Should We Believe That Our Current Best Scientific Theories Are Approximately True, or Will Future Theories Look Substantially Different?
Science has boosted living standards, has enabled humans to travel into Earth’s orbit and to the moon, and has given us new ways of thinking about ourselves and the universe.
A common concern is that notions of simplicity appear vague, and judgments about the relative simplicity of particular theories appear irredeemably subjective. Thus, one problem is to explain more precisely what it is for theories to be simpler than others and how, if at all, the relative simplicity of theories can be objectively measured. In addition, even if we can get clearer about what simplicity is and how it is to be measured, there remains the problem of explaining what justification, if any, can be provided for choosing between rival scientific theories on grounds of simplicity (Crick, F. 1988). For instance, do we have any reason for thinking that simpler theories are more likely to be true?
Why should we believe this principle to be true? Hume insists that we provide some reason in support of this belief. Because the above argument is an inductive rather than a deductive argument, the problem of showing that it is a good argument is typically referred to as the “problem of induction.” We might think that there is a simple and straightforward solution to the problem of induction, and that we can indeed provide support for our belief that PUN is true.
Crick, F. 1988. What Mad Pursuit: a Personal View of Scientific Discovery. New York: Basic Books.
Dowe, D, Gardner, S., and Oppy, G. 2007. Bayes not bust! Why simplicity is no problem for Bayesians. British Journal for the Philosophy of Science, 58, 709-754.
Contra Forster and Sober (1994), argues that Bayesians can make sense of the role of simplicity in curve-fitting.
Duhem, P. 1954. The Aim and Structure of Physical Theory. Princeton: Princeton University Press.
Fitzpatrick, S. 2009. The primate mindreading controversy: a case study in simplicity and methodology in animal psychology. In R. Lurz (ed.), The Philosophy of Animal Minds. New York: Cambridge University Press.