Cameron Shelley, Multiple Analogies in Science and Philosophy

An analysis of Cameron Shelley's book on multiple analogies in science and philosophy

Building and Using Models as Examples

Sometimes, theoreticians explicitly state that they consider their models as examples. When this is not the case, it is fairly common for theoreticians to attribute to their models the characteristics and objectives of illustrative examples. However, this way of understanding models has not received enough attention in the methodological literature focused on economics. Given that didactic examples and their properties are extremely familiar in practice, considering theoretical models as examples can offer a useful perspective on models and their properties. On the basis of both explanatory and exemplifying role played by the deductive arguments by which results are proved, the paper emphasizes also the importance of understanding in theoretical work, the analogical and tentative character of the application of models, the central role played by the above mentioned arguments in such application, the didactic function of theory, and the transmision of plausibility from those arguments to the results obtained.models; examples; explanatory arguments; theoretical understanding; analogical application

Visual analogies and arguments

I argue that a basic similarity analysis of analogical reasoning handles many apparent cases of visual analogy. I consider how the visual and verbal elements interact in analogical cases. Finally, I offer two analyses of visual elements. One analysis is evidential. The visual elements are evidence for their ver-bal counterparts. One is non-evidential: the visual elements link to verbal elements without providing evi-dence for those elements. The result is to make more room for the logical analysis of visual argumentatio

Possibility, relevant similarity, and structural knowledge

Recently, interest has surged in similarity-based epistemologies of possibility. However, it has been pointed out that the notion of ‘relevant similarity’ is not properly developed in this literature. In this paper, I look at the research done in the field of analogical reasoning, where we find that one of the most promising ways of capturing relevance in similarity reasoning is by relying on the predictive analogy similarity relation. This takes relevant similarity to be based on shared properties that have structural relations to the property of interest. I argue that if we base our epistemology of possibility on similarity reasoning on the predictive analogy similarity relation, we require prior knowledge of the specifics of these structural relations. I discuss a number of possible responses to this on behalf of the similarity theorists given their methodological approach to the epistemology of modality more generally. They could either opt for making explicit the metaphysics underlying these structural relations, in which case they need to spell out how we can come to know these relations. Or they could opt for developing a theory that explains why we do not need to have explicit knowledge of these structural relations; for example by suggesting that we make use of epistemic shortcuts

Expert and Corpus-Based Evaluation of a 3-Space Model of Conceptual Blending

This paper presents the 3-space model of conceptual blending that estimates the figurative similarity between Input spaces 1 and 2 using both their analogical similarity and the interconnecting Generic Space. We describe how our Dr Inventor model is being evaluated as a model of lexically based figurative similarity. We describe distinct but related evaluation tasks focused on 1) identifying novel and quality analogies between computer graphics publications 2) evaluation of machine generated translations of text documents 3) evaluation of documents in a plagiarism corpus. Our results show that Dr Inventor is capable of generating novel comparisons between publications but also appears to be a useful tool for evaluating machine translation systems and for detecting and assessing the level of plagiarism between documents. We also outline another more recent evaluation, using a corpus of patent applications