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Life sciences · Preprint
arXiv · September 9, 2026
The material analysed did not support any firm read.
This is an unrefereed preprint presenting a philosophical and mathematical framework that attempts to unify different interpretations of probability by characterizing all probabilities as outputs of prediction methods. The work is theoretical, offers no empirical validation, and has not undergone peer review.
Preprint.
Proposes that every probability depends on both a method of constructing abstractions and a method of transforming them into predictions Claims that even supposedly objective probabilities are model-dependent Argues that when a finite calibration criterion is met, one can anticipate the distribution of utilities for a given policy and inform decision-making on finite sets of events
Safety was not reported in the material analysed. Check the source before drawing any conclusion about harm.
The source did not state who this applies to in practice.
A theoretical framework paper proposing a unifying philosophical perspective on probability interpretation; raises conceptual questions about the nature of probabilistic statements rather than testing empirical claims or providing actionable clinical guidance.
Graded across the dimensions that decide whether you should act, each from what the source actually supports. There is no single score, and where a dimension was not assessed it says so.
What is missing. This record has no reported figures. That is a gap in the analysis, not a judgement about the study.
Although probabilistic statements are ubiquitous, foundational disagreements persist about their understanding, as exemplified by debates between Bayesians and frequentists; moreover, it is unclear when and why acting on them actually leads to desirable outcomes. Here, we argue that every probability is the output of a \emph{prediction method}, that is, it depends on both a particular way of constructing abstractions and a way of transforming them into predictions. Through this, we provide a unifying perspective on supposedly different kinds of probabilities and show that even supposedly objective ones are model-dependent. We demonstrate that when a finite calibration criterion is met, one can anticipate the distribution of utilities for a given policy and inform successful decision-making on finite sets of events. Based on the notion of prediction methods, inductive arguments, and the probability calculus, we explain the feasibility of the calibration criterion in many settings. Overall, we develop a coherent perspective on probabilities and their use, connecting key intuitions behind other interpretations along the way.
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