Abstract
We present a computer-supported approach for the logical analysis and conceptual explicitation of argumentative discourse. Computational hermeneutics harnesses recent progresses in automated reasoning for higher-order logics and aims at formalizing natural-language argumentative discourse using flexible combinations of expressive non-classical logics. In doing so, it allows us to render explicit the tacit conceptualizations implicit in argumentative discursive practices. Our approach operates on networks of structured arguments and is iterative and two-layered. At one layer we search for logically correct formalizations for each of the individual arguments. At the next layer we select among those correct formalizations the ones which honor the argument’s dialectic role, i.e. attacking or supporting other arguments as intended. We operate at these two layers in parallel and continuously rate sentences’ formalizations by using, primarily, inferential adequacy criteria. An interpretive, logical theory will thus gradually evolve. This theory is composed of meaning postulates serving as explications for concepts playing a role in the analyzed arguments. Such a recursive, iterative approach to interpretation does justice to the inherent circularity of understanding: the whole is understood compositionally on the basis of its parts, while each part is understood only in the context of the whole (hermeneutic circle). We summarily discuss previous work on exemplary applications of human-in-the-loop computational hermeneutics in metaphysical discourse. We also discuss some of the main challenges involved in fully-automating our approach. By sketching some design ideas and reviewing relevant technologies, we argue for the technological feasibility of a highly-automated computational hermeneutics.
“...that the same way that the whole is, of course, understood in reference to the individual, so too, the individual can only be understood in reference to the whole.”
Friedrich Schleiermacher (1829)
Benzmüller received funding for this research from VolkswagenStiftung under grant CRAP 93678 (Consistent Rational Argumentation in Politics).
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Notes
- 1.
This is similar to the distinction between TBox and ABox in knowledge bases. Some may claim that ‘ontological’ sentences (TBox) tend to be more permanent and mostly concern types, classes and other universals; while other, ‘non-ontological’ sentences (ABox) mostly concern their instances. The former may be treated as being always true and the latter as subject to on-line revision. However, what counts as a class, what as an instance and what is subject to revision is heavily dependent on the use we intend to give to the theory (knowledge base).
- 2.
Recalling Carnap’s related notion of explication [23], we can think of a set of meaning postulates as providing a precise characterization for some new, exact concept (explicatum) aimed at “replacing” an inexact, pre-theoretical notion (explicandum), for the purpose of advancing a theory. Thus, in computational hermeneutics, the non-logical terms of our interpretive theory characterize concepts playing the role of explicata aimed at explicitly representing fuzzy, unarticulated explicanda from a tacit conceptualization.
- 3.
As we see it, those rules are (their tacitness notwithstanding) of a logical nature: they concern which arguments or inferences are endorsed by (a community of) speakers.
- 4.
When we talk of models of a logical theory or ontology, we always refer to models in a model-theoretical sense, i.e. interpretations: assignments of values (as denoted entities) to non-logical terms.
- 5.
For instance, the existence of human races may need to be posited for some eugenicist arguments to succeed; or the presupposition of highly-localized specific brain functions may be needed for a phrenology-related argument to get through. When we intuitively accept the conclusions of arguments we may thereby also commit to the existence of their posits (as made explicit in the logical forms of adequate formalizations). Conversely, such arguments can be attacked by calling into question the mere existence of what they posit.
- 6.
Guarino further considers factors like language expressivity (richness of logical and non-logical vocabulary) and scope of the domain of discourse in having a bearing on the degree to which an ontology specifies a conceptualization.
- 7.
Logical correctness encompasses, among others, the more traditional concept of logical validity. Our working notion of logical correctness also encompasses axioms/premises consistency and lack of circularity (no petitio principii) as well as avoiding idle premises. Other accounts may consider different criteria. We have restricted ourselves to the ones that can be efficiently computed with today’s automated reasoning technology. See [50] for an interesting discussion of logical (in)correctness.
- 8.
Sharing a similar background and motivation, computational hermeneutics might support a technological implementation of contemporary approaches for the revisionary philosophical analysis of public discourse like ameliorative analysis (in particular, as presented in [47]), conceptual ethics [22], and conceptual engineering (e.g. as discussed in [20]).
- 9.
In this sense, Davidson has emphatically made clear that he does not aim at showing how humans actually interpret let alone acquire natural language. This being rather a subject of empirical research (e.g. in cognitive science and linguistics) [25]. However, Davidson’s philosophical point becomes particularly interesting in artificial intelligence, as regards the design of artificial language-capable machines.
- 10.
- 11.
The notion of reflective equilibrium has been initially proposed by Nelson Goodman [40] as an account for the justification of the principles of (inductive) logic and has been popularized years later in political philosophy and ethics by John Rawls [52] for the justification of moral principles. In Rawls’ account, “reflective equilibrium” refers to a state of balance or coherence between a set of general principles and particular judgments (where the latter follow from the former). We arrive at such a state through a deliberative give-and-take process of mutual adjustment between principles and judgments. More recent methodical accounts of reflective equilibrium have been proposed as a justification condition for scientific theories [31] and objectual understanding [2], and as a methodology for conceptual engineering [20].
- 12.
There are ongoing efforts on our part to frame the problem of finding an adequate interpretive theory as an optimization problem to be approached by appropriate heuristic methods.
- 13.
Recall that we think of discourses as networks of mutually supporting/attacking arguments. Each formalized argument can be seen as a collection of axioms and theorems; the latter being intended to logically follow from a combination of the former plus some further axioms of a definitional nature (meaning postulates). This view is in tune with prominent structured approaches to argumentation in artificial intelligence (cf. [14, 30]).
- 14.
As mentioned in Sect. 2.1, there is no definitive criteria for distinguishing meaning postulates from others (cf. ontological vs. non-ontological or TBox vs. ABox sentences). The heuristics for labeling sentences as meaning postulates thus constitute another degree of freedom in our interpretive process, which we address primarily (but not exclusively) by means of inferential adequacy criteria. Moreover, our set of meaning postulates can at some point become inconsistent, thus urging us to mark some of them for controlled removal. In this aspect, our approach resembles reason-maintenance and belief-revision frameworks (cf. [29]).
- 15.
Argument databases and arguments extracted from text sources usually provide information on support and attack relations (see [21, 44] and references therein). Another alternative is to dynamically construct the needed arguments by using the working theory plus hypothetical premises and conclusions as building stones. Those arguments would then be presented, in an interactive way, to the user for rejection or endorsement. This mode of operation would correspond to a kind of inverted (we could call it ‘Socratic’) question-answering system.
- 16.
Note that this approach is not related to the similarly named notion of word embeddings in natural language processing (NLP).
- 17.
Note that since Isabelle-specific extensions of HOL (except for prefix polymorphism) are not exploited in our work, the technical framework we depict here can easily be transferred to other HOL theorem proving environments.
- 18.
- 19.
Such a reading would be in tune with strong conceptions of existence drawing on the Quinean slogan “no entity without identity”.
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Fuenmayor, D., Benzmüller, C. (2019). A Computational-Hermeneutic Approach for Conceptual Explicitation. In: Nepomuceno-Fernández, Á., Magnani, L., Salguero-Lamillar, F., Barés-Gómez, C., Fontaine, M. (eds) Model-Based Reasoning in Science and Technology. MBR 2018. Studies in Applied Philosophy, Epistemology and Rational Ethics, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-030-32722-4_25
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