Proponency as an Applicability Operator: Reconciling Class Probability with Individual Experience

This article introduces and develops the Principle of Individual Proponency Adjustment to the Applicability of Class Probability, a conceptual and methodological framework addressing a longstanding problem in the philosophy of probability and applied decision-making: how population-level probabilities can be rationally used in individual, non-repeatable cases without committing the error of treating individuals as probability-bearing entities. Probability, as traditionally understood in frequentist, Bayesian, and propensity-based frameworks, is inherently class-based. It describes distributions over reference classes rather than properties of singular individuals or one-off events. Nevertheless, probabilistic statements are routinely extended to individual contexts in medicine, economics, risk assessment, meteorology, and everyday reasoning, often producing epistemic overconfidence, misinterpretation, or retrospective narratives of "luck" and "risk." The article argues that this tension does not arise from incorrect probabilities, but from the unregulated transfer of valid class probabilities into individual decision contexts where their applicability may be structurally degraded. To address this, the article introduces proponency as a non-predictive, non-causal applicability operator. Proponency is defined as an ordinal descriptor of structural displacement between an individual trajectory and a probabilistic reference class. It does not generate individual probabilities, revise population statistics, or explain outcomes. Instead, it regulates when and to what extent a class probability remains epistemically usable for an individual case. The framework preserves class probability as the only genuine probability while introducing explicit boundary conditions for its individual application. A heuristic formalization is presented to make implicit judgment criteria explicit, alongside a practical operational procedure based on ordinal assessment of cumulative individual deviation. The concept of epistemic saturation is introduced to mark the point at which class probability, though still valid for the population, loses informational value for individual decision-making and should be replaced by alternative non-probabilistic decision strategies. Changes in Version 2.0 This revision substantially strengthens the connection between the proponency framework and normative decision theory. The key additions and modifications include: Decision-Theoretic Integration: Proponency is now explicitly interpreted as an operator regulating the normative permissibility of expected-utility reasoning. The article specifies when expected-utility maximization is rationally permitted (positive residual applicability), when its normative force is degraded (approaching epistemic saturation), and when it must yield to alternative decision frameworks (saturation reached). New Section on Decision Theory: A dedicated section (Section 4) clarifies that proponency does not compete with probability theory or decision theory but operates at a prior level, determining which decision framework is applicable under given epistemic conditions. This reframes proponency as a boundary condition on rational choice rather than a modification of probabilities or utilities. Engagement with Transformative Experience Literature: Following consultation with Professor Richard Pettigrew during the development of the preceding paper, this version includes an explicit philosophical comparison with Pettigrew's work on transformative experience (Choosing for Changing Selves, 2019) and L.A. Paul's foundational contributions (Transformative Experience, 2014). The article clarifies that proponency generalizes the problem identified in that literature: even when class probabilities exist and are well-defined, they may be rationally inapplicable to individual decisions due to structural displacement. The frameworks are positioned as complementary rather than competing. Alternative Decision Frameworks: The revision specifies concrete alternative decision strategies (precautionary reasoning, minimax, satisficing, reversibility, expert consultation) appropriate when epistemic saturation is reached and expected-utility reasoning loses normative warrant. Illustrative Decision-Theoretic Example: A new conceptual example demonstrates how two actions with known class probabilities become non-orderable by expected utility once applicability is exhausted, showing the practical implications of the framework. Updated Abstract and Conclusion: Both now explicitly state that the framework contributes to decision theory by specifying when probabilistic optimization is rationally permitted, degraded, or forbidden. Acknowledgements This article benefited from correspondence with Professor Alan Hájek (Australian National University), whose remarks emphasized the importance of connecting this framework explicitly to decision theory—particularly in light of recent work on transformative experience. The decision-theoretic interpretation of proponency developed in Section 4 was substantially clarified in response to his guidance. The author also acknowledges prior correspondence with Professor Richard Pettigrew (University of Bristol) during the development of the preceding paper on the Principle of Optimal Applicability of Probability, which informed the present engagement with the transformative experience literature and the understanding of boundary conditions on expected-utility reasoning. Situating the Work The article situates this approach within the broader literature on the reference class problem (Reichenbach, Hájek), heterogeneity of treatment effects (Kent et al.), Knightian uncertainty, Keynesian weight of evidence, and critiques of individualized risk in evidence-based medicine and risk communication. It explicitly distinguishes the proponency framework from Bayesian updating, propensity theories, and subclassification strategies, emphasizing that it operates at a meta-epistemic level rather than within probabilistic inference itself. This work does not aim to predict outcomes, produce personalized risk estimates, or replace statistical models. Its contribution lies in clarifying the epistemic boundary between measurable risk and structural uncertainty, and in specifying when probability-based decision rules retain normative force for individual agents. It offers a disciplined way to use population-derived probabilities in individual contexts without collapsing into pseudo-precision, subjectivism, or metaphysical speculation. Intended Audience The article is intended for researchers and practitioners in philosophy of probability, philosophy of science, decision theory, medical epistemology, risk analysis, and related interdisciplinary fields where population statistics must be responsibly applied to singular decisions. Keywords probability; reference class problem; decision theory; expected utility; proponency; applicability; epistemic saturation; transformative experience; Knightian uncertainty; philosophy of probability; risk; individual decision-making