I argue that Goodman's puzzle of grue at least poses no real challenge about inductive inference. By drawing on Stove's characterisation of Hume's characterisation of inductive inference, we see that the premises in an inductive inference report experienced impressions; and Goodman can be interpreted as posing a real challenge about inductive inference only if we treat an epistemic subject's observations more as logical contents and less as experienced impressions. So, even though the grue puzzle was effective against its stated logicist (...) targets, it is not thereby an enduring difficulty regarding experience's ability to impart epistemic justification via inductive evidence. (shrink)

Philosophers talk routinely of 'Hume's problem of induction'. But the usual accompanying exegesis is mistaken in a way that has led epistemologists to conceive of 'Hume's problem' in needlessly narrow terms. They have overlooked a way of articulating the conceptual problem, along with a potential way of solving it. Indeed, they have overlooked Hume's own way. In explaining this, I will supplement Hume's insights by adapting Ryle's thinking on knowledge-how and knowledge-that. We will also see why (...) Hume's 'sceptical solution' was a perfectly appropriate response to his 'sceptical argument' -- rather than (as is often thought by analytic epistemologists) a merely descriptive response patently missing the normative sceptical point so strikingly formulated by Hume. (shrink)

We present six significant open problems in Pure Inductive Logic, together with their background and current status, with the intention of raising awareness and leading ultimately to their resolution.

A cornerstone of game theory is backward induction, whereby players reason backward from the end of a game in extensive form to the beginning in order to determine what choices are rational at each stage of play. Truels, or three-person duels, are used to illustrate how the outcome can depend on (1) the evenness/oddness of the number of rounds (the parity problem) and (2) uncertainty about the endpoint of the game (the uncertainty problem). Since there is no (...) known endpoint in the latter case, an extension of the idea of backward induction is used to determine the possible outcomes. The parity problem highlights the lack of robustness of backward induction, but it poses no conflict between foundational principles. On the other hand, two conflicting views of the future underlie the uncertainty problem, depending on whether the number of rounds is bounded (the players invariably shoot from the start) or unbounded (they may all cooperate and never shoot, despite the fact that the truel will end with certainty and therefore be effectively bounded). Some real-life examples, in which destructive behavior sometimes occurred and sometimes did not, are used to illustrate these differences, and some ethical implications of the analysis are discussed. (shrink)

In this paper I adduce a new argument in support of the claim that IBE is an autonomous form of inference, based on a familiar, yet surprisingly, under-discussed, problem for Hume’s theory of induction. I then use some insights thereby gleaned to argue for the claim that induction is really IBE, and draw some normative conclusions.

The argument from inductive risk is considered to be one of the strongest challenges for value-free science. A great part of its appeal lies in the idea that even an ideal epistemic agent—the “perfect scientist” or “scientist qua scientist”—cannot escape inductive risk. In this paper, I scrutinize this ambition by stipulating an idealized Bayesian decision setting. I argue that inductive risk does not show that the “perfect scientist” must, descriptively speaking, make non-epistemic value-judgements, at least not in a way that (...) undermines the value-free ideal. However, the argument is more successful in showing that there are cases where the “perfect scientist” should, normatively speaking, use non-epistemic values. I also show that this is possible without creating problems of illegitimate prescription and wishful thinking. Thus, while inductive risk does not refute value-freedom completely, it still represents a powerful critique of value-free science. (shrink)

Metaphysical problems may be solved by the methods of inference employed in the empirical sciences. So we are told by many realists and pragmatists, among whom may be mentioned Professors J. B. Pratt, William Savery, and Donald Williams. Mr. Williams and Mr. Pratt have argued for the use of inductive methods in establishing the existence of an external world. Mr. Savery has asserted that all philosophical inference as to matter of fact is inductive. This naturalistic attitude is by no means (...) unusual—in fact it is so common in this age in which the speculations of scientists as well as their achievements have captivated the popular imagination, that even philosophers have for the most part failed to examine its validity and presuppositions. It is the purpose of this paper to present such an examination, not of the philosophic employment of inductive methods in general, but in regard to one specific problem; namely, that of the existence of an external world. What I purpose then is to present and evaluate the view that the existence of an external world may be established by inductive methods as employed in the empirical sciences. (shrink)

Inductive methods can be used to estimate the accuracies of inductive methods. Call a method immodest if it estimates that it is at least as accurate as any of its rivals. It would be unreasonable to adopt any but an immodest method. Under certain assumptions, exactly one of Carnap's lambda-methods is immodest. This may seem to solve the problem of choosing among the lambda-methods; but sometimes the immodest lambda-method is λ =0, which it would not be reasonable to adopt. (...) We should therefore reconsider the assumptions that led to this conclusion: for instance, the measure of accuracy. (shrink)

Historical inductions, that is, the pessimistic metainduction and the problem of unconceived alternatives, are critically analyzed via John D. Norton’s material theory of induction and subsequently rejected as noncogent arguments. It is suggested that the material theory is amenable to a local version of the pessimistic metainduction, for example, in the context of some medical studies.

Inductive thinking is a universal human habit; we generalise from our experiences the best we can. The induction problem is to identify which observed regularities provide reasonable justification for inductive conclusions. In the natural sciences, we can often use strict laws in making successful inferences about unobserved states of affairs. In the social sciences, by contrast, we have no strict laws, only regularities which most often are conditioned on ceteris paribus clauses. This makes it much more difficult to (...) make reliable inferences in the social sciences. In particular, we want knowledge about general causal relations in order to be able to determine what to do in order to achieve a certain state of affairs. Knowledge about causal relations that are also valid in the future requires experiments or so called ‘natural experiments’. Only knowledge derived from such experiences enable us to draw reasonably reliable inferences about how to act in order to achieve our goals. (shrink)

In ‘Induction and Natural Kinds’, I proposed a solution to the problem of induction according to which our use of inductive inference is reliable because it is grounded in the natural kind structure of the world. When we infer that unobserved members of a kind will have the same properties as observed members of the kind, we are right because all members of the kind possess the same essential properties. The claim that the existence of natural kinds (...) is what grounds reliable use of induction is based on an inference to the best explanation of the success of our inductive practices. As such, the argument for the existence of natural kinds employs a form of ampliative inference. But induction is likewise a form of ampliative inference. Given both of these facts, my account of the reliability of induction is subject to the objection that it provides a circular justification of induction, since it employs an ampliative inference to justify an ampliative inference. In this paper, I respond to the objection of circularity by arguing that what justifies induction is not the inference to the best explanation of its reliability. The ground of induction is the natural kinds themselves. (shrink)

The local justification of beliefs and hypotheses has recently become a major concern for epistemologists and philosophers of induction. As such, the problem of local justification is not entirely new. Most pragmatists had addressed themselves to it, and so did, to some extent, many classical inductivists in the Bacon-Whewell-Mill tradition. In the last few decades, however, the use of logic and semantics, probability calculus, statistical methods, and decision-theoretic concepts in the reconstruction of in ductive inference has revealed some (...) important technical respects in which inductive justification can be local: the choice of a language, with its syntactic and semantic features, the relativity of probabilistic evalua tions to an initial body of evidence or background knowledge and to an agent's utilities and preferences, etc. Some paradoxes and difficulties encountered by purely formal accounts of inductive justification, the erosion of the once dominant empiricist position, which most approaches to induction took for granted, and the increasing challenge of noninduc tivist epistemolgies have underscored the need of accounting for the methodological problems of applying inductive logic to real life contexts, particularly in science. As a result, in the late fifties and sixties, several related developments pointed to a new, local approach to inductive justification. (shrink)

In this chapter we will deal with “mechanizing” induction, i.e. with ways in which theoretical computer science approaches inductive generalization. In the field of Machine Learning, algorithms for induction are developed. Depending on the form of the available data, the nature of these algorithms may be very different. Some of them combine geometric and statistical ideas, while others use classical reasoning based on logical formalism. However, we are not so much interested in the algorithms themselves, but more on (...) the philosophical and theoretical foundations they share. Thus in the first of two parts, we will examine different approaches and inductive assumptions in two particular learning settings. While many machine learning algorithms work well on a lot of tasks, the interpretation of the learned hypothesis is often difficult. Thus, while e.g. an algorithm surprisingly is able to determine the gender of the author of a given text with about 80 percent accuracy [Argamon and Shimoni, 2003], for a human it takes some extra effort to understand on the basis of which criteria the algorithm is able to do so. With that respect the advantage of approaches using logic are obvious: If the output hypothesis is a formula of predicate logic, it is easy to interpret. However, if decision trees or algorithms from the area of inductive logic programming are based purely on classical logic, they suffer from the fact that most universal statements do not hold for exceptional cases, and classical logic does not offer any convenient way of representing statements which are meant to hold in the “normal case”. Thus, in the second part we will focus on approaches for Nonmonotonic Reasoning that try to handle this problem. Both Machine Learning and Nonmonotonic Reasoning have been anticipated partially by work in philosophy of science and philosophical logic. At the same time, recent developments in theoretical computer science are expected to trig- ger further progress in philosophical theories of inference, confirmation, theory revision, learning, and the semantic and pragmatics of conditionals. We hope this survey will contribute to this kind of progress by building bridges between computational, logical, and philosophical accounts of induction. (shrink)

This study investigated the cognitive processes involved in inductive reasoning. Sixteen undergraduates solved quadratic function–finding problems and provided concurrent verbal protocols. Three fundamental areas of inductive activity were identified: Data Gathering, Pattern Finding, and Hypothesis Generation. These activities are evident in three different strategies that they used to successfully find functions. In all three strategies, Pattern Finding played a critical role not previously identified in the literature. In the most common strategy, called the Pursuit strategy, participants created new quantities from (...) x and y, detected patterns in these quantities, and expressed these patterns in terms of x. These expressions were then built into full hypotheses. The processes involved in this strategy are instantiated in an ACT‐based model that simulates both successful and unsuccessful performance. The protocols and the model suggest that numerical knowledge is essential to the detection of patterns and, therefore, to higher‐order problem solving. (shrink)

The current state of inductive logic is puzzling. Survey presentations are recurrently offered and a very rich and extensive handbook was entirely dedicated to the topic just a few years ago [23]. Among the contributions to this very volume, however, one finds forceful arguments to the effect that inductive logic is not needed and that the belief in its existence is itself a misguided illusion , while other distinguished observers have eventually come to see at least the label as “slightly (...) antiquated” .What seems not to have lost any of its currency is the problem which inductive logic is meant to address. Inference from limited ascertained information to uncertain hypotheses is ubiquitous in learning, prediction and discovery. The logical insight that such kind of inference is fallible m .. (shrink)

I want to examine a possible solution to the problem of induction-one which, as far as I know, has not been discussed elsewhere. The solution makes crucial use of the notion of objective natural necessity. For the purposes of this discussion, I shall assume that this notion is coherent. I am aware that this assumption is controversial, but I do not have space to examine the issue here.

Under what conditions does machine learning (ML) model opacity inhibit the possibility of explaining and understanding phenomena? In this article, I argue that nonepistemic values give shape to the ML opacity problem even if we keep researcher interests fixed. Treating ML models as an instance of doing model-based science to explain and understand phenomena reveals that there is (i) an external opacity problem, where the presence of inductive risk imposes higher standards on externally validating models, and (ii) an (...) internal opacity problem, where greater inductive risk demands a higher level of transparency regarding the inferences the model makes. (shrink)

The paper sketches an ontological solution to an epistemological problem in the philosophy of science. Taking the work of Hilary Kornblith and Brian Ellis as a point of departure, it presents a realist solution to the Humean problem of induction, which is based on a scientific essentialist interpretation of the principle of the uniformity of nature. More specifically, it is argued that use of inductive inference in science is rationally justified because of the existence of real, natural (...) kinds of things, which are characterized as such by the essential properties which all members of a kind necessarily possess in common. The proposed response to inductive scepticism combines the insights of epistemic naturalism with a metaphysical outlook that is due to s cientific realism. (shrink)

Safety accounts of knowledge claim, roughly, that knowledge that p requires that one's belief that p could not have easily been false. Such accounts have been very popular in recent epistemology. However, one serious problem safety accounts have to confront is to explain why certain lottery‐related beliefs are not knowledge, without excluding obvious instances of inductive knowledge. We argue that the significance of this objection has hitherto been underappreciated by proponents of safety. We discuss Duncan Pritchard's recent solution to (...) the problem and argue that it fails. More importantly, the problem reaches deeper and poses a threat to any current safety accounts that require a belief's modal stability in close possibilities (as well as safety accounts that appeal to ‘normality’). We end by arguing that ways out of the problem require substantial reconstruction for a safety‐based account of knowledge. (shrink)

In a recent work, Popper claims to have solved the problem of induction. In this paper I argue that Popper fails both to solve the problem, and to formulate the problem properly. I argue, however, that there are aspects of Popper's approach which, when strengthened and developed, do provide a solution to at least an important part of the problem of induction, along somewhat Popperian lines. This proposed solution requires, and leads to, a new (...) theory of the role of simplicity in science, which may have helpful implications for science itself, thus actually stimulating scientific progress. (shrink)

Aristotle said that induction (epagōgē) is a proceeding from particulars to a universal, and the definition has been conventional ever since. But there is an ambiguity here. Induction in the Scholastic and the (so-called) Humean tradition has presumed that Aristotle meant going from particular statements to universal statements. But the alternate view, namely that Aristotle meant going from particular things to universal ideas, prevailed all through antiquity and then again from the time of Francis Bacon until the mid-nineteenth (...) century. Recent scholarship is so steeped in the first-mentioned tradition that we have virtually forgotten the other. In this essay McCaskey seeks to recover that alternate tradition, a tradition whose leading theoreticians were William Whewell, Francis Bacon, Socrates, and in fact Aristotle himself. The examination is both historical and philosophical. The first part of the essay fills out the history. The latter part examines the most mature of the philosophies in the Socratic tradition, specifically Bacon’s and Whewell’s. After tracing out this tradition, McCaskey shows how this alternate view of induction is indeed employed in science, as exemplified by several instances taken from actual scientific practice. In this manner, McCaskey proposes to us that the Humean problem of induction is merely an artifact of a bad conception of induction and that a return to the Socratic conception might be warranted. (shrink)

Inmaculada de Melo-Martín and Kristen Intemann consider whether, from the perspective of the argument from inductive risk, ethical and political values might be logically, epistemically, pragmatically, or ethically necessary in the “core” of scientific reasoning. In each case, they argue that there are significant conceptual problems. In this comment, employing regulatory uses of high-throughput toxicology at the US Environmental Protection Agency as a case study, I respond to some of their claims about the notion of “pragmatic necessity.” I conclude that, (...) while an inductive risk framework has some significant limitations, it is still conceptually and rhetorically valuable. (shrink)

The aim of this contribution is to provide a rather general answer to Hume’s problem. To this end, induction is treated within a straightforward formal paradigm, i.e., several connected levels of abstraction. Within this setting, many concrete models are discussed. On the one hand, models from mathematics, statistics and information science demonstrate how induction might succeed. On the other hand, standard examples from philosophy highlight fundamental difficulties. Thus it transpires that the difference between unbounded and bounded inductive (...) steps is crucial: while unbounded leaps of faith are never justified, there may well be reasonable bounded inductive steps. In this endeavour, the twin concepts of information and probability prove to be indispensable, pinning down the crucial arguments, and, at times, reducing them to calculations. Essentially, a precise study of boundedness settles Goodman’s challenge. Hume’s more profound claim of seemingly inevitable circularity is answered by obviously non-circular hierarchical structures. (shrink)

Arguably, Hume's greatest single contribution to contemporary philosophy of science has been the problem of induction (1739). Before attempting its statement, we need to spend a few words identifying the subject matter of this corner of epistemology. At a first pass, induction concerns ampliative inferences drawn on the basis of evidence (presumably, evidence acquired more or less directly from experience)—that is, inferences whose conclusions are not (validly) entailed by the premises. Philosophers have historically drawn further distinctions, often (...) appropriating the term “induction” to mark them; since we will not be concerned with the philosophical issues for which these distinctions are relevant, we will use the word “inductive” in a catch-all sense synonymous with “ampliative”. But we will follow the usual practice of choosing, as our paradigm example of inductive inferences, inferences about the future based on evidence drawn from the past and present. A further refinement is more important. Opinion typically comes in degrees, and this fact makes a great deal of difference to how we understand inductive inferences. For while it is often harmless to talk about the conclusions that can be rationally believed on the basis of some.. (shrink)

After decades of intense debate over the old pessimistic induction (Laudan, 1977; Putnam, 1978), it has now become clear that it has at least the following four problems. First, it overlooks the fact that present theories are more successful than past theories. Second, it commits the fallacy of biased statistics. Third, it erroneously groups together past theories from different fields of science. Four, it misses the fact that some theoretical components of past theories were preserved. I argue that these (...) four problems entitle us to construct what I call the grand pessimistic induction that since the old pessimistic induction has infinitely many hidden problems, the new pessimistic induction (Stanford, 2006) also has infinitely many hidden problems. (shrink)

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)

We present a method for integrating rippling-based rewriting into matrix-based theorem proving as a means for automating inductive specification proofs. The selection of connections in an inductive matrix proof is guided by symmetries between induction hypothesis and induction conclusion. Unification is extended by decision procedures and a rippling/reverse-rippling heuristic. Conditional substitutions are generated whenever a uniform substitution is impossible. We illustrate the integrated method by discussing several inductive proofs for the integer square root problem as well as (...) the algorithms extracted from these proofs. (shrink)

The inductive category formation framework, an influential set of theories of learning in psychology and artificial intelligence, is deeply flawed. In this framework a set of necessary and sufficient features is taken to define a category. Such definitions are not functionally justified, are not used by people, and are not inducible by a learning system. Inductive theories depend on having access to all and only relevant features, which is not only impossible but begs a key question in learning. The crucial (...) roles of other cognitive processes (such as explanation and credit assignment) are ignored or oversimplified. Learning necessarily involves pragmatic considerations that can only be handled by complex cognitive processes.We provide an alternative framework for learning according to which category definitions must be based on category function. The learning system invokes other cognitive processes to accomplish difficult tasks, makes inferences, analyses and decides among potential features, and specifies how and when categories are to be generated and modified. We also examine the methodological underpinnings of the two approaches and compare their motivations. (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)

RÉSUMÉ : Depuis l’ouvrage classique de Goodman, on sait que toute théorie de l’induction doit comporter une composante non formelle. Or, la liberté théorique offerte par le recours à un tel élément implique des responsabilités. Cet article propose comme desideratum d’une théorie de l’induction qu’elle rende compte de la dynamique de sa composante non formelle. Ce desideratum, qui est nouveau, n’est pas satisfait par les principales théories existantes de l’induction. L’identification de l’importance de la dynamique a pourtant (...) l’avantage de suggérer une voie vers une théorie plus acceptable et plus compléte de l’induction. ABSTRACT: Since Goodman’s classic monograph, it has been known that any reasonable theory of induction must involve non formal elements. However, the right to make theoretical use of such an element comes with responsibilities. This article argues for an apparently new desideratum for a theory of induction: namely, that it accounts for the dynamics of the non formal element used. None of the main existing theories of induction satisfies this desideratum. The recognition of the importance of dynamics does however have the advantage of suggesting where to look for a more acceptable and complete theory of induction. (shrink)

A large class of games is that of non-cooperative, extensive form games of perfect information. When the length of these games is finite, the method used to reach a solution is that of a backward induction. Working from the terminal nodes, dominated strategies are successively deleted and what remains is a unique equilibrium. Game theorists have generally assumed that the informational requirement needed to solve these games is that the players have common knowledge of rationality. This assumption, however, has (...) given rise to several problems and paradoxes. Most notably, it has been shown that the common knowledge assumption makes the theory of the game inconsistent at some information set. The present paper shows that a) no common knowledge of rationality need be assumed for the backward induction solution to hold. Rather, it is sufficient that the players have a number of levels of knowledge proportional to the length of the game, and b) it is also necessary that the number of levels of knowledge is finite and proportional to the length of the game. For a higher number of levels of knowledge, inconsistencies arise. (shrink)

Various forms of mathematical induction are applicable to domains with some kinds of order. This naturally leads to the questions about the possibility of unification of different inductions and their generalization to wider classes of ordered domains. In the paper we propose a common framework for formulating induction proof principles in various structures and apply it to partially ordered sets. In this framework we propose a fixed induction principle which is indirectly applicable to the class of all (...) posets. In a certain sense, this result provides a solution to the problem of formulating a common generalization of a number of well-known induction principles for discrete and continuous ordered structures. (shrink)

The fact that simplicity has been linked with induction by many philosophers of science, some of whom have proposed or supported criteria of “inductive simplicity,” means that the problem must be given some serious attention. I take “inductive simplicity” as a title, however, only by way of concession to these historical treatments, since it is precisely the burden of my paper to show that there is no such thing. So much for the conclusion. I shall spend the remainder (...) of my time arguing for it. (shrink)

This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. According to this proposal, background knowledge is important in the application of inductive rules and Bayesianism qualifies as an inductive rule. Finally, I consider a (...) Bayesian formulation of inductive skepticism suggested by Lange. I argue that while there is no good Bayesian reason for judging this inductive skeptic irrational, the approach I advocate indicates a straightforward reason not to be an inductive skeptic. (shrink)

It is shown that many different problems have the same degree of unsolvability. Among these problems are: THE INDUCTIVE INFERENCE PROBLEM. Infer in the limit an index for a recursive function f presented as f(0), f(1), f(2),.... THE RECURSIVE INDEX PROBLEM. Decide in the limit if i is the index of a total recursive function. THE ZERO NONVARIANT PROBLEM. Decide in the limit if a recursive function f presented as f(0), f(1), f(2),... has value unequal to zero (...) for infinitely many arguments. Finally, it is shown that these unsolvable problems are strictly easier than the halting problem. (shrink)

In this chapter we consider unsupervised learning from two perspectives. First, we briefly look at its advantages and disadvantages as an engineering technique applied to large corpora in natural language processing. While supervised learning generally achieves greater accuracy with less data, unsupervised learning offers significant savings in the intensive labour required for annotating text. Second, we discuss the possible relevance of unsupervised learning to debates on the cognitive basis of human language acquisition. In this context we explore the implications of (...) recent work on grammar induction for poverty of stimulus arguments that purport to motivate a strong bias model of language learning, commonly formulated as a theory of Universal Grammar (UG). We examine the second issue both as a problem in computational learning theory, and with reference to empirical work on unsupervised Machine Learning (ML) of syntactic structure. We compare two models of learning theory and the place of unsupervised learning within each of them. Looking at recent work on part of speech tagging and the recognition of syntactic structure, we see how far unsupervised ML methods have come in acquiring different kinds of grammatical knowledge from raw text. (shrink)

According to the Bayesian view, scientific hypotheses must be appraised in terms of their posterior probabilities relative to the available experimental data. Such posterior probabilities are derived from the prior probabilities of the hypotheses by applying Bayes'theorem. One of the most important problems arising within the Bayesian approach to scientific methodology is the choice of prior probabilities. Here this problem is considered in detail w.r.t. two applications of the Bayesian approach: (1) the theory of inductive probabilities (TIP) developed by (...) Rudolf Carnap and other epistomologists and (2) the analysis of the multinational inferences provided by Bayesian statstics (BS). ... Zie: Summary. (shrink)

The orthodox view about sensitivity and induction has it that beliefs formed via induction are insensitive. Since inductive knowledge is highly plausible, this problem is usually regarded as a reductio argument against sensitivity accounts of knowledge. Some adherents of sensitivity defend sensitivity against this objection, for example by considering backtracking interpretations of counterfactuals. All these extant views about sensitivity and induction have to be revised, since the problem of sensitivity and induction is a different (...) one. Regardless of whether we allow backtracking interpretations of counterfactuals, some instances of induction yield insensitive beliefs whereas others yield sensitive ones. These results are too heterogenous for providing a plausible sensitivity-account of inductive knowledge. Induction remains a serious problem for sensitivity accounts of knowledge. (shrink)

Conceptual change and its connection to the development of new seien tific theories has reeently beeome an intensively discussed topic in philo sophieal literature. Even if the inductive aspects related to conceptual change have already been discussed to some extent, there has so far existed no systematic treatment of inductive change due to conceptual enrichment. This is what we attempt to accomplish in this work, al though most of our technical results are restricted to the framework of monadic languages. We (...) extend Hintikka's system of inductive logic to apply to situations in which new concepts are introduced to the original language. By interpreting them as theoretica1 concepts, it is possible to discuss a number of currently debated philosophical and methodological problems which have previously escaped systematic and exact treatment. For instance, the role which seientific theories employing theoretical con cepts may play within inductive inference can be studied within this framework. From the viewpoint of seientific realism, sueh a study gives outlines for a theory of what we call hypothetico-induetive inference. Some parts of this work which are based on Hintikka's system of in ductive logic are fairly technical. However, no previous knowledge of this system is required, but, in general, acquaintance with the basic ideas of elementary logic and probability theory is suffieient. This work is part of a project, originated by Professors Jaakko Hintikka and Raimo Tuomela, concerning the role of theoretical concepts in science. (shrink)

A computational theory of induction must be able to identify the projectible predicates, that is to distinguish between which predicates can be used in inductive inferences and which cannot. The problems of projectibility are introduced by reviewing some of the stumbling blocks for the theory of induction that was developed by the logical empiricists. My diagnosis of these problems is that the traditional theory of induction, which started from a given (observational) language in relation to which all (...) inductive rules are formulated, does not go deep enough in representing the kind of information used in inductive inferences. As an interlude, I argue that the problem of induction, like so many other problems within AI, is a problem of knowledge representation. To the extent that AI-systems are based on linguistic representations of knowledge, these systems will face basically the same problems as did the logical empiricists over induction. In a more constructive mode, I then outline a non-linguistic knowledge representation based on conceptual spaces. The fundamental units of these spaces are "quality dimensions". In relation to such a representation it is possible to define "natural" properties which can be used for inductive projections. I argue that this approach evades most of the traditional problems. (shrink)

The context for this paper is Feferman's theory of explicit mathematics, T 0 . We address a problem that was posed in [6]. Let MID be the principle stating that any monotone operation on classifications has a least fixed point. The main objective of this paper is to show that T 0 + MID, when based on classical logic, also proves the existence of non-monotone inductive definitions that arise from arbitrary extensional operations on classifications. From the latter we deduce (...) that MID, when adjoined to classical T 0 , leads to a much stronger theory than T 0. (shrink)

In this article, I argue that arguments from the history of science against scientific realism, like the arguments advanced by P. Kyle Stanford and Peter Vickers, are fallacious. The so-called Old Induction, like Vickers's, and New Induction, like Stanford's, are both guilty of confirmation bias—specifically, of cherry-picking evidence that allegedly challenges scientific realism while ignoring evidence to the contrary. I also show that the historical episodes that Stanford adduces in support of his New Induction are indeterminate between (...) a pessimistic and an optimistic interpretation. For these reasons, these arguments are fallacious, and thus do not pose a serious challenge to scientific realism. (shrink)

I have applied a fairly general, learning theoretic perspective to some questions raised by Reichenbach's positions on induction and discovery. This is appropriate in an examination of the significance of Reichenbach's work, since the learning-theoretic perspective is to some degree part of Reichenbach's reliabilist legacy. I have argued that Reichenbach's positivism and his infatuation with probabilities are both irrelevant to his views on induction, which are principally grounded in the notion of limiting reliability. I have suggested that limiting (...) reliability is still a formidable basis for the formulation of methodological norms, particularly when reliability cannot possibly be had in the short run, so that refined judgments about evidential support must depend upon measure-theoretic choices having nothing to do in the short run with the truth of the hypothesis under investigation. To illustrate the generality of Reichenbach's program, I showed how it can be applied to methods that aim to solve arbitrary assessment and discovery problems in various senses. In this generalized Reichenbachian setting, we can characterize the intrinsic complexity of reliable inductive inference in terms of topological complexity. Finally, I let Reichenbach's theory of induction have the last say about hypothetico-deductive method. (shrink)

Hume’s view of reason is notoriously hard to pin down, not least because of the apparently contradictory positions which he appears to adopt in different places. The problem is perhaps most clear in his writings concerning induction - in his famous argument of Treatise I iii 6 and Enquiry IV, on the one hand, he seems to conclude that “probable inference” has no rational basis, while elsewhere, for example in much of his writing on natural theology, he seems (...) happy to acknowledge that such inference is not only reasonable, but is even a paradigm of reasoning against which the theistic arguments must be judged. In the face of this apparent contradiction, many recent commentators have proferred “non-sceptical” interpretations of Hume’s argument concerning induction, but in this paper I sketch an alternative and perhaps less radical method of resolving the problem, by identifying a major threefold ambiguity in Hume’s use of the word “reason”. On this interpretation, Hume indeed sees induction as a paradigm of reasonableness in what is arguably the most important sense, but he nevertheless believes induction to be entirely non-reasonable in another sense, which though less important in common life is nevertheless very significant philosophically. A comparison with Locke can help to illuminate Hume’s position, which though indeed not entirely sceptical about induction, is by no means entirely non-sceptical either. (shrink)

Is it possible to combine different logics into a coherent system with the goal of applying it to specific problems so that it sheds some light on foundational aspects of those logics? These are two of the most basic issues of combining logics. Paranormal modal logic is a combination of paraconsistent logic and modal logic. In this paper, I propose two further combinatory developments, focusing on each one of these two issues. On the foundational side, I combine paranormal modal logic (...) with normal modal logic, resulting into a paraconsistent and paracomplete multimodal logic dealing with the notions of plausibility and certainty. On the application side, I combine this logic with Reiter’s default logic, resulting into an inductive and consequently nonmonotonic paraconsistent and paracomplete logic able to represent some key inductive principles. (shrink)

Drawing the complex terrain of the theories of induction and of the various ways to ground inductive knowledge in the middle ages is the aim of this paper. There have already been two excellent attempts to draw this terrain. The first is by Julius R. Weinberg and the second by E. P. Bos. My attempt differs from theirs in two major respects. The first is that it is more detailed in the examination of the various theories and their relations. (...) The second is that I focus on the role of natural necessities in induction. In particular, I try to place the various conceptions of induction within a network of issues that relate to the problem of universals, natural necessities and a power-based approach to activity in nature. Here is the road map. Section 2 explains Aristotle’s views of induction, as this were mainly developed in Posterior Analytics Book II.19 and states what I take it to be the main dilemma of induction, as this was described by Sextus Empiricus: induction is either perfect and impossible or imperfect and unjustified. In section 3, I move to Thomas Aquinas and his own attempt to justify induction and the actuality of general and necessary principles based on experience by an appeal to the natural light of reason. In section 4 I discuss John Duns Scotus’s reliance on a self-evident maxim to bridge the gap between imperfect and perfect induction. Section 5 moves to William of Ockham ’s peculiar attempt to justify single-instance inductions. Then, section 6 offers a detailed account of Jean Buridan’s ground-breaking re-conceptualisation of induction and the role of intellect in it. Section 7 discusses the critique of induction by Nicolaus of Autrecourt. Finally, section 8 offers a brief account of Pseudo-Scotus’s move from knowledge to opinion. (shrink)