Abstract
The use of “levels of abstraction” in philosophical analysis (levelism) has recently come under attack. In this paper, I argue that a refined version of epistemological levelism should be retained as a fundamental method, called the method of levels of abstraction. After a brief introduction, in section “Some Definitions and Preliminary Examples” the nature and applicability of the epistemological method of levels of abstraction is clarified. In section “A Classic Application of the Method of Abstraction”, the philosophical fruitfulness of the new method is shown by using Kant’s classic discussion of the “antinomies of pure reason” as an example. In section “The Philosophy of the Method of Abstraction”, the method is further specified and supported by distinguishing it from three other forms of “levelism”: (i) levels of organisation; (ii) levels of explanation and (iii) conceptual schemes. In that context, the problems of relativism and antirealism are also briefly addressed. The conclusion discusses some of the work that lies ahead, two potential limitations of the method and some results that have already been obtained by applying the method to some long-standing philosophical problems.
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See for example Brown (1916). Of course the theory of ontological levels and the “chain of being” goes as far back as Plotin and forms the basis of at least one version of the ontological argument.
Poli (2001) provides a reconstruction of ontological levelism; more recently, Craver (2004) has analysed ontological levelism, especially in biology and cognitive science, see also Craver (forthcoming).
The distinction is really a matter of topology rather than cardinality. However, this definition serves our present purposes.
As the reader probably knows, this is done by recording the history of the game: move by move the state of each piece on the board is recorded—in English algebraic notation—by rank and file, the piece being moved and the consequences of the move.
It is interesting to note that the catastrophes of chaos theory are not smooth; although they do appear so when extra observables are added, taking the behaviour into a smooth curve on a higher-dimensional manifold. Typically, chaotic models are weaker than traditional models, their observables merely reflecting average or long-term behaviour. The nature of the models is clarified by making explicit the LoA.
The case of infinite sets has application to analogue systems but is not considered here.
I wish to thank Jesse F. Hughes for having pointed out to me the last requirement, without which only the variables would be related but not the elements of their types.
Direct knowledge is to be understood here as typically knowledge of one’s mental states, which is apparently not mediated; indirect knowledge is usually taken to be knowledge that is obtained inferentially or through some other form of mediated communication with the world.
Feynman (1995), the citation is from the Penguin edition, p. 66.
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Acknowledgements
I wish to thank Jeff Sanders, who should really be considered a co-author of this paper, with the exception of any of its potential mistakes; Gian Maria Greco, Jesse F. Hughes, Gianluca Paronitti and Matteo Turilli for their discussions of several previous drafts; Paul Oldfield for his editorial suggestions; Carl Craver for having made his forthcoming research available to me; and finally the anonymous referees of the journal for their constructive feedback.
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Floridi, L. The Method of Levels of Abstraction. Minds & Machines 18, 303–329 (2008). https://doi.org/10.1007/s11023-008-9113-7
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DOI: https://doi.org/10.1007/s11023-008-9113-7