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  1. Greg Bamford (1989). Watkins and the Pragmatic Problem of Induction. Analysis 49 (4):203 - 205..
    Watkins proposes a neo-Popperian solution to the pragmatic problem of induction. He asserts that evidence can be used non-Inductively to prefer the principle that corroboration is more successful over all human history than that, Say, Counter-Corroboration is more successful either over this same period or in the future. Watkins's argument for rejecting the first counter-Corroborationist alternative is beside the point, However, As whatever is the best strategy over all human history is irrelevant to the pragmatic problem of induction since we (...)
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  2. James Blachowicz (1996). Ampliative Abduction. International Studies in the Philosophy of Science 10 (2):141 – 157.
    Abstract In Peirce's and Hanson's characterization of abductive inference, the abducted hypothesis (but not others) is present in the premises, so that the inference can hardly be taken as ampliative. Abduction has consequently been treated as part of the process whereby already generated hypotheses are judged in terms of their plausibility, simplicity, etc. I propose an interpretation of abduction which supports an ampliative view. It relies on a distinction between two logical stages in the generation of hypotheses, one ?factual? and (...)
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  3. Simon Blackburn (1973). Reason and Prediction. London,Cambridge University Press.
    An original study of the philosophical problems associated with inductive reasoning. Like most of the main questions in epistemology, the classical problem of induction arises from doubts about a mode of inference used to justify some of our most familiar and pervasive beliefs. The experience of each individual is limited and fragmentary, yet the scope of our beliefs is much wider; and it is the relation between belief and experience, in particular the belief that the future will in some respects (...)
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  4. Peter Brössel (2013). Assessing Theories: The Coherentist Approach. Erkenntnis 79 (3):593-623.
    In this paper we show that the coherence measures of Olsson (J Philos 94:246–272, 2002), Shogenji (Log Anal 59:338–345, 1999), and Fitelson (Log Anal 63:194–199, 2003) satisfy the two most important adequacy requirements for the purpose of assessing theories. Following Hempel (Synthese 12:439–469, 1960), Levi (Gambling with truth, New York, A. A. Knopf, 1967), and recently Huber (Synthese 161:89–118, 2008) we require, as minimal or necessary conditions, that adequate assessment functions favor true theories over false theories and true and informative (...)
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  5. Ronald Christensen (1964/1980). Foundations of Inductive Reasoning. Entropy,Ltd.].
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  6. L. Jonathan Cohen & Avishai Margalit (1970). The Role of Inductive Reasoning in the Interpretation of Metaphor. Synthese 21 (3-4):469 - 487.
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  7. W. Geo Davies (1878). Necessary Connexion and Inductive Reasoning. Mind 3 (11):417-424.
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  8. Aron Edidin (1984). Inductive Reasoning and the Uniformity of Nature. Journal of Philosophical Logic 13 (3):285 - 302.
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  9. Theodore J. Everett (2010). Observation and Induction. Logos and Episteme 1 (2):303-324.
    This article offers a simple technical resolution to the problem of induction, which is to say that general facts are not always inferred from observations of particular facts, but are themselves sometimes defeasibly observed. The article suggests a holistic account of observation that allows for general statements in empirical theories to be interpreted as observation reports, in place of the common but arguably obsolete idea that observations are exclusively particular. Predictions and other particular statements about unobservable facts can then appear (...)
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  10. Aidan Feeney, Aimee K. Crisp & Catherine J. Wilburn (2008). Inductive Reasoning and Semantic Cognition: More Than Just Different Names for the Same Thing? Behavioral and Brain Sciences 31 (6):715-716.
    We describe evidence that certain inductive phenomena are associated with IQ, that different inductive phenomena emerge at different ages, and that the effects of causal knowledge on induction are decreased under conditions of memory load. On the basis of this evidence we argue that there is more to inductive reasoning than semantic cognition.
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  11. Aidan Feeney & Evan Heit (2011). Properties of the Diversity Effect in Category-Based Inductive Reasoning. Thinking and Reasoning 17 (2):156 - 181.
    Four experiments investigated how people judge the plausibility of category-based arguments, focusing on the diversity effect, in which arguments with diverse premise categories are considered particularly strong. In Experiment 1 we show that priming people as to the nature of the blank property determines whether sensitivity to diversity is observed. In Experiment 2 we find that people's hypotheses about the nature of the blank property predict judgements of argument strength. In Experiment 3 we examine the effect of our priming methodology (...)
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  12. W. GeoDavies (1878). Necessary Connexion and Inductive Reasoning. Mind 3 (11).
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  13. Nina Gierasimczuk (2009). Bridging Learning Theory and Dynamic Epistemic Logic. Synthese 169 (2):371-384.
    This paper discusses the possibility of modelling inductive inference (Gold 1967) in dynamic epistemic logic (see e.g. van Ditmarsch et al. 2007). The general purpose is to propose a semantic basis for designing a modal logic for learning in the limit. First, we analyze a variety of epistemological notions involved in identification in the limit and match it with traditional epistemic and doxastic logic approaches. Then, we provide a comparison of learning by erasing (Lange et al. 1996) and iterated epistemic (...)
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  14. Gary Gigliotti (1996). The Testing Principle: Inductive Reasoning and the Ellsberg Paradox. Thinking and Reasoning 2 (1):33 – 49.
    We postulate the Testing Principle : that individuals ''act like statisticians'' when they face uncertainty in a decision problem, ranking alternatives to the extent that available evidence allows. The Testing Principle implies that completeness of preferences, rather than the sure-thing principle , is violated in the Ellsberg Paradox. In the experiment, subjects chose between risky and uncertain acts in modified Ellsberg-type urn problems, with sample information about the uncertain urn. Our results show, consistent with the Testing Principle, that the uncertain (...)
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  15. Vittorio Girotto (1994). Is the Model Theory of Induction Also a Theory of Inductive Reasoning? International Studies in the Philosophy of Science 8 (1):41 – 43.
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  16. D. Goldstick (1972). Hume's “Circularity” Charge Against Inductive Reasoning. Dialogue 11 (02):258-266.
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  17. James Hawthorne & Branden Fitelson (2004). Discussion: Re‐Solving Irrelevant Conjunction with Probabilistic Independence. Philosophy of Science 71 (4):505-514.
    Naive deductivist accounts of confirmation have the undesirable consequence that if E confirms H, then E also confirms the conjunction H·X, for any X—even if X is completely irrelevant to E and H. Bayesian accounts of confirmation may appear to have the same problem. In a recent article in this journal Fitelson (2002) argued that existing Bayesian attempts to resolve of this problem are inadequate in several important respects. Fitelson then proposes a new‐and‐improved Bayesian account that overcomes the problem of (...)
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  18. Jürgen Humburg (1987). The Bayes Rule is Not Sufficient to Justify or Describe Inductive Reasoning. Erkenntnis 26 (3):379 - 390.
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  19. Andre Kukla (1992). Endogenous Constraints on Inductive Reasoning. Philosophical Psychology 5 (4):411 – 425.
    It is widely recognized that computational theories of learning must posit the existence of a priori constraints on hypothesis selection. The present article surveys the theoretical options available for modelling the dynamic process whereby the constraints have their effect. According to the 'simplicity' theory (exemplified by Fodor's treatment), hypotheses are preference-ordered in terms of their syntactic or semantic properties. It is argued that the same explanatory power can be obtained with a weaker (hence better) theory, the 'minimalist' theory, which dispenses (...)
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  20. Conor Mayo-Wilson (2011). The Problem of Piecemeal Induction. Philosophy of Science 78 (5):864-874.
    It is common to assume that the problem of induction arises only because of small sample sizes or unreliable data. In this paper, I argue that the piecemeal collection of data can also lead to underdetermination of theories by evidence, even if arbitrarily large amounts of completely reliable experimental and observational data are collected. Specifically, I focus on the construction of causal theories from the results of many studies (perhaps hundreds), including randomized controlled trials and observational studies, where the studies (...)
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  21. Stephen E. Newstead (1994). Inductive Reasoning, Deductive Reasoning and Mental Models. International Studies in the Philosophy of Science 8 (1):65 – 67.
    (1994). Inductive reasoning, deductive reasoning and mental models. International Studies in the Philosophy of Science: Vol. 8, No. 1, pp. 65-67. doi: 10.1080/02698599408573483.
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  22. Richard Nisbett, Krantz E., H. David, Christopher Jepson & Ziva Kunda (1983). The Use of Statistical Heuristics in Everyday Inductive Reasoning. Psychological Review 90:339-363.
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  23. T. Parent (2013). Note on Induction. Think 12 (33):37-39.
    Research Articles Ted Parent, Think , FirstView Article(s).
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  24. Daniel Rasmussen & Chris Eliasmith (2011). A Neural Model of Rule Generation in Inductive Reasoning. Topics in Cognitive Science 3 (1):140-153.
    Inductive reasoning is a fundamental and complex aspect of human intelligence. In particular, how do subjects, given a set of particular examples, generate general descriptions of the rules governing that set? We present a biologically plausible method for accomplishing this task and implement it in a spiking neuron model. We demonstrate the success of this model by applying it to the problem domain of Raven's Progressive Matrices, a widely used tool in the field of intelligence testing. The model is able (...)
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  25. Samuel Rathmanner & Marcus Hutter (2011). A Philosophical Treatise of Universal Induction. Entropy 13 (6):1076-1136.
    Understanding inductive reasoning is a problem that has engaged mankind for thousands of years. This problem is relevant to a wide range of fields and is integral to the philosophy of science. It has been tackled by many great minds ranging from philosophers to scientists to mathematicians, and more recently computer scientists. In this article we argue the case for Solomonoff Induction, a formal inductive framework which combines algorithmic information theory with the Bayesian framework. Although it achieves excellent theoretical results (...)
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  26. Nicholas Rescher (1980). Induction: An Essay on the Justification of Inductive Reasoning. Blackwell.
  27. Nicholas Rescher (1961). Non-Deductive Rules of Inference and Problems in the Analysis of Inductive Reasoning. Synthese 13 (3):242 - 251.
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  28. Marjorie Rhodes, Daniel Brickman & Susan A. Gelman (2008). Sample Diversity and Premise Typicality in Inductive Reasoning: Evidence for Developmental Change. Cognition 108 (2):543-556.
  29. Maxwell J. Roberts, Heather Welfare, Doreen P. Livermore & Alice M. Theadom (2000). Context, Visual Salience, and Inductive Reasoning. Thinking and Reasoning 6 (4):349 – 374.
    An important debate in the reasoning literature concerns the extent to which inference processes are domain-free or domain-specific. Typically, evidence in support of the domain-specific position comprises the facilitation observed when abstract reasoning tasks are set in realistic context. Three experiments are reported here in which the sources of facilitation were investigated for contextualised versions of Raven's Progressive Matrices (Richardson, 1991) and non-verbal analogies from the AH4 test (Richardson & Webster, 1996). Experiment 1 confirmed that the facilitation observed for the (...)
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  30. David H. Sanford (1990). The Inductive Support of Inductive Rules: Themes From Max Black. Dialectica 44 (1‐2):23-41.
    Overall, Max Black's defense of the inductive support of inductive rules succeeds. Circularity is best explained in terms of epistemic conditions of inference. When an inference is circular, another inference token of the same type may, because of a difference of surrounding circumstances, not be circular. Black's inductive arguments in support of inductive rules fit this pattern: a token circular in some circumstances may be noncircular in other circumstances.
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  31. Alfred Schramm (2014). Evidence, Hypothesis, and Grue. Erkenntnis 79 (3):571-591.
    Extant literature on Goodman’s ‘New Riddle of Induction’ deals mainly with two versions. I consider both of them, starting from the (‘epistemic’) version of Goodman’s classic of 1954. It turns out that it belongs to the realm of applications of inductive logic, and that it can be resolved by admitting only significant evidence (as I call it) for confirmations of hypotheses. Sect. 1 prepares some ground for the argument. As much of it depends on the notion of evidential significance, this (...)
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  32. Alfred Schramm (2006). Methodological Objectivism and Critical Rationalist ’Induction’. In Ian Jarvie, Karl Milford & David Miller (eds.), Karl Popper: A Centenary Assessment, Volume II. Ashgate.
    This paper constitutes one extended argument, which touches on various topics of Critical Rationalism as it was initiated by Karl Popper and further developed (although into different directions) in his aftermath. The result of the argument will be that critical rationalism either offers no solution to the problem of induction at all , or that it amounts, in the last resort, to a kind of Critical Rationalist Inductivism as it were, a version of what I call Good Old Induction. One (...)
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  33. O. Schulte (1999). Means-Ends Epistemology. British Journal for the Philosophy of Science 50 (1):1-31.
    This paper describes the corner-stones of a means-ends approach to the philosophy of inductive inference. I begin with a fallibilist ideal of convergence to the truth in the long run, or in the 'limit of inquiry'. I determine which methods are optimal for attaining additional epistemic aims (notably fast and steady convergence to the truth). Means-ends vindications of (a version of) Occam's Razor and the natural generalizations in a Goodmanian Riddle of Induction illustrate the power of this approach. The paper (...)
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  34. Joseph Solomon (1888). The Aim of Inductive Reasoning. Mind 13 (49):85-89.
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  35. Wolfgang Spohn (1990). A General Non-Probabilistic Theory of Inductive Reasoning. In R. D. Shachter, T. S. Levitt, J. Lemmer & L. N. Kanal (eds.), Uncertainty in Artificial Intelligence 4. Elsevier.
    Probability theory, epistemically interpreted, provides an excellent, if not the best available account of inductive reasoning. This is so because there are general and definite rules for the change of subjective probabilities through information or experience; induction and belief change are one and same topic, after all. The most basic of these rules is simply to conditionalize with respect to the information received; and there are similar and more general rules. 1 Hence, a fundamental reason for the epistemological success of (...)
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  36. Jacob Stegenga (2013). An Impossibility Theorem for Amalgamating Evidence. Synthese 190 (12):2391-2411.
    Amalgamating evidence of different kinds for the same hypothesis into an overall confirmation is analogous, I argue, to amalgamating individuals’ preferences into a group preference. The latter faces well-known impossibility theorems, most famously “Arrow’s Theorem”. Once the analogy between amalgamating evidence and amalgamating preferences is tight, it is obvious that amalgamating evidence might face a theorem similar to Arrow’s. I prove that this is so, and end by discussing the plausibility of the axioms required for the theorem.
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  37. Ahmed Y. Tawfik (2004). Inductive Reasoning and Chance Discovery. Minds and Machines 14 (4):441-451.
    This paper argues that chance (risk or opportunity) discovery is challenging, from a reasoning point of view, because it represents a dilemma for inductive reasoning. Chance discovery shares many features with the grue paradox. Consequently, Bayesian approaches represent a potential solution. The Bayesian solution evaluates alternative models generated using a temporal logic planner to manage the chance. Surprise indices are used in monitoring the conformity of the real world and the assessed probabilities. Game theoretic approaches are proposed to deal with (...)
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  38. Naftali Weinberger (2014). Evidence-Based Policy: A Practical Guide to Doing It Better, Nancy Cartwright and Jeremy Hardie. Oxford University Press, 2013, Ix + 196 Pages. [REVIEW] Economics and Philosophy 30 (1):113-120.
  39. Roger White (2005). Explanation as a Guide to Induction. Philosophers' Imprint 5 (2):1-29.
    It is notoriously difficult to spell out the norms of inductive reasoning in a neat set of rules. I explore the idea that explanatory considerations are the key to sorting out the good inductive inferences from the bad. After defending the crucial explanatory virtue of stability, I apply this approach to a range of inductive inferences, puzzles, and principles such as the Raven and Grue problems, and the significance of varied data and random sampling.
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