Verisimilitude

Truth is the aim of inquiry, but some theories seem to better approximate that goal than others. One may think, for instance, that Copernicus’ model of the solar system is closer to the truth than Ptolemy’s. Since Copernicus assumed that planetary orbits were circles, his theory is, strictly speaking, false. Thus, we have here a case of a false theory which better approximates the truth than another false theory. Moreover, Copernicus’ theory also seems better than the plain claim that planets move in orbits: so, false propositions may sometimes be more verisimilar even than true but uninformative ones. This suggests that verisimilitude (or truthlikeness, as it is also known) is a mixture of truth and informative content: a theory is verisimilar when it tells many things about the world, and many of these things are true. Explicating this intuition in a rigorous way amounts to solving the so-called logical problem of truthlikeness. In normal circumstances, we simply don’t know what the truth about a given matter is. Thus, we cannot directly assess the relative verisimilitude of competing theories. However, we can estimate their verisimilitude on the basis of available evidence: explaining exactly how this can be done means answering to the so-called epistemic problem of truthlikeness. Interestingly, estimated verisimilitude doesn’t follow the rules of probability. In particular, it may be that a theory with low probability is estimated as highly truthlike; and, a logically stronger theory may be estimated as more verisimilar than a logically weaker one. This makes (estimated) verisimilitude a mathematically less well-behaved notion than probability; still, it provides the philosopher of science with a flexible and useful tool to analyze such issues as theory change, scientific progress, convergence to the truth, and the realism/antirealism debate.