Induction

This book proposes a novel position in the debate on scientific realism: Modal Empiricism. Modal empiricism is the view that the aim of science is to provide theories that correctly delimit, in a unified way, the range of experiences that are naturally possible given our position in the world. The view is associated with a pragmatic account of scientific representation and an original notion of situated modalities, together with an inductive epistemology for modalities. It purports to provide a faithful account (...) of scientific practice and of its impressive achievements, and defuses the main motivations for scientific realism. More generally, Modal Empiricism purports to be the precise articulation of a pragmatist stance towards science. This book is of interest to any philosopher involved in the debate on scientific realism, or interested in how to properly understand the content, aim and achievements of science. (shrink)

This book seeks to offer original answers to all the major open questions in epistemology—as indicated by the book’s title. These questions and answers arise organically in the course of a validation of the entire corpus of human knowledge. The book explains how we know what we know, and how well we know it. The author presents a positive theory, motivated and directed at every step not by a need to reply to skeptics or subjectivists, but by the need of (...) a rational individual to know the world. -/- The author draws heavily from Ayn Rand’s theory of concepts as presented in her book, Introduction to Objectivist Epistemology, but departs from her epistemology—especially as explicated by leading exponents of her philosophy—in important ways. Areas of departure include the perception of entities and the role of probability theory in epistemology. -/- A main idea in the book is to begin the validation of the law of causality by identifying that existence causes our consciousness of it. Another key idea is this: The very fact that we perceive entities is the most basic and most extensive evidence that causal forces and causal interactions are regular. -/- Applying Ayn Rand’s principle that characteristics are ranges of measurement, the book offers a philosophical validation of “nonparametric predictive inference,” in turn providing an objective basis for prior probabilities in Bayesian analysis. -/- This book is primarily for a specialized readership—familiar with epistemological ideas and with Ayn Rand’s theory of concepts in particular. The last third of the book uses probability theory and mathematics somewhat beyond basic calculus, but less mathematical explanations are also included to make the general ideas accessible to lay readers. (shrink)

A number of philosophers have recently proposed several alleged cases of “knowledge from falsehood,” i.e., cases of inferential knowledge epistemised by an inference with a false crucial premise. This paper examines such cases and argues against interpreting them as cases of knowledge from falsehood. Specifically, I argue that the inferences in play in such cases are in no position to epistemise their conclusions.

Transcranial magnetic stimulation is used to make inferences about relationships between brain areas and their functions because, in contrast to neuroimaging tools, it modulates neuronal activity. The central aim of this article is to critically evaluate to what extent it is possible to draw causal inferences from repetitive TMS data. To that end, we describe the logical limitations of inferences based on rTMS experiments. The presented analysis suggests that rTMS alone does not provide the sort of premises that are sufficient (...) to warrant strong inferences about the direct causal properties of targeted brain structures. Overcoming these limitations demands a close look at the designs of rTMS studies, especially the methodological and theoretical conditions which are necessary for the functional decomposition of the relations between brain areas and cognitive functions. The main points of this article are that TMS-based inferences are limited in that stimulation-related causal effects are not equivalent to structure-related causal effects due to TMS side effects, the electric field distribution, and the sensitivity of neuroimaging and behavioral methods in detecting structure-related effects and disentangling them from confounds. Moreover, the postulated causal effects can be based on indirect effects. A few suggestions on how to manage some of these limitations are presented. We discuss the benefits of combining rTMS with neuroimaging in experimental reasoning and we address the restrictions and requirements of rTMS control conditions. The use of neuroimaging and control conditions allows stronger inferences to be gained, but the strength of the inferences that can be drawn depends on the individual experiment’s designs. Moreover, in some cases, TMS might not be an appropriate method of answering causality-related questions or the hypotheses have to account for the limitations of this technique. We hope this summary and formalization of the reasoning behind rTMS research can be of use not only for scientists and clinicians who intend to interpret rTMS results causally but also for philosophers interested in causal inferences based on brain stimulation research. (shrink)

En el presente trabajo expongo la propuesta falsacionista de Karl Popper como resultado de su solución al problema de la inducción. En este sentido, la analizo bajo sus dos aspectos, el lógico y el metodológico. La idea detrás de ello es mostrar, en primer lugar, que su solución lógica al problema de la inducción es totalmente independiente de los criterios metodológicos que propone para la elección entre teorías rivales, y en segundo lugar, que estos últimos constituyen una transgresión a su (...) pretendida exclusión de la inducción en la práctica científica. (shrink)

Scientific reasoning represents complex argumentation patterns that eventually lead to scientific discoveries. Social epistemology of science provides a perspective on the scientific community as a whole and on its collective knowledge acquisition. Different techniques have been employed with the goal of maximization of scientific knowledge on the group level. These techniques include formal models and computer simulations of scientific reasoning and interaction. Still, these models have tested mainly abstract hypothetical scenarios. The present thesis instead presents data-driven approaches in social epistemology (...) of science. A data-driven approach requires data collection and curation for its further usage, which can include creating empirically calibrated models and simulations of scientific inquiry, performing statistical analyses, or employing data- mining techniques and other procedures. -/- We present and analyze in detail three co-authored research projects on which the thesis’ author was engaged during her PhD. The first project sought to identify optimal team composition in high energy physics laboratories using data-mining techniques. The results of this project are published in (Perović et al. 2016), and indicate that projects with smaller numbers of teams and team members outperform bigger ones. In the second project, we attempted to determine whether there is an epistemic saturation point in experimentation in high energy physics. The initial results from this project are published in (Sikimić et al. 2018). In the thesis, we expand on this topic by using computer simulations to test for biases that could induce scientists to invest in projects beyond their epistemic saturation point. Finally, in previous examples of data-driven analyses, citations are used as a measure of epistemic efficiency of projects in high energy physics. In order to additionally justify and analyze the usage of this parameter in their data-driven research, in the third project Perović & Sikimić (2019) analyzed and compared inductive patterns in experimental physics and biology with the reliability of citation records in these fields. They conclude that while citations are a relatively reliable measure of efficiency in high energy physics research, the same does not hold for the majority of research in experimental biology. -/- Additionally, contributions of the author that are for the first time published in this theses are: (a) an empirically calibrated model of scientific interaction of research groups in biology, (b) a case study of irregular argumentation patterns in some pathogen discoveries, and (c) an introductory discussion of the benefits and limitations of data- driven approaches to the social epistemology of science. Using computer simulations of an empirically calibrated model, we demonstrate that having several levels of hierarchy and division into smaller research sub-teams is epistemically beneficial for researchers in experimental biology. We also show that argumentation analysis in biology represents a good starting point for further data-driven analyses in the field. Finally, we conclude that a data-driven approach is informative and useful for science policy, but requires careful considerations about data collection, curation, and interpretation. (shrink)

Talk given at a memorial meeting for C. I. Lewis at Harvard University, April 23, 1964.

John Norton says that philosophers have been led astray for thousands of years by their attempt to treat induction formally. He is correct that such an attempt has caused no end of trouble, but he is wrong about the history. There is a rich tradition of non-formal induction. In fact, material theories of induction prevailed all through antiquity and from the Renaissance to the mid-1800s. Recovering these past systems would not only fill lacunae in Norton’s own theory but would highlight (...) areas where Norton has not freed himself from the straightjacket of formal induction as much as he might think. This essay begins that recovery. (shrink)

How induction was understood took a substantial turn during the Renaissance. At the beginning, induction was understood as it had been throughout the medieval period, as a kind of propositional inference that is stronger the more it approximates deduction. During the Renaissance, an older understanding, one prevalent in antiquity, was rediscovered and adopted. By this understanding, induction identifies defining characteristics using a process of comparing and contrasting. Important participants in the change were Jean Buridan, humanists such as Lorenzo Valla and (...) Rudolph Agricola, Paduan Aristotelians such as Agostino Nifo, Jacopo Zabarella, and members of the medical faculty, writers on philosophy of mind such as the Englishman John Case, writers of reasoning handbooks, and Francis Bacon. (shrink)

According to John D. Norton's Material Theory of Induction, all reasonable inductive inferences are justified in virtue of background knowledge about local uniformities in nature. These local uniformities indicate that our samples are likely to be representative of our target population in our inductions. However, a variety of critics have noted that there are many circumstances in which induction seems to be reasonable, yet such background knowledge is apparently absent. I call such an absence of circumstances ‘the frontiers of science', (...) where background scientific theories do not provide information about such local uniformities. I argue that the Material Theory of Induction can be reconciled with our intuitions in favour of these inductions. I adapt an attempted justification of induction in general, the Combinatoric Justification of Induction, into a more modest rationalisation at the less foundational level that the critics discuss. Subject to a number of conditions, we can extrapolate from large samples using our knowledge of facts about the minimum proportions of representative subsets of finite sets. I also discuss some of Norton's own criticisms of his theory and argue that he is overly pessimistic. I conclude that Norton's theory at least performs well at the frontiers of science. (shrink)

This paper presents and defends an argument that the continuum hypothesis is false, based on considerations about objective chance and an old theorem due to Banach and Kuratowski. More specifically, I argue that the probabilistic inductive methods standardly used in science presuppose that every proposition about the outcome of a chancy process has a certain chance between 0 and 1. I also argue in favour of the standard view that chances are countably additive. Since it is possible to randomly pick (...) out a point on a continuum, for instance using a roulette wheel or by flipping a countable infinity of fair coins, it follows, given the axioms of ZFC, that there are many different cardinalities between countable infinity and the cardinality of the continuum. (shrink)

Given plausible assumptions about the nature of evidence and undercutting defeat, many believe that the force of the evidential problem of evil depends on sceptical theism’s being false: if evil is...

This article compares and contrasts the reception of Comte’s positivism in the works of William Whewell, John Stuart Mill and Franz Brentano. It is argued that Whewell’s rejection of positivism derives from his endorsement of a constructivist account of the inductive sciences, while Mill and Brentano’s sympathies for positivism are connected to their endorsement of an empiricist account. The mandate of the article is to spell out the chief differences between these two rival accounts. In the last, conclusive section, Whewell’s (...) anti-positivist argument is briefly assessed, and rebutted. (shrink)

The environmental costs and energy constraints have become emerging issues for the future development of Machine Learning (ML) and Artificial Intelligence (AI). So far, the discussion on environmental impacts of ML/AI lacks a perspective reaching beyond quantitative measurements of the energy-related research costs. Building on the foundations laid down by Schwartz et al., 2019 in the GreenAI initiative, our argument considers two interlinked phenomena, the gratuitous generalisation capability and the future where ML/AI performs the majority of quantifiable inductive inferences. The (...) gratuitous generalisation capability refers to a discrepancy between the cognitive demands of a task to be accomplished and the performance (accuracy) of a used ML/AI model. If the latter exceeds the former because the model was optimised to achieve the best possible accuracy, it becomes inefficient and its operation harmful to the environment. The future dominated by the non-anthropic induction describes a use of ML/AI so all-pervasive that most of the inductive inferences become furnished by ML/AI generalisations. The paper argues that the present debate deserves an expansion connecting the environmental costs of research and ineffective ML/AI uses (the issue of gratuitous generalisation capability) with the (near) future marked by the all-pervasive Human-Artificial Intelligence Nexus. (shrink)

Most scientists would hold that science has not established that the cosmos is physically comprehensible – i.e. such that there is some as-yet undiscovered true physical theory of everything that is unified. This is an empirically untestable, or metaphysical thesis. It thus lies beyond the scope of science. Only when physics has formulated a testable unified theory of everything which has been amply corroborated empirically will science be in a position to declare that it has established that the cosmos is (...) physically comprehensible. But this argument presupposes a widely accepted but untenable conception of science which I shall call standard empiricism. According to standard empiricism, in science theories are accepted solely on the basis of evidence. Choice of theory may be influenced for a time by considerations of simplicity, unity, or explanatory capacity, but not in such a way that the universe itself is permanently assumed to be simple, unified or physically comprehensible. In science, no thesis about the universe can be accepted permanently as a part of scientific knowledge independently of evidence. Granted this view, it is clear that science cannot have established that the universe is physically comprehensible. Standard empiricism is, however, as I have indicated, untenable. Any fundamental physical theory, in order to be accepted as a part of theoretical scientific knowledge, must satisfy two criteria. It must be (1) sufficiently empirically successful, and (2) sufficiently unified. Given any accepted theory of physics, endlessly many empirically more successful disunified rivals can always be concocted – disunified because they assert that different dynamical laws govern the diverse phenomena to which the theory applies. These disunified rivals are not considered for a moment in physics, despite their greater empirical success. This persistent rejection of empirically more successful but disunified rival theories means, I argue, that a big, highly problematic, implicit assumption is made by science about the cosmos, to the effect, at least, that the cosmos is such that all seriously disunified theories are false. Once this point is recognized, it becomes clear, I argue, that we need a new conception of science which makes explicit, and so criticizable and improvable the big, influential, and problematic assumption that is at present implicit in physics in the persistent preference for unified theories. This conception of science, which I call aim-oriented empiricism, represents the assumption of physics in the form of a hierarchy of assumptions. As one goes up the hierarchy, the assumptions become less and less substantial, and more and more nearly such that their truth is required for science, or the pursuit of knowledge, to be possible at all. At each level, that assumption is accepted which (a) best accords with the next one up, and (b) has, associated with it the most empirically progressive research programme in physics, or holds out the greatest hope of leading to such an empirically progressive research programme. In this way a framework of relatively insubstantial, unproblematic, fixed assumptions and associated methods is created, high up in the hierarchy, within which much more substantial and problematic assumptions and associated methods, low down in the hierarchy, can be changed, and indeed improved, as scientific knowledge improves. One assumption in this hierarchy of assumptions, I argue, is that the cosmos is physically comprehensible – that is, such that some yet-to-be-discovered unified theory of everything is true. Hence the conclusion: improve our ideas about the nature of science and it becomes apparent that science has already established that the cosmos is physically comprehensible – in so far as science can ever establish anything theoretical. (shrink)

Doubting that any version of induction could be satisfactory as the basic principle of nondemonstrative inference the author analyzes elaborately and rejects eliminative and enumerative induction in the search for a satisfactory criterion of confirmation. The basic difficulty is that induction fails to provide a means of confirming hypotheses about unobserved things. He inclines toward a method of hypothesis but rejects previous formulations in favor of one that involves systems of confirmable hypotheses that are competetively chosen as the simplest available.

A complete calculus of inductive inference captures the totality of facts about inductive support within some domain of propositions as relations or theorems within the calculus. It is demonstrated that there can be no complete, non-trivial calculus of inductive inference. 1Introduction 2The Deductive Structure 2.1Finite Boolean algebras of propositions 2.2Symmetries of the Boolean algebra 3Deductively Definable Logics of Induction: The Formal Expression of Completeness 3.1Strength of inductive support 3.2Explicit definition 3.3Implicit definition 4The Symmetry Theorem 4.1An illustration 4.2The general case 5Asymptotic (...) Stability 5.1Illustrations 5.2The general condition 6The No-Go Result 6.1Illustration: the principle of indifference 6.2The result 7Incompleteness 8Unsuccessful Escapes 8.1Enriching the deductive logic 8.2Enrich the inductive logic 8.3Preferred refinements and preferred languages 8.4The subjective turn 9Conclusions Appendices. (shrink)

A crucial aspect of scientific realism is what do we mean by true. In Luk’s theory and model of scientific study, a theory can be believed to be “true” but a model is only accurate. Therefore, what do we mean by a “true” theory in scientific realism? Here, we focus on exploring the notion of truth by some thought experiments and we come up with the idea that truth is related to what we mean by the same. This has repercussion (...) to the repeatability of the experiments and the predictive power of scientific knowledge. Apart from sameness, we also found that truth is related to the granularity of the observation, the limit of detection, the distinguishability of the objects in theory, the simultaneous measurements of objects/processes, the consistencies of the theory and the one-to-one correspondence between terms/events and objects/processes, respectively. While there is no guarantee that we can arrive at the final “true” theory, we have a process/procedure with more and more experiments together with our own ingenuity, to direct us towards such a “true” theory. For quantum mechanics, since a particle is also regarded as a wave, quantum mechanics cannot be considered as a true theory based on the correspondence theory of truth. Failing this, truth may be defined by the coherence theory of truth which is similar to the coherence of beliefs. However, quantum mechanics may not be believed to be a true theory based on the coherence theory of truth because wave properties and particle properties may contradict. Further research is needed to address this problem if we want to regard quantum mechanics as a “true” theory. (shrink)

Unconscious logical inference seems to rely on the syntactic structures of mental representations (Quilty-Dunn & Mandelbaum 2018). Other transitions, such as transitions using iconic representations and associative transitions, are harder to assimilate to syntax-based theories. Here we tackle these difficulties head on in the interest of a fuller taxonomy of mental transitions. Along the way we discuss how icons can be compositional without having constituent structure, and expand and defend the “symmetry condition” on Associationism (the idea that associative links and (...) transitions are perfectly symmetric). In the end, we show how a BIT (“bare inferential transition”) theory can cohabitate with these other non-inferential mental transitions. (shrink)

I examine and resolve an exegetical dichotomy between two main interpretations of Peirce’s theory of abduction, namely, the Generative Interpretation and the Pursuitworthiness Interpretation. According to the former, abduction is the instinctive process of generating explanatory hypotheses through a mental faculty called insight. According to the latter, abduction is a rule-governed procedure for determining the relative pursuitworthiness of available hypotheses and adopting the worthiest one for further investigation—such as empirical tests—based on economic considerations. It is shown that the Generative Interpretation (...) is inconsistent with a fundamental fact of logic for Peirce—i.e., abduction is a kind of inference—and the Pursuitworthiness Interpretation is flawed and inconsistent with Peirce’s naturalistic explanation for the possibility of science and his view about the limitations of classical scientific method. Changing the exegetical locus classicus from the logical form of abduction to insight and economy of research, I argue for the Unified Interpretation according to which abduction includes both instinctive hypotheses-generation and rule-governed hypotheses-ranking. I show that the Unified Interpretation is immune to the objections raised successfully against the Generative and the Pursuitworthiness interpretations. (shrink)

Bayesian confirmation theory is a leading theory to decide the confirmation/refutation of a hypothesis based on probability calculus. While it may be much discussed in philosophy of science, is it actually practiced in terms of hypothesis testing by scientists? Since the assignment of some of the probabilities in the theory is open to debate and the risk of making the wrong decision is unknown, many scientists do not use the theory in hypothesis testing. Instead, they use alternative statistical tests that (...) can measure the risk or the reliability in decision making, circumventing some of the theoretical problems in practice. Therefore, the theory is not very popular in hypothesis testing among scientists at present. However, there are some proponents of Bayesian hypothesis testing, and software packages are made available to accelerate utilization by scientists. Time will tell whether Bayesian confirmation theory can become both a leading theory and a widely practiced method. In addition, this theory can be used to model the belief of scientists when testing hypotheses. (shrink)

Epistemological naturalists reject the demand for a priori justification of empirical knowledge; no such thing is possible. Observation reports, being the foundation of empirical knowledge, are neither justified by other sentences, nor certain; but they may be agreed upon as starting points for inductive reasoning and they function as implicit definitions of predicates used. Making inductive generalisations from observations is a basic habit among humans. We do that without justification, but we have strong intuitions that some inductive generalisations will fail, (...) while for some other we have better hopes. Why? This is the induction problem according to Goodman. He suggested that some predicates are projectible when becoming entrenched in language. This is a step forward, but not entirely correct. Inductions result in universally generalised conditionals and these contain two predicates, one in the antecedent, one in the consequent. Counterexamples to preliminary inductive generalisations can be dismissed by refining the criteria of application for these predicates. This process can be repeated until the criteria for application of the predicate in the antecedent includes the criteria for the predicate in the consequent, in which case no further counterexample is possible. If that is the case we have arrived at a law. Such laws are implicit definitions of theoretical predicates. An accidental generalisation has not this feature, its predicates are unrelated. Laws are said to be necessary, which may be interpreted as ‘“Laws” are necessarily true’. ‘Necessarily true’ is thus a semantic predicate, not a modal operator. In addition, laws, being definitions, are necessarily true in the sense of being necessary assumptions for further use of the predicates implicitly defined by such laws. Induction, when used in science, is thus our way of inventing useful scientific predicates; it is a heuristic, not an inference principle. (shrink)

Historical inductions, viz., the pessimistic meta-induction and the problem of unconceived alternatives, are critically analyzed via John D. Norton’s material theory of induction and subsequently rejected as non-cogent arguments. It is suggested that the material theory is amenable to a local version of the pessimistic meta-induction, e.g., in the context of some medical studies.

John D. Norton’s “Material Theory of Induction” has been one of the most intriguing recent additions to the philosophy of induction. Norton’s account appears to be a notably natural account of actual inductive practices, although his theory has attracted considerable criticism. I detail several novel issues for his theory but argue that supplementing the Material Theory with a theory of direct inference could address these problems. I argue that if this combination is possible, a stronger theory of inductive reasoning emerges, (...) which has a more propitious answer to the Problem of Induction. (shrink)

›Bottom-up‹ und ›top-down‹ sind heutzutage gängige Methodenbezeichnungen in allen Bereichen der Wissenschaft. Dennoch sind beide Methoden keine Entdeckung der Moderne, sondern wurden unter Begriffen wie beispielsweise ›Auf-‹ und ›Abstieg‹, ›Induktion‹ und ›Deduktion‹ in der Wissenschaftsgeschichte häufig verwendet, um komplexe Wissensbestände vollständig aufzuarbeiten und zu strukturieren. Paradigmatisch für eine derartige Aufarbeitung stehen die mittelalterliche Summa und das neuzeitliche System. Aktuellen Studien zufolge hat aber bereits Dionysius Areopagita in der Spätantike eine derartige Summe verfasst, während in der Neuzeit erst J. G. Fichtes (...) Wissenschaftslehre (1804) allen Systemansprüchen genügen konnte. Daher beschäftigt sich die vorliegende Studie mit den Auf- und Abstiegsmethoden bei Dionysius und Fichte, ihrer antiken Vorgeschichte und kontrastiert ihre Methoden mit der aktuellen Wissenschaftsphilosophie. Dabei zeigt sich, dass beide Autoren bei der Aufstiegs- bzw. bottom-up-Methode bislang unberücksichtigte Strategien zur Lösung des Induktionsproblems anwenden. (shrink)

Newton’s Regulae philosophandi—the rules for reasoning in natural philosophy—are maxims of causal reasoning and induction. This essay reviews their significance for Newton’s method of inquiry, as well as their application to particular propositions within the Principia. Two main claims emerge. First, the rules are not only interrelated, they defend various facets of the same core idea: that nature is simple and orderly by divine decree, and that, consequently, human beings can be justified in inferring universal causes from limited phenomena, if (...) only fallibly. Second, the rules make substantive ontological assumptions on which Newton’s argument in the Principia relies. (shrink)

William Whewell’s account of induction differs dramatically from the one familiar from twentieth-century debates. I argue that Whewell’s induction can be usefully understood by comparing the difference between his views and more standard accounts to contemporary debates between semantic and syntactic views of theories: rather than understanding inductive inference as capturing a relationship between sentences or propositions, Whewell understands it as a method for constructing a model of the world. The difference between this view and the more familiar picture of (...) induction is reflected in other aspects of Whewell’s philosophy of science, particularly his treatment of consilience and the order of discovery. (shrink)

Review Essay of ‘Isaac Newton’s Scientific Method’ by William L. Harper.

Francis Bacon’s method of induction is often understood as a form of eliminative induction. The idea, on this interpretation, is to list the possible formal causes of a phenomenon and, by reference to a copious and reliable natural history, to falsify all of them but one. Whatever remains must be the formal cause. Bacon’s crucial instances are often seen as the crowning example of this method. In this article, I argue that this interpretation of crucial instances is mistaken, and it (...) has caused us to lose sight of why Bacon assigns crucial instances a special role in his quest for epistemic certainty about formal causes. If crucial instances are interpreted eliminatively, then they are subject to the two problems related to underdetermination raised by Duhem: (1) that it is impossible to be certain one has specified all of the possible alternatives and (2) that an experiment falsifies a whole theory, not just a single hypothesis in isolation. I show that Bacon anticipates and aims to dodge both of these problems by conceiving of crucial instances as working, in the ideal case, through direct affirmations that are supported by links to more foundational knowledge. (shrink)

This paper studies the use of hypotheses schemes in generating inductive predictions. After discussing Carnap-Hintikka inductive logic, hypotheses schemes are defined and illustrated with two partitions. One partition results in the Carnapian continuum of inductive methods, the other results in predictions typical for hasty generalization. Following these examples I argue that choosing a partition comes down to making inductive assumptions on patterns in the data, and that by choosing appropriately any inductive assumption can be made. Further considerations on partitions make (...) clear that they do not suggest any solution to the problem of induction. Hypotheses schemes provide the tools for making inductive assumptions, but they also reveal the need for such assumptions. (shrink)

Peter Lipton argues that inference to the best explanation involves the selection of a hypothesis on the basis of its loveliness. I argue that in optimal cases of IBE we may be able to eliminate all but one of the hypotheses. In such cases we have a form of eliminative induction takes place, which I call ‘Holmesian inference’. I argue that Lipton’s example in which Ignaz Semmelweis identified a cause of puerperal fever better illustrates Holmesian inference than Liptonian IBE. I (...) consider in detail the conditions under which Holmesian inference is possible and conclude by considering the epistemological relations between Holmesian inference and Liptonian IBE.Keywords: Inference to the best explanation; Peter Lipton; Abduction; Holmesian inference; Eliminative induction. (shrink)

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)