Inference to the best explanation and Norton's material theory of induction

I argue that we should consider Norton's material theory of induction as consisting of two largely independent claims. First, there is the claim that material facts license inductions - a claim which I interpret as a type of contextualism about induction. Second, there is the claim that there are no universal rules of induction. While a good case can be made for the first claim, I believe that Norton's arguments for the second claim are lacking. In particular, I spell out Norton's argument against the claim that all induction may be reduced to inference to the best explanation, and argue that it is not persuasive. Rejecting this part of Norton's theory does not however require us to abandon the first claim that material facts license inductions. In this way, I distinguish the parts of the material theory of induction we should happily accept from the parts about which we should be more skeptical.     

The material theory of induction and the epistemology of thought experiments

John D. Norton is responsible for a number of influential views in contemporary philosophy of science. This paper will discuss two of them. The material theory of induction claims that inductive arguments are ultimately justified by their material features, not their formal features. Thus, while a deductive argument can be valid irrespective of the content of the propositions that make up the argument, an inductive argument about, say, apples, will be justified (or not) depending on facts about apples. The argument view of thought experiments claims that thought experiments are arguments, and that they function epistemically however arguments do. These two views have generated a great deal of discussion, although there hasn't been much written about their combination. I argue that despite some interesting harmonies, there is a serious tension between them. I consider several options for easing this tension, before suggesting a set of changes to the argument view that I take to be consistent with Norton's fundamental philosophical commitments, and which retain what seems intuitively correct about the argument view. These changes require that we move away from a unitary epistemology of thought experiments and towards a more pluralist position.     

Epistemic benefits of the material theory of induction

C. D. Broad famously labelled the problem of providing our inductive practices with a proper justification “the scandal of philosophy” (Broad, 1952). Recently, John Norton has provided a dissolution of this problem (2014). According to Norton, inductive inference is grounded in particular facts obtaining within particular domains (J. Norton, 2003b, 2010, 2014). Because the material theory does not involve a universal schema of induction, Norton claims it dissolves the problem of induction (which implies that such universal schemas cannot be justified).In this paper, I critically evaluate Norton's dissolution. In particular, I argue that the problem of induction is an epistemological problem, that Norton's material theory entails an externalist epistemology, and that it is a common feature of such epistemologies that they dissolve the problem of induction. The upshot is that the material theory is not unique in its ability to reap the specifically epistemic benefits of dissolving the problem of induction, and thus that the epistemic advantages of the material theory over extant alternatives in this regard are fewer than it may appear at first sight.     

How to think about analogical inferences: A reply to Norton

In this paper, I raise some worries with John D. Norton's application of his material theory of induction to the study of analogical inferences. Skeptical that these worries can be properly addressed, I propose a principle to guide the philosophical research on analogical inferences and argue for its usefulness.     

Thomas Reid and the problem of induction: from common experience to common sense

By the middle of the eighteenth century the new science had challenged the intellectual primacy of common experience in favor of recondite, expert and even counter-intuitive knowledge increasingly mediated by specialized instruments. Meanwhile modern philosophy had also problematized the perceptions of common experience — in the case of David Hume this included our perception of causal relations in nature, a fundamental precondition of scientific endeavor.In this article I argue that, in responding to the ‘problem of induction’ as advanced by Hume, Reid reformulated Aristotelian foundationalism in distinctly modern terms. An educator and mathematician self-consciously working within the framework of the new science, Reid articulated a philosophical foundation for natural knowledge anchored in the human constitution and in processes of adjudication in an emerging modern public sphere of enlightened discourse. Reid thereby transformed one of the bases of Aristotelian science — common experience — into a philosophically and socially justified notion of ‘common sense’. Reid's intellectual concerns had as much to do with the philosophy of science as they did with moral philosophy or epistemology proper, and were bound up with wider social and scientific changes taking place in the early modern period.     

Inductive reasoning in the context of discovery: Analogy as an experimental stratagem in the history and philosophy of science

Building on Norton's “material theory of induction,” this paper shows through careful historical analysis that analogy can act as a methodological principle or stratagem, providing experimentalists with a useful framework to assess data and devise novel experiments. Although this particular case study focuses on late eighteenth and early nineteenth-century experiments on the properties and composition of acids, the results of this investigation may be extended and applied to other research programs. A stage in-between what Steinle calls “exploratory experimentation” and robust theory, I argue that analogy encouraged research to substantiate why the likenesses should outweigh the differences (or vice versa) when evaluating results and designing experiments.     

Empirical data sets are algorithmically compressible: reply to McAllister?

James McAllister’s 2003 article, ‘Algorithmic randomness in empirical data’, claims that empirical data sets are algorithmically random, and hence incompressible. We show that this claim is mistaken. We present theoretical arguments and empirical evidence for compressibility, and discuss the matter in the framework of Minimum Message Length (MML) inference.