Abstract
The basic problem of a theory of truth approximation is defining when a theory is “close to the truth” about some relevant domain. Existing accounts of truthlikeness or verisimilitude address this problem, but are usually limited to the problem of approaching a “deterministic” truth by means of deterministic theories. A general theory of truth approximation, however, should arguably cover also cases where either the relevant theories, or “the truth”, or both, are “probabilistic” in nature. As a step forward in this direction, we first present a general characterization of both deterministic and probabilistic truth approximation; then, we introduce a new account of verisimilitude which provides a simple formal framework to deal with such issue in a unified way. The connections of our account with some other proposals in the literature are also briefly discussed.
Highlights
Popper introduced the notion of the truthlikeness of a scientific theory or hypothesis in order to make sense of the idea that the goal of inquiry is an increasing approximation to “the whole truth” about the relevant domain
We define a measure of the truthlikeness of both deterministic and probabilistic theories in terms of the probability they assign to the “basic features” of the relevant domain
This measure fits well within the so called basic feature (BF) approach to verisimilitude, and shares interesting connections with other accounts proposed in the literature
Summary
Popper introduced the notion of the truthlikeness (or verisimilitude) of a scientific theory or hypothesis in order to make sense of the idea that the goal of inquiry is an increasing approximation to “the whole truth” about the relevant domain Explicating this intuition is the shared problem of all post-Popperian accounts of verisimilitude, which differ from each other on the way to solve it, and disagree on some crucial features of the notion of truthlikeness. 3, we introduce a new, probability-based measure of truthlikeness, following the idea that a theory is the closer to the truth the more it “agrees” with it on the basic features of the domain; as we argue, this account provides a common, abstract framework for studying both deterministic and probabilistic truth approximation. The proofs of the main claims in the paper appear in the final appendix
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