Two types of inductive analogy by similarity

Kuipers, Theo Α.F.

doi:10.1007/bf00176183

Cited by 26 publications

(15 citation statements)

References 7 publications

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“…In addition to symmetry there have been numerous attempts to incorporate principles based on analogy into Inductive Logic, initially by Carnap, for example in [4], by Carnap & Stegmüller [5] and later for example by Festa [6], Kuipers [13], Maher [16], [17], Maio [18], Niiniluoto [19], Romeijn [27], Skyrms [28]. Generally these have considered analogy as deriving from the sharing of similar or identical properties by the constants.…”

Section: The Atom Exchangeability Principle (Ax)mentioning

confidence: 99%

The Counterpart Principle of Analogical Support by Structural Similarity

Hill

Paris

2013

Erkenn

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Section: The Atom Exchangeability Principle (Ax)mentioning

confidence: 99%

The Counterpart Principle of Analogical Support by Structural Similarity

Hill

Paris

2013

Erkenn

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“…Let me stress once again an important aspect to the model of analogical predictions defined in this section, namely that inductive relevances serve as explicit input to the prediction rules. In this sense the model is similar to the models of Niiniluoto (1981) and Kuipers (1984), while it differs from the models of Festa (1997) and Maher (2000). In these latter models, there is no direct access, in terms of input parameters, to the inductive relevances that may be assumed.…”

Section: A Model For Explicit Similaritymentioning

confidence: 84%

“…Some of these prediction rules are exchangeable, that is, invariant under permutations of the given Qpredicates, and some are nonexchangeable. Examples of such models are given in Kuipers (1984Kuipers ( , 1988, Skyrms (1993), Di Maio (1995) and Festa (1997). However, to my mind analogical predictions are more easily associated with similarity in terms of underlying predicates, here called explicit similarity, than with similarity between predicates directly.…”

Section: Analogical Predictionsmentioning

confidence: 98%

Analogical Predictions for Explicit Similarity

Romeijn

2006

Erkenntnis

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This paper concerns exchangeable analogical predictions based on similarity relations between predicates, and deals with a restricted class of such relations. It describes a system of Carnapian kc rules on underlying predicate families to model the analogical predictions for this restricted class. Instead of the usual axiomatic definition, the system is characterized with a Bayesian model that employs certain statistical hypotheses. Finally the paper argues that the Bayesian model can be generalized to cover cases outside the restricted class of similarity relations. ANALOGY WITHIN CARNAPIAN RULESImagine that the marketing director of a bowling alley is interested in the demographic composition of the crowds visiting her alley. Every evening she records the gender of some of the visitors, and whether they are married or not. Now let us say that on one evening half of the recorded visitors are male and married, and the other half are female and unmarried. Then if a newly arrived visitor is a man, the director may consider it more likely that he will be married than not. But if this visitor is a woman, the director may consider it more likely that she is not married. At least some of the similarity between individuals at the bowling alley is thus explicit in the observation language, namely in their gender, and in making predictions on their marital status this similarity may be employed. In such a case we speak of inductive relevance of explicit similarity in terms of specific predicate. Such relations are possible because individuals are categorised with predicates from more than one predicate family. The explicit similarity is always with respect to one of these predicate families.Putting G for gender and M for marital status, the analogical prediction in the example of explicit similarity may be represented in the following way:

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“…Various remedies have been proposed. 10 That to be pressed into service here originated with Kuipers (1984) as a counter-proposal to one by Niiniluoto (1980Niiniluoto ( , 1981, who subsequently endorsed it (1988,287). Kuipers observes that we can view (1) as the application of the straight rule to n Q real empirical instances of the strongest property Q and λ(K)/K virtual logical instances of Q (1984, 69).…”

Section: Norms For Analogiesmentioning

confidence: 92%

“…Carnap (1952) made the decisive step forward, and his work has served as the basis for later advances by , Carnap (1971Carnap ( , 1980, Pietarinen (1972), Hintikka and Niiniluoto (1976), Kuipers (1978Kuipers ( , 1984, Niiniluoto (1981), Skyrms (1991Skyrms ( , 1993, and Festa (1997), among others. In Carnap's mature work (e.g., 1942, 96-97; 1945, 73-75), the concept of range is a semantic concept explicable as the set of models in which a given sentence (or conjunction of sentences) is true.…”

Section: Norms For Analogiesmentioning

confidence: 97%

Vagueness and Inductive Molding

Welch

2007

Synthese

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Vagueness is epistemic, according to some. Vagueness is ontological, according to others. This paper deploys what I take to be a compromise position. Predicates are coined in specific contexts for specific purposes, but these limited practices do not automatically fix the extensions of predicates over the domain of all objects. The linguistic community using the predicate has rarely considered, much less decided, all questions that might arise about the predicate's extension. To this extent, the ontological view is correct. But a predicate that applies in some contexts can be reasonably extended to other contexts where it is initially vague. This process of development approximates the cognitive remedy for vagueness that the epistemic view prescribes. The process is piecemeal and inductive, akin to what von Wright described as the molding of concepts.Vagueness cannot be understood apart from the backdrop of classification, for vagueness is classification gone awry. Hence these pages explore the classification of particulars, both its clear successes and vague failures. How we classify unique particulars is the theme of Sections 2 and 3, which are primarily descriptive. Section 2 identifies a way of classifying particulars that pervades discourse of all sorts, and Section 3 illustrates its use in a field notorious for vagueness: ethics. Why a certain particular should (or should not) be classified in a certain way is a normative question, however, and it occupies Sections 4 and 5. Section 4 proposes a norm for strong arguments by analogy, and Section 5 illustrates how the norm might resolve vagueness in one kind of ethical dispute. This norm, which has a strong probabilistic component, is one way of affirming that probability is a guide to life.

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Two types of inductive analogy by similarity

Cited by 26 publications

References 7 publications

The Counterpart Principle of Analogical Support by Structural Similarity

The Counterpart Principle of Analogical Support by Structural Similarity

Analogical Predictions for Explicit Similarity

Vagueness and Inductive Molding

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