Summary |
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. |