I review and challenge the views on simplicity and its role in linguistics put forward by Ludlow. In particular, I criticize the claim that simplicity—in the sense pertinent to science—is nothing more than ease of use or “user-friendliness”, motivated by economy of labor. I argue that Ludlow’s discussion fails to do justice to the diversity of factors that are relevant to simplicity considerations. This, in turn, leads to the neglect of crucial cases in which the rationale for simplification is unmistakably (...) epistemic, as well as instances where simplicity is part of the content of substantive, empirical hypotheses. I illustrate these points with examples from the history of generative linguistics, such as: the shaping influence exerted by simplicity, via its involvement in the notion of “linguistically significant generalization”, its methodological and substantive contribution to the goal of explanatory adequacy, and its central role in the Minimalist Program’s search “beyond explanatory adequacy”. (shrink)

In this paper, I consider the relationship between Inference to the Best Explanation and Bayesianism, both of which are well-known accounts of the nature of scientific inference. In Sect. 2, I give a brief overview of Bayesianism and IBE. In Sect. 3, I argue that IBE in its most prominently defended forms is difficult to reconcile with Bayesianism because not all of the items that feature on popular lists of “explanatory virtues”—by means of which IBE ranks competing explanations—have confirmational import. (...) Rather, some of the items that feature on these lists are “informational virtues”—properties that do not make a hypothesis \ more probable than some competitor \ given evidence E, but that, roughly-speaking, give that hypothesis greater informative content. In Sect. 4, I consider as a response to my argument a recent version of compatibilism which argues that IBE can provide further normative constraints on the objectively correct probability function. I argue that this response does not succeed, owing to the difficulty of defending with any generality such further normative constraints. Lastly, in Sect. 5, I propose that IBE should be regarded, not as a theory of scientific inference, but rather as a theory of when we ought to “accept” H, where the acceptability of H is fixed by the goals of science and concerns whether H is worthy of commitment as research program. In this way, IBE and Bayesianism, as I will show, can be made compatible, and thus the Bayesian and the proponent of IBE can be friends. (shrink)

The place of induction in the framing and test of scientific hypotheses is investigated. The meaning of 'induction' is first equated with generalization on the basis of case examination. Two kinds of induction are then distinguished: the inference of generals from particulars (first degree induction), and the generalization of generalizations (second degree induction). Induction is claimed to play a role in the framing of modest empirical generalizations and in the extension of every sort of generalizations--not however in the invention of (...) high-level hypotheses containing theoretical predicates. It is maintained, on the other hand, that induction by enumeration is essential in the empirical test of the lowest-level consequences of scientific theories, since it occurs in the drawing of "conclusions" from the examination of empirical evidence. But it is also held that the empirical test is insufficient, and must be supplemented with theorification, or the expansion of isolated hypotheses into theories. Refutation is not viewed as a substitute for confirmation but as its complement, since the very notion of unfavorable case is meaningful only in connection with the concept of positive instance. Although the existence of an inductive method is disclaimed, it is maintained that the various patterns of plausible reasoning (inductive inference included) are worth being investigated. It is concluded that scientific research follows neither the advice of inductivism nor the injunction of deductivism, but takes a middle course in which induction is instrumental both heuristically and methodologically, although the over-all pattern of research is hypothetico-deductive. (shrink)

The article focusses on grey system theory and its methodological foundations. Key topics include: axiomatisation of the concept of grey, comparison of grey systems theory with fuzzy logic and probabilistic approaches, and methodological development of the systems approach in grey data modelling. The article discusses in detail the challenges of defining grey space, grey functions, and their applications in solving the methodological problems of grey systems theory. The differences between grey systems theory and other analytical methodologies are highlighted, paying attention (...) to the specifics of the data and the research context and epistemological perspective. (shrink)

The group theorist William Burnside devoted much of the last decade of his life to probability and statistics. The work led to contact with Ronald Fisher who was on his way to becoming the leading statistician of the age and with Karl Pearson, the man Fisher supplanted. Burnside corresponded with Fisher for nearly three years until their correspondence ended abruptly. This paper examines Burnside’s interactions with the statisticians and looks more generally at his work in probability and statistics.

An inductive logic is a system of inference that describes the relation between propositions on data, and propositions that extend beyond the data, such as predictions over future data, and general conclusions on all possible data. Statistics, on the other hand, is a mathematical discipline that describes procedures for deriving results about a population from sample data. These results include predictions on future samples, decisions on rejecting or accepting a hypothesis about the population, the determination of probability assignments over such (...) hypotheses, the selection of a statistical model for studying the population, and so on. Both inductive logic and statistics are calculi for getting from the given data to propositions or results that transcend the data. (shrink)

Harold Jeffreys' ideas on the interpretation of probability and epistemology are reviewed. It is argued that with regard to the interpretation of probability, Jeffreys embraces a version of logicism that shares some features of the subjectivism of Ramsey and de Finetti. Jeffreys also developed a probabilistic epistemology, characterized by a pragmatical and constructivist attitude towards notions such as ‘objectivity’, ‘reality’ and ‘causality’. 1 Introductory remarks 2 The interpretation of probability 3 Jeffreys' probabilistic epistemology.

Introduction: Purpose of this essay is to draw attention to some points which are relevant to the underlying philosophy of modern statistics, but which the writer feels have been largely overlooked both by the defenders and the opponents of the classic conceptions of Laplace. There is no quarrel with methodologies as such which have found their introduction under the heads of “maximum likelihood”, “fiducial limits”, etc. But the writer cannot accept arguments ) which would make of such procedures an absolute (...) sine qua non for all decisions based on evidence, and which would relegate human judgment and all considerations of the intended use of a decision to the rubbish heap of outmoded conceptions. To assume that two rational minds, having different backgrounds and different objectives, must necessarily find themselves in agreement in their appraisal of a given objective situation, is simply absurd; yet this is the basis which underlies, so far as I can make out, all the “criticisms” of Laplace and Bayes which constitute the excuses put forward for attempting to displace those procedures in Statistics which involve “inverse probability”. Also the so-called “paradoxes” which have been invented by many writers in order to assail Laplace, contradict only this one assumption; without it they are not paradoxes. (shrink)

The Jeffreys–Lindley paradox exposes a rift between Bayesian and frequentist hypothesis testing that strikes at the heart of statistical inference. Contrary to what most current literature suggests, the paradox was central to the Bayesian testing methodology developed by Sir Harold Jeffreys in the late 1930s. Jeffreys showed that the evidence for a point-null hypothesis $${\mathcal {H}}_0$$ H 0 scales with $$\sqrt{n}$$ n and repeatedly argued that it would, therefore, be mistaken to set a threshold for rejecting $${\mathcal {H}}_0$$ H 0 (...) at a constant multiple of the standard error. Here, we summarize Jeffreys’s early work on the paradox and clarify his reasons for including the $$\sqrt{n}$$ n term. The prior distribution is seen to play a crucial role; by implicitly correcting for selection, small parameter values are identified as relatively surprising under $${\mathcal {H}}_1$$ H 1. We highlight the general nature of the paradox by presenting both a fully frequentist and a fully Bayesian version. We also demonstrate that the paradox does not depend on assigning prior mass to a point hypothesis, as is commonly believed. (shrink)