between quantum machine learning (QML) and classical econometric methods applied to the problem of measuring and explaining AI governance readiness — a comparison already conducted in finance and, more distantly, in climate science, but not yet in this field. Building from foundational quantum formalism and the current econometric toolkit used in AI governance research, the book traces the genealogy of quantum econometrics from mathematical analogy to applied benchmarking, then runs an illustrative case study comparing a bootstrap mediation pipeline against a quantum-kernel regression on a 23-country synthetic dataset structured to mirror real cross-national governance data. It concludes that classical econometrics currently retains a demonstrated advantage for this specific research problem, and proposes a concrete, conditional research agenda for revisiting the comparison as AI-governance data matures.