Abstract
Both logic and philosophy of science have developed independent pluralistic views. Nevertheless, a general notion of pluralism is quite elusive. This paper will draw on the theory of information, including Shannon’s version and its fundamental concept of coding, to set up a notion of pluralism that may contribute to an updated idea of the scientific method, such as to interact with computational models. I will discuss Patrick Allo’s notion of “informational pluralism” and connect it with Wesley Salmon’s treatment of “statistical relevance” and James Woodward’s analysis of the “data-phenomena” relationship. Based on the concept of information, I will argue that there is a possible convergence of the three views.
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Notes
- 1.
Beall and Restall’s formal account of logical pluralism provides the best opportunities for an information-theoretic approach, directed to support scientific methodology. Consequently, this paper will focus on semantic and model-theoretic issues and will be less concerned with modal topics such as necessity, possibility, and consistency. In this line of thought, modal logic will be reduced to a few mentions, mostly concerning the accessibility relation. For an alternative view and a more intense connection between pluralism and modal logic, see Bueno and Shalkowski (2009).
- 2.
If objective information consists of potential data, they plausibly become actual when encoded in the formulae of a language. See Sect. 6.4, below.
- 3.
Russell distinguishes (1) a substitutional account of LC, where non-logical expressions are uniformly substituted throughout the argument by “syntactically appropriate alternative expressions”; (2) an interpretational account, where different meanings replace the meanings of certain non-logical expressions; (3) a representational account, where non-logical expressions are represented by various situations or states of the world; (4) a universal account, where the non-logical vocabulary is uniformly substituted by different assignments of values to non-logical variables. See Russell (2019), 337–339.
- 4.
The concept of informational content has been used by Dretske in his classical analysis. He distinguishes between meaning and information and defines information as “a commodity capable of yielding knowledge” (Dretske 1981, 44). Allo accepts this distinction (Allo 2007, 687), which involves the problem of defining “information.” I am not going to pursue such a thorny question in this paper.
- 5.
- 6.
Any theory of channel cannot but be connected to the information theory that Claude Shannon established in 1948.
- 7.
In the second part of this paper, I intend to show that informational pluralism may also impact the philosophy of science.
- 8.
See also Mares’ relation of “accessibility” in Mares (2010), 117, and 125.
- 9.
- 10.
Oron Shagrir has written that “Computation is information processing.”
- 11.
Data are recorded (and I think encoded!) observations. Phenomena are defined this way: “facts about phenomena are natural candidates for systematic scientific explanations in a way in which facts about data are not.” (Bogen and Woodward 1988, 326)
- 12.
Bogen and Woodward do not consider models and simulations.
- 13.
More than this, I maintain that the pluralistic features of DPR also lead to the autonomy of computational models. I may even suggest that models, insofar as consisting of interpretive patterns applied to data collections, may be equated to the structures that connect and organize what Woodward calls “phenomena”.
- 14.
For an epistemic synthesis of Woodward’s ideas about causality, see Gonzalez 2018.
- 15.
This is based on the well-known equivalence – asserted by Claude Shannon (1998, 40) – between a discrete information source and a stochastic process.
- 16.
Besides the equivalence between situations and classes, we may recall other important computational equivalences, such as that between propositions and types, which is the basis of the fundamental Curry-Howard Isomorphism.
- 17.
- 18.
I am slightly departing from Salmon’s account at this point.
- 19.
Salmon was aware that computability theory was needed for the crucial problem of the construction of objectively homogeneous reference classes (see Salmon 1984, 58–60, 67–68, 81). As for information theory, see Salmon 1984, in particular, 97–101, 125–126, 139–154. S-R approach has been recently updated in informational terms, especially via the notion of “causal power”. This represents the degree to which changes in a cause C produce changes in an effect E. Causal power uses relevance logic to cover not only conditional probabilities and statistical datasets but also Bayesian Networks used in Artificial Intelligence. Causal power is measured using Shannon’s entropy concept (Korb et al. 2009).
- 20.
The completeness of partitions provides to Salmon’s S-R models the character of objectivity that distinguishes them from Hempel’s I-S models (Salmon 1984, 41). Completeness is granted by the maximal homogeneity of both explanans and explanandum partitions (Salmon 1984, 37). It is also to be mentioned that Salmon’s objective homogeneity is the same thing as Shannon’s “ergodicity,” roughly defined as “statistical homogeneity” (Shannon 1998, 45), and reputed to be essential for the final Theorem 11 of Information theory (cf. Kinchin 1957).
- 21.
Salmon’s and Greeno’s visible source is Kullback’s classical treatise Information Theory and Statistics. Kullback supplements Shannon’s concepts with the works of Fisher on information (cf. Kullback 1968). However, apart from Salmon’s theory, it is crucial to shift from Fisher’s to Shannon’s view. In doing so, we gain an intensional perspective on statistical types that is the premise for being able to build computational models. Sayre also misses this point.
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Camardi, G. (2020). Information and Pluralism. Consequences for Scientific Representation and Methods. In: Gonzalez, W.J. (eds) Methodological Prospects for Scientific Research. Synthese Library, vol 430. Springer, Cham. https://doi.org/10.1007/978-3-030-52500-2_6
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