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Abduction Logics: Illustrating Pitfalls of Defeasible Methods

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Part of the book series: Logic, Argumentation & Reasoning ((LARI,volume 14))

Abstract

On the one hand this paper offers an introduction to adaptive logics, focussing on properties that are imposed upon adaptive logics by the fact that they explicate defeasible reasoning. On the other hand new adaptive logics of abduction are presented and employed to illustrate those properties. These logics were developed in view of the criticism to existing adaptive logics of abduction.

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Notes

  1. 1.

    Formal logics need not validate Uniform Substitution. Let \(\mathcal {S}\), \(\mathcal {P}^r\), and \(\mathcal {C}\) be respectively the sets of sentential letters, predicates of rank r, and individual constants of \(\mathcal {L}\). Let f be a one-one mapping such that \(f:\mathcal {S}\longrightarrow \mathcal {S}\), \(f:\mathcal {P}^r\longrightarrow \mathcal {P}^r\), and \(f:\mathcal {C}\longrightarrow \mathcal {C}\). Extend f first to formulas, f(A) being the result of replacing in A every \(\xi \in \mathcal {S}\cup \mathcal {P}^r\cup \mathcal {C}\) by \(f(\xi )\); next to sets of formulas: \(f(\varGamma ) = _{ df } \{ f(A) \mid A\in \varGamma \}\). That \(\varGamma \vdash _\mathbf {L} A\) iff \(f(\varGamma ) \vdash _\mathbf {L} f(A)\) is sufficient for \(\mathbf {L}\) to be formal. Incidentally, the Uniform Substitution rule is tiresome at the predicative level [39].

  2. 2.

    For readers not familiar with paraconsistency, if \(A\vee B\) and \(\lnot A\) are true, then so is \((A\wedge \lnot A)\vee (B\wedge \lnot A)\). Only the second disjunct entails B. So, in the paraconsistent case, \(\{ A\vee B, \lnot A\}\) does not entail B but only \(B\vee (A\wedge \lnot A)\). This has been pointed out a long time ago by Newton da Costa, by Alan Anderson and Nuel D. Belnap, and by many others, and it gave rise to inconsistency-adaptive logics.

  3. 3.

    If both B and \(\lnot B\) are true, then so is \(A\supset B\), even if A is true and \(\lnot A\) is false.

  4. 4.

    For present purposes, call an adaptive logic \(\mathbf {AL}\) corrective iff all \(\mathbf {AL}\)-consequences of \(\varGamma \) are \(\mathbf {CL}\)-consequences (classical logic consequences) of \(\varGamma \); ampliative iff the converse holds.

  5. 5.

    Both readers familiar with the literature on explanation and readers familiar with detective stories will remember cases of a person who was poisoned at 4 PM and shot at 6 PM, but actually died of a heart attack at 5 PM.

  6. 6.

    I phrase this in terms of probabilities, but any similar approach may be equally acceptable.

  7. 7.

    People often define their views as the coherent systematization of their explicit views. The connected reasoning displays the internal dynamics. So everyone who has thought about his or her views is familiar with the internal dynamics.

  8. 8.

    The idea behind \(\mathbf {AAL}\) is that some formulas of the form \(A(a)\triangleright E(a)\) are derivable from \(\varGamma \) on top of the \(\mathbf {CL}\)-consequences of \(\varGamma \). So the transition from \(\mathbf {CL}\) to \(\mathbf {AAL}\) cannot have the effect that more generalizations become derivable from \(\varGamma \).

  9. 9.

    Note that defeasibility is not connected to derivability but to one’s insights in derivability.

  10. 10.

    A is a Weak consequence of \(\varGamma \) iff it is a \(\mathbf {CL}\)-consequence of a consistent subset of \(\varGamma \). Incidentally, Weak is paraconsistent, \(p,\lnot p\nvdash _{\mathrm {Weak}} q\), and not reflexive, \(p\wedge \lnot p\nvdash _{\mathrm {Weak}}p\wedge \lnot p\).

  11. 11.

    The reader may safely identify a logic that has static proofs with a compact Tarski logic, viz. a logic that is compact, reflexive, transitive, and monotonic.

  12. 12.

    The symbol \(\mathbin {\mathring{\vee }}\) is a conventional name to refer to a symbol of the language that has the meaning of classical disjunction.

  13. 13.

    For a restricted logical form, see the form defining \(\mathcal {G}\) below in the text.

  14. 14.

    Which applications are validated is determined by \(\mathbf {LLL}\)-consequences of the premise set. So adaptive logics display a form of content-guidance—Dudley Shapere [47] among others stated and defended the function of this feature in scientific methodology.

  15. 15.

    The reasoning in this paragraph applies to language schemas. It applies to languages of which the predicates are conceptually independent.

  16. 16.

    The simplified normal forms of contingent formulas do not contain irrelevant literals. Example: the simplified disjunctive normal form of \((Px\wedge Qx)\vee (Px\wedge \lnot Qx)\vee (Rx\wedge Px)\) is Px.

  17. 17.

    In the present context, \(\lceil \forall x(A(x)\supset B(x))\rceil \) may also be defined as \(\{ \forall x(C(x))\mid C\in s(A(x)\supset B(x))\}\). This however would not suit the generic approach, for example in case causal generalizations are considered.

  18. 18.

    A proof stage is a sequence of lines. A proof is a chain of stages, every stage containing the lines of the previous stage in the same order.

  19. 19.

    The logic under consideration has the unusual property that lines that are marked at a stage remain marked at all subsequent stages.

  20. 20.

    The \(\mathbf {ULL}\)-models are the \(\mathbf {LLL}\)-models that verify no abnormality.

  21. 21.

    The subset is proper, except for some exceptional adaptive logics called flip-flops.

  22. 22.

    A choice set of \(\langle \varDelta _1, \varDelta _2,\ldots \rangle \) comprises one member of every \(\varDelta _i\) (\(i\in \{1,2,\ldots \}\)). A choice set of \(\langle \varDelta _1, \varDelta _2,\ldots \rangle \) is minimal iff none of its proper subsets is a choice set of \(\langle \varDelta _1, \varDelta _2,\ldots \rangle \).

  23. 23.

    The so-called Simple strategy will do for such \(\varGamma \). Marking for the Simple strategy is defined by: line l is marked at stage s iff, at stage s, a member of the condition of line l is derived on the condition \(\emptyset \).

  24. 24.

    The statement should be taken literally in several senses. It pertains to the proofs, to the computational complexity of the consequence sets, to the semantic selection criterion, ....

  25. 25.

    Note that \(\lnot \Box _i\lnot A\) is adaptively derivable whenever A is compatible with \(T_i\).

  26. 26.

    While the premise \(\Box _2 Sa\) allows me to easily make the point, its presence is obviously artificial. Note, however, that \(\Box _2(\lnot Pa\vee Sa\vee Ta)\) is \({{\mathbf {CL}}^{\mathrm {M}}}\)-derivable from \(\varGamma _{2}\) and hence does not have any potential explanations.

  27. 27.

    Actually this too involves adaptive features, just like the study of yes/no questions [30, 31].

  28. 28.

    So inconsistency-adaptive logics assign all \(\mathbf {CL}\)-consequences to consistent premise sets.

  29. 29.

    That insights gained in terms of dynamic proofs may be rephrased in semantic terms does not undermine the avail of dynamic proofs.

  30. 30.

    Several points from this paragraph were clarified by a weird discussion in the literature [12, 21, 52] that started off as an attack on adaptive logics, presumably on the dynamic proofs, although that was never very clear.

References

  1. Aliseda, A. (2006). Abductive Reasoning., Logical investigations into discovery and explanation Dordrecht: Springer.

    Google Scholar 

  2. Batens, D. (1980). Paraconsistent extensional propositional logics. Logique et Analyse, 90–91, 195–234.

    Google Scholar 

  3. Batens, D. (1989). Dynamic dialectical logics. In G. Priest, R. Routley, & J. Norman (Eds.), Paraconsistent logic (pp. 187–217)., Essays on the inconsistent München: Philosophia Verlag.

    Google Scholar 

  4. Batens, D. (1995). The clue to dynamic aspects of logic. Logique et Analyse, 150–152, 285–328. Appeared 1997.

    Google Scholar 

  5. Batens, D. (2000). Towards the unification of inconsistency handling mechanisms. Logic and Logical Philosophy, 8, 5–31. Appeared 2002.

    Article  Google Scholar 

  6. Batens, D. (2001). A general characterization of adaptive logics. Logique et Analyse, 173–175, 45–68. Appeared 2003.

    Google Scholar 

  7. Batens, D. (2004). The need for adaptive logics in epistemology. In D. Gabbay, S. Rahman, J. Symons, & J. P. V. Bendegem (Eds.), Logic (pp. 459–485)., Epistemology and the unity of science Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  8. Batens, D. (2005). A procedural criterion for final derivability in inconsistency-adaptive logics. Journal of Applied Logic, 3, 221–250.

    Article  Google Scholar 

  9. Batens, D. (2006). Narrowing down suspicion in inconsistent premise sets. In J. Malinowski & A. Pietruszczak (Eds.), Essays in Logic and Ontology (Vol. 91, pp. 185–209)., Poznań studies in the philosophy of the sciences and the humanities Amsterdam/New York: Rodopi.

    Google Scholar 

  10. Batens, D. (2007). A universal logic approach to adaptive logics. Logica Universalis, 1, 221–242.

    Article  Google Scholar 

  11. Batens, D. (2009). Towards a dialogic interpretation of dynamic proofs. In Cédric Dégremont, Laurent Keiff & Helge Rückert (Eds.), Dialogues, logics and other strange things. essays in honour of shahid rahman, (pp. 27–51). London: College Publications, 558 p.

    Google Scholar 

  12. Batens, D., De Clercq, K., Verdée, P., & Meheus, J. (2009). Yes fellows, most human reasoning is complex. Synthese, 166, 113–131.

    Article  Google Scholar 

  13. Batens, D., & Meheus, J. (2000a). The adaptive logic of compatibility. Studia Logica, 66, 327–348.

    Google Scholar 

  14. Batens, D., & Meheus, J. (2000b). A tableau method for inconsistency-adaptive logics. In R. Dyckhoff (Ed.), Automated Reasoning with Analytic Tableaux and Related Methods (Vol. 1847, pp. 127–142). Lecture Notes in Artificial Intelligence, Springer.

    Google Scholar 

  15. Batens, D., & Meheus, J. (2001). Shortcuts and dynamic marking in the tableau method for adaptive logics. Studia Logica, 69, 221–248.

    Article  Google Scholar 

  16. Batens, D., Straßer, C., & Verdée, P. (2009). On the transparency of defeasible logics: Equivalent premise sets, equivalence of their extensions, and maximality of the lower limit. Logique et Analyse, 207, 281–304.

    Google Scholar 

  17. Beirlaen, M., & Aliseda, A. (2014). A conditional logic for abduction. Synthese, 191, 3733–3758.

    Article  Google Scholar 

  18. Carl G. Hempel. Aspects of Scientific Explanation and Other Essays in the Philosophy of Science. The Free Press, New York, 1965.

    Google Scholar 

  19. Gauderis, T. (2013). Modelling abduction in science by means of a modal adaptive logic. Foundations of Science, 18, 611–624.

    Article  Google Scholar 

  20. Halonen, I., & Hintikka, J. (2005). Toward a theory of the process of explanation. Synthese, 143, 5–61.

    Article  Google Scholar 

  21. Horsten, L., & Welch, P. (2007). The undecidability of propositional adaptive logic. Synthese, 158, 41–60.

    Article  Google Scholar 

  22. Leuridan, B. (2009). Causal discovery and the problem of ignorance. An adaptive logic approach. Journal of Applied Logic, 7, 188–205.

    Article  Google Scholar 

  23. Lewis, D. (1973). Counterfactuals. Mass.: Harvard University Press, Cambridge.

    Google Scholar 

  24. Lycke, H. (2012). A formal explication of the search for explanations: the adaptive logics approach to abductive reasoning. Logic Journal of the IGPL, 20, 497–516.

    Article  Google Scholar 

  25. Magnani, L. (2001). Abduction, reason, and science processes of discovery and explanation. New York: Kluwer Academic / Plenum Publishers.

    Book  Google Scholar 

  26. Magnani, L., Carnielli, W. & Pizzi, C. Eds. (2010). Model-based reasoning in science and technology. abduction, logic, and computational discovery (Vol. 314). Studies in Computational Intelligence, Heidelberg: Springer.

    Google Scholar 

  27. Meheus, J. (1993). Adaptive logic in scientific discovery: The case of Clausius. Logique et Analyse, 143–144, 359–389. Appeared 1996.

    Google Scholar 

  28. Meheus, J. (1999a). Clausius’ discovery of the first two laws of thermodynamics. A paradigm of reasoning from inconsistencies. Philosophica, 63, 89–117. Appeared 2001.

    Google Scholar 

  29. Meheus, J. (1999b). Deductive and ampliative adaptive logics as tools in the study of creativity. Foundations of Science, 4, 325–336.

    Google Scholar 

  30. Meheus, J. (1999b). Erotetic arguments from inconsistent premises. Logique et Analyse, 165–166, 49–80. Appeared 2002.

    Google Scholar 

  31. Meheus, J. (2001). Adaptive logics for question evocation. Logique et Analyse, 173–175, 135–164. Appeared 2003.

    Google Scholar 

  32. Meheus, J. (2002). Inconsistencies in scientific discovery. Clausius’s remarkable derivation of Carnot’s theorem. In H. Krach, G. Vanpaemel, & P. Marage (Eds.), History of modern physics (pp. 143–154)., Acta of the XXth International Congress of History of Science Turnhout (Belgium): Brepols.

    Chapter  Google Scholar 

  33. Meheus, J. (2011). A formal logic for the abduction of singular hypotheses. In D. Dieks, W. J. Gonzalez, S. Hartmann, T. Uebel, & M. Weber (Eds.), Explanation (pp. 93–108)., Prediction, and confirmation Dordrecht: Springer.

    Google Scholar 

  34. Meheus, J., Adaptive logics for abduction and the explication of explanation-seeking processes. In Pombo and Gerner [41], pp. 97–119.

    Google Scholar 

  35. Meheus, J., & Batens, D. (2006). A formal logic for abductive reasoning. Logic Journal of the IGPL, 14, 221–236.

    Article  Google Scholar 

  36. Odintsov, S. P., & Speranski, S. O. (2012). On algorithmic properties of propositional inconsistency-adaptive logics. Logic and Logical Philosophy, 21, 209–228.

    Google Scholar 

  37. Odintsov, S. P., & Speranski, S. O. (2013). Computability issues for adaptive logics in multi-consequence standard format. Studia Logica, 101(6), 1237–1262. doi:10.1007/s11225-013-9531-2.

    Article  Google Scholar 

  38. Paul, G. (2000). AI approaches to abduction. In D. M. Gabbay & P. Smets (Eds.), Handbook of defeasible reasoning and uncertainty management systems (pp. 35–98)., Abductive reasoning and learning Dordrecht: Kluwer Academic Publishers.

    Google Scholar 

  39. Pogorzelski, W. A., & Prucnal, T. (1975). The substitution rule for predicate letters in the first-order predicate calculus. Reports on Mathematical Logic, 5, 77–90.

    Google Scholar 

  40. John, L. (1976). Pollock. Dordrecht: Subjunctive Reasoning. Reidel.

    Google Scholar 

  41. Pombo, O., & Gerner, A. (Eds.). (2007). Abduction and the Process of Scientific Discovery. Lisboa: Centro de Filosofia das Ciências da Universidade de Lisboa.

    Google Scholar 

  42. Rescher, N. (1964). Hypothetical Reasoning. Amsterdam: North-Holland.

    Google Scholar 

  43. Rescher, N. (1973). The Coherence Theory of Truth. Oxford: Clarendon.

    Google Scholar 

  44. Rescher, N. (2005). What If?. New Brunswick, New Jersey: Transaction Publishers.

    Google Scholar 

  45. Rescher, N., & Manor, R. (1970). On inference from inconsistent premises. Theory and Decision, 1, 179–217.

    Article  Google Scholar 

  46. Routley, R., & Meyer, R. K. (1976). Dialectical logic, classical logic, and the consistency of the world. Studies in Soviet Thought, 16, 1–25.

    Article  Google Scholar 

  47. Shapere, D. (2004). Logic and the philosophical interpretation of science. In P. Weingartner (Ed.), Alternative logics (pp. 41–54)., Do sciences need them? Berlin, Heidelberg: Springer.

    Google Scholar 

  48. Vanackere, G. (1997). Ambiguity-adaptive logic. Logique et Analyse, 159, 261–280. Appeared 1999.

    Google Scholar 

  49. Vanackere, G. (1999). Minimizing ambiguity and paraconsistency. Logique et Analyse, 165–166, 139–160. Appeared 2002.

    Google Scholar 

  50. Van Dyck, M. (2004). Causal discovery using adaptive logics. Towards a more realistic heuristics for human causal learning. Logique et Analyse, 185–188, 5–32. Appeared 2005.

    Google Scholar 

  51. Van Kerckhove, B., & Vanackere, G. (2003). Vagueness-adaptive logic: A pragmatical approach to Sorites paradoxes. Studia Logica, 75, 383–411.

    Article  Google Scholar 

  52. Verdée, P. (2009). Adaptive logics using the minimal abnormality strategy are \(\Pi ^1_1\)-complex. Synthese, 167, 93–104.

    Article  Google Scholar 

  53. Verdée, P. (2013). A proof procedure for adaptive logics. Logic Journal of the IGPL, 21, 743–766. doi:10.1093/jigpal/jzs046.

    Article  Google Scholar 

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Acknowledgements

I am indebted to Joke Meheus and especially to Frederik Van De Putte for comments on a draft of this paper.

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Correspondence to Diderik Batens .

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Batens, D. (2017). Abduction Logics: Illustrating Pitfalls of Defeasible Methods. In: Urbaniak, R., Payette, G. (eds) Applications of Formal Philosophy. Logic, Argumentation & Reasoning, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-319-58507-9_8

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