Primarily between 1833 and 1840, William Whewell attempted to accomplish what natural philosophers and scientists since at least Galileo had failed to do: to provide a systematic and broad-ranged study of the tides and to attempt to establish a general scientific theory of tidal phenomena. I document the close interaction between Whewell’s philosophy of science and his scientific practice as a tidologist. I claim that the intertwinement between Whewell’s methodology and his tidology is more fundamental than has hitherto been documented.Keywords: (...) William Whewell; Tidology; History of HPS; Scientific methodology; Whewell papers. (shrink)

Michael Weisberg’s account of scientific models concentrates on the ways in which models are similar to their targets. He intends not merely to explain what similarity consists in, but also to capture similarity judgments made by scientists. In order to scrutinize whether his account fulfills this goal, I outline one common way in which scientists judge whether a model is similar enough to its target, namely maximum likelihood estimation method. Then I consider whether Weisberg’s account could capture the judgments involved (...) in this practice. I argue that his account fails for three reasons. First, his account is simply too abstract to capture what is going on in MLE. Second, it implies an atomistic conception of similarity, while MLE operates in a holistic manner. Third, Weisberg’s atomistic conception of similarity can be traced back to a problematic set-theoretic approach to the structure of models. Finally, I tentatively suggest how these problems might be solved by a holistic approach in which models and targets are compared in a non-set-theoretic fashion. (shrink)

This paper defends the deflationary character of two recent views regarding scientific representation, namely RIG Hughes’ DDI model and the inferential conception. It is first argued that these views’ deflationism is akin to the homonymous position in discussions regarding the nature of truth. There, we are invited to consider the platitudes that the predicate “true” obeys at the level of practice, disregarding any deeper, or more substantive, account of its nature. More generally, for any concept X, a deflationary approach is (...) then defined in opposition to a substantive approach, where a substantive approach to X is an analysis of X in terms of some property P, or relation R, accounting for and explaining the standard use of X. It then becomes possible to characterize a deflationary view of scientific representation in three distinct senses, namely: a “no-theory” view, a “minimalist” view, and a “use-based” view – in line with three standard deflationary responses in the philosophical literature on truth. It is then argued that both the DDI model and the inferential conception may be suitably understood in any of these three different senses. The application of these deflationary ‘hermeneutics’ moreover yields significant improvements on the DDI model, which bring it closer to the inferential conception. It is finally argued that what these approaches have in common – the key to any deflationary account of scientific representation – is the denial that scientific representation may be ultimately reduced to any substantive explanatory property of sources, or targets, or their relations. (shrink)

This paper critically examines Weisberg’s weighted feature matching account of model-world similarity. A number of concerns are raised, including that Weisberg provides an account of what underlies scientific judgments of relative similarity, when what is desired is an account of the sorts of model-target similarities that are necessary or sufficient for achieving particular types of modeling goal. Other concerns relate to the details of the account, in particular to the content of feature sets, the nature of shared features and the (...) assumed independence of feature weightings. (shrink)

In this paper, I examine William Whewell’s (1794–1866) ‘Discoverer’s Induction’, and argue that it 21 supplies a strikingly accurate characterization of the logic behind many statistical methods, exploratory 22 data analysis (EDA) in particular. Such methods are additionally well-suited as a point of evaluation of 23 Whewell’s philosophy since the central techniques of EDA were not invented until after Whewell’s death, 24 and so couldn’t have influenced his views. The fact that the quantitative details of some very general 25 methods (...) designed to suggest hypotheses would so closely resemble Whewell’s views of how theories 26 are formed is, I suggest, a strongly positive comment on these views. (shrink)

Most contributions to Whewell scholarship have tended to stress the idealistic, antiempirical temper of Whewell’s philosophy. Thus, the only two monograph-length studies on Whewell, Blanché’s Le Rationalisme de Whewell and Marcucci’s L’ ‘Idealismo’ Scientifico di William Whewell, are, as their titles suggest, concerned primarily with Whewell’s departures from classical British empiricism. Particularly in his famous dispute with Mill, it has proved tempting to parody Whewell’s position in the debate by treating it as a straightforward encounter between an arch-empiricist and an (...) arch-rationalist. There is, however, a danger that an emphasis on the necessitarian and a priori elements in Whewell’s philosophy may well obscure the unmistakable empirical emphasis in Whewell’s theory of science. I think it is time to begin to redress the balance, by focusing attention on the significant ‘empiricist’ strains in Whewell’s philosophy of science. One of the most important of those strains is connected with the operation which Whewell calls ‘the consilience of inductions’. (shrink)

In several accounts of what models are and how they function a specific view dominates. This view contains the following characteristics. First, there is a clear-cut distinction between theories, models and data and secondly, empirical assessment takes place after the model is built. This view in which discovery and justification are disconnected is not in accordance with several practices of mathematical business-cycle model building. What these practices show is that models have to meet implicit criteria of adequacy, such as satisfying (...) theoretical, mathematical and statistical requirements, and be useful for policy. In order to be adequate, models have to integrate enough items to satisfy such criteria. These items include besides theoretical notions, policy views, mathematisations of the cycle and metaphors also empirical data and facts. So, the main thesis of this chapter is that the context of discovery is the successful integration of those items that satisfy the criteria of adequacy. Because certain items are empirical data and facts, justification can be built-in. (shrink)

Starting with some illustrative examples, I develop a systematic account of a specific type of experimentation--an experimentation which is not, as in the "standard view", driven by specific theories. It is typically practiced in periods in which no theory or--even more fundamentally--no conceptual framework is readily available. I call it exploratory experimentation and I explicate its systematic guidelines. From the historical examples I argue furthermore that exploratory experimentation may have an immense, but hitherto widely neglected, epistemic significance.

In this paper I demonstrate that, contrary to the standard interpretations, William Whewell's view of scientific method is neither that of the hypothetico-deductivist nor that of the retroductivist. Rather, he offers a unique inductive methodology, which he calls "discoverers' induction." After explicating this methodology, I show that Kepler's discovery of his first law of planetary motion conforms to it, as Whewell claims it does. In explaining Whewell's famous phrase about "happy guesses" in science, I suggest that Whewell intended a distinction (...) between "inductions," which can be empirically verified, and "mere hypotheses"--or guesses--which cannot. Finally, I argue that Whewell's discoverers' induction is a view worthy of our attention today, because it avoids a number of problems faced by prominent alternative methodologies. (shrink)

This paper offers an account of data manipulation in scientific experiments. It will be shown that in many cases raw, unprocessed data is not produced, but rather a form of processed data that will be referred to as a data model. The language of data models will be used to provide a framework within which to understand a recent debate about the status of data and data manipulation. It will be seen that a description in terms of data models allows (...) one to understand cases in which data acquisition and data manipulation cannot be separated into two independent activities. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

I propose a framework that explicates and distinguishes the epistemic roles of data and models within empirical inquiry through consideration of their use in scientific practice. After arguing that Suppes’ characterization of data models falls short in this respect, I discuss a case of data processing within exploratory research in plant phenotyping and use it to highlight the difference between practices aimed to make data usable as evidence and practices aimed to use data to represent a specific phenomenon. I then (...) argue that whether a set of objects functions as data or models does not depend on intrinsic differences in their physical properties, level of abstraction or the degree of human intervention involved in generating them, but rather on their distinctive roles towards identifying and characterizing the targets of investigation. The paper thus proposes a characterization of data models that builds on Suppes’ attention to data practices, without however needing to posit a fixed hierarchy of data and models or a highly exclusionary definition of data models as statistical constructs. (shrink)

William Whewell’s philosophy of scientific discovery is applied to the problem of understanding the nature of unification and explanation by the composition of causes in Newtonian mechanics. The essay attempts to demonstrate: the sense in which ”approximate’ laws successfully refer to real physical systems rather than to idealizations of them; why good theoretical constructs are not badly underdetermined by observation; and why, in particular, Newtonian forces are not conventional and how empiricist arguments against the existence of component causes, and against (...) the veracity of the fundamental laws, are flawed. (shrink)

In 1833 John Herschel published a graphical method for determining the orbits of double stars. He argued that his method, which depended on human judgment rather than mathematical analysis, gave better results than computation, given the uncertainty in the data. Herschel found that astronomy and terrestrial physics were especially suitable for graphical treatment, and he expected that graphs would soon become important in all areas of science. He argued with William Whewell and James D. Forbes over the process of induction, (...) over the application of probability, and over the moral content of science. Graphs entered into all these debates; but because they constituted a method, not a metaphysics, they were acceptable to most practicing scientists and became increasingly popular throughout the nineteenth century. (shrink)

In his paper “William Whewell on the Consilience of Inductions” Professor Laudan has suggested that Whewell’s use of “consilience of inductions” is not the same as mine in my paper of that title. Suppose we have a theory T which entails three empirical laws L1, L2, L3. L1 is supposed already confirmed by direct evidence of its instances, but we have as yet no direct evidence for L2 or for L3. Then Laudan distinguishes two problems: Whewell’s problem: T is suggested (...) to explain L1 and is supported by L1; how much is our confidence in T increased if it also predicts L2, and L2 is subsequently directly confirmed? My problem: T is suggested to explain L1 and is supported by L1; T also entails L3; how much is our confidence that L3 will turn out a correct prediction increased by the fact that T entails it? (shrink)

Michael Weisberg has given us a lovely book on models. It has very broad coverage of issues intersecting the nature of models and their use, an extensive consideration of long ignored “concrete” models with a rich case study, a discussion and classification of the many diverse kinds of models, and a particularly groundbreaking and innovative discussion of similarity concerning how models relate to the world. Included are insightful discussions of increasingly used “agent based” models, and the conjoint use of multiple (...) models in looking for robust results. Weisberg fills in some discussions with modeling and simulation results of his own.In addition, he considers and critiques other recent competing views on models, including the increasingly popular “models as fictions” account. His discussion is clear and technically precise without being needlessly so, and his distinctions are useful. The scientist is more likely to recognize his objects than the semantic theorist, and that is how it .. (shrink)

In his paper “William Whewell on the Consilience of Inductions” Professor Laudan has suggested that Whewell’s use of “consilience of inductions” is not the same as mine in my paper of that title. Suppose we have a theory T which entails three empirical laws L1, L2, L3. L1 is supposed already confirmed by direct evidence of its instances, but we have as yet no direct evidence for L2 or for L3. Then Laudan distinguishes two problems: Whewell’s problem: T is suggested (...) to explain L1 and is supported by L1; how much is our confidence in T increased if it also predicts L2, and L2 is subsequently directly confirmed? My problem: T is suggested to explain L1 and is supported by L1; T also entails L3; how much is our confidence that L3 will turn out a correct prediction increased by the fact that T entails it? (shrink)

The paper attempts to elucidate and evaluate William Whewell's notion of a "consilience of inductions." In section I Whewellian consilience is defined and shown to differ considerably from what latter-day writers talk about when they use the term. In section II a primary analysis of consilience is shown to yield two types of consilient processes, one in which one of the lower-level laws undergoes a conceptual change (the case aptly discussed in Butts [1977]), and one in which the explanatory theory (...) undergoes conceptual "stretching." In section III both consilient cases are compared to the non-consilient case in reference to L. J. Cohen's method of relevant variables. In section IV we examine the test procedures of the theory in all three cases, and it is shown that in the event of genuine consilience (consilience of the second type) a theory acquires extraordinarily high support. In the final section something is said of the short-comings of standard Bayesian confirmation theories that are highlighted by Whewellian consilience. (shrink)