David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
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A ﬁnite data set is consistent with inﬁnitely many alternative theories. Scientiﬁc realists recommend that we prefer the simplest one. Anti-realists ask how a ﬁxed simplicity bias could track the truth when the truth might be complex. It is no solution to impose a prior probability distribution biased toward simplicity, for such a distribution merely embodies the bias at issue without explaining its eﬃcacy. In this note, I argue, on the basis of computational learning theory, that a ﬁxed simplicity bias is necessary if inquiry is to converge to the right answer eﬃciently, whatever the right answer might be. Eﬃciency is understood in the sense of minimizing the least ﬁxed bound on retractions or errors prior to convergence.
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