The Dialectical Transformation of Ceteris Paribus in the Age of Big Data: Toward a Hybrid Paradigm

The structural limitations of the ceteris paribus assumption in complex economic systems raise fundamental questions about its methodological relevance in the data-driven era. This article examines the transformation of the epistemological role of ceteris paribus under the conditions of Big Data, machine learning (ML), artificial intelligence (AI), and agent-based modeling (ABM). The paper argues that Big Data does not invalidate the logic of ceteris paribus; rather, it transforms its operationalization. The assumption shifts from theoretically imposed “constant conditions” toward empirically informed control mechanisms based on large-scale data analysis. This transformation is enabled by interpretable machine learning approaches (LIME, SHAP, PDP, ICE), nowcasting techniques, ABM, and contemporary methods of causal inference. The central thesis of the paper is that ceteris paribus and Big Data are not antagonistic but instead form a dialectical interaction, in which abstraction (deduction) and empirical reality (induction) are reconciled through abduction. This interaction generates an iterative cycle in which each stage contributes to the refinement of both economic theory and its empirical foundations. Within this framework, Friedman’s instrumentalist methodology is reconsidered in light of the analytical possibilities offered by Big Data. The paper proposes a hybrid modeling paradigm in which economic theory provides the causal structure, while Big Data and AI/ML enable empirical validation, calibration, and refinement of this structure. To quantify this dialectical relationship, the paper introduces the Ceteris Paribus Validity Coefficient (CPV) and its extensions -CPV(BD) and AI-CPV - as instruments for evaluating the validity of ceteris paribus assumptions. The CPV framework provides a formal metric for assessing how well a given model approximates the "other things equal" condition, with higher values indicating stronger empirical grounding and reduced reliance on untested abstractions.