Online research in philosophy

In The Rationality of Induction, David Stove presents an argument against scepticism about inductive inference—where, for Stove, inductive inference is inference from the observed to the unobserved. Let U be a finite collection of n particulars such that each member of U either has property F-ness or does not. If s is a natural number less than n, define an s-fold sample of U as s observations of distinct members of U each either having F-ness or not having F-ness. Let pU denote the proportion of members of U that are Fs and, if S is an s-fold sample of U, let pS denote the proportion of members of S that are Fs. Call S representative if and only if |pS – pU|<0.01. Stove‘s argument against inductive scepticism is built on the following statistical fact:

As s gets larger the proportion of all possible s-fold samples of U that are representative gets closer to 1 (regardless of the size of U or of the value of pU).

In this essay I subject Stove‘s argument to thorough scrutiny. I show that the argument – as it stands – is incomplete, and I illuminate the issues involved in trying to fill the gaps. Along the way I demonstrate that one of the commonest objects to Stove‘s argument misses the point.

This paper attempts to place Goodman's "New Riddle of Induction" within the context of a subjectivist understanding of inductive logic. It will be argued that predicates such as 'grue' cannot be denied projectible status in any a priori way, but must be considered in the context of a situation of inductive support. In particular, it will be argued that questions of projectibility are to be understood as a variety of questions about the ways a given sample is random. Various examples are considered, including cases when 'grue' (as opposed to 'green') should be projected, and some remarks are offered on what is meant when it is claimed that a sample is random (in ways relevant to inductive support) and how such randomness is determined. The view presented helps to make clear the relation between such apparently non-projectible predicates as 'grue' and the concept of being examined.

Nelson Goodman cast the ‘problem of induction’ as the task of articulating the principles and standards by which to distinguish valid from invalid inductive inferences. This paper explores some logical bounds on the ability of a rational reasoner to accomplish this task. By a simple argument, either an inductive inference method cannot admit its own fallibility, or there exists some non-inferable hypothesis whose non-inferability the method cannot infer (violating the principle of ‘negative introspection’). The paper discusses some implications of this limited self-knowledge for the justifiability of inductive inferences, auto-epistemic logic, and the epistemic foundations of game theory.

First, a brief historical trace of the developments in confirmation theory leading up to Goodman’s infamous “grue” paradox is presented. Then, Goodman’s argument is analyzed from both Hempelian and Bayesian perspectives. A guiding analogy is drawn between certain arguments against classical deductive logic, and Goodman’s “grue” argument against classical inductive logic. The upshot of this analogy is that the “New Riddle” is not as vexing as many commentators have claimed (especially, from a Bayesian inductive-logical point of view). Specifically, the analogy reveals an intimate connection between Goodman’s problem, and the “problem of old evidence”. Several other novel aspects of Goodman’s argument are also discussed (mainly, from a Bayesian perspective).

This paper aims to be a friendly introduction to formal learning theory. I introduce key concepts at a slow pace, comparing and contrasting with other approaches to inductive inference such as con…rmation theory. A number of examples are discussed, some in detail, such as Goodman’s Riddle of Induction. I outline some important results of formal learning theory that are of philosophical interest. Finally, I discuss recent developments in this approach to inductive inference.

This chapter1 concerns the relation between statistics and inductive logic. I start by describing induction in formal terms, and I introduce a general notion of probabilistic inductive inference. This provides a setting in which statistical procedures and inductive logics can be cap- tured. Speciacally, I discuss three statistical procedures (hypotheses testing, parameter estimation, and Bayesian statistics) and I show to what extend they can be captured by certain inductive logics. I end with some suggestions on how inductive.

Since the mid-1970s, scholars have recognized that the skeptical interpretation of Hume’s central argument about induction is problematic. The science of human nature presupposes that inductive inference is justified and there are endorsements of induction throughout Treatise Book I. The recent suggestion that I.iii.6 is confined to the psychology of inductive inference cannot account for the epistemic flavor of its claims that neither a genuine demonstration nor a non-question-begging inductive argument can establish the uniformity principle. For Hume, that inductive inference is justified is part of the data to be explained. Bad argument is therefore excluded as the cause of inductive inference; and there is no good argument to cause it. Does this reinstate the problem of induction, undermining Hume’s own assumption that induction is justified? It does so only if justification must derive from “reason”, from the availability of a cogent argument. Hume rejects this internalist thesis; induction’s favorable epistemic status derives from features of custom, the mechanism that generates inductive beliefs. Hume is attracted to this externalist posture because it provides a direct explanation of the epistemic achievements of children and non-human animals—creatures that must rely on custom unsupplemented by argument.

It has been common wisdom for centuries that scientific inference cannot be deductive; if it is inference at all, it must be a distinctive kind of inductive inference. According to demonstrative theories of induction, however, important scientific inferences are not inductive in the sense of requiring ampliative inference rules at all. Rather, they are deductive inferences with sufficiently strong premises. General considerations about inferences suffice to show that there is no difference in justification between an inference construed demonstratively or ampliatively. The inductive risk may be shouldered by premises or rules, but it cannot be shirked. Demonstrative theories of induction might, nevertheless, better describe scientific practice. And there may be good methodological reasons for constructing our inferences one way rather than the other. By exploring the limits of these possible advantages, I argue that scientific inference is neither of essence deductive nor of essence inductive.

Overall, Max Black's defense of the inductive support of inductive rules succeeds. Circularity is best explained in terms of epistemic conditions of inference. When an inference is circular, another inference token of the same type may, because of a difference of surrounding circumstances, not be circular. Black's inductive arguments in support of inductive rules fit this pattern: a token circular in some circumstances may be noncircular in other circumstances.

Contrary to formal theories of induction, I argue that there are no universal inductive inference schemas. The inductive inferences of science are grounded in matters of fact that hold only in particular domains, so that all inductive inference is local. Some are so localized as to defy familiar characterization. Since inductive inference schemas are underwritten by facts, we can assess and control the inductive risk taken in an induction by investigating the warrant for its underwriting facts. In learning more facts, we extend our inductive reach by supplying more localized inductive inference schemes. Since a material theory no longer separates the factual and schematic parts of an induction, it proves not to be vulnerable to Hume's problem of the justification of induction.