Plausible Reasoning for the Problems of Cognitive Sociology

• Authors: Victor K. Finn , Maria A. Mikheyenkova
• Languages of publication: EN

Abstracts

EN

The plausible reasoning class (called the JSM-reasoning in hon- our of John Stuart Mill) is described. It implements interaction of three forms of non-deductive procedures-induction, analogy and abduction. Empirical induction in the JSM-reasoning is the basis for generation of hy- potheses on causal relations (determinants of social behaviour). Inference by analogy means that predictions about previously unknown properties of objects (individual’s behaviour) are inferred from causal relations. Ab- ductive inference is performed to check on the explanatory adequacy of generated hypotheses. To recognize rationality of respondents’ opinion de- ductive inference is used. Plausible reasoning, semantics of argumentation logic and deductive recognition of opinion rationality represent logical tool for cognitive sociology problems.

Keywords

EN

plausible reasoning; induction; analogy; abduction; knowledge discovery; reasoning about causality

• Publisher: Uniwersytet Mikołaja Kopernika (Nicolaus Copernicus University in Toruń)
• Journal: Logic and Logical Philosophy
• Year: 2011
• Volume: 20
• Issue: 1-2
• Pages: 111-137

Dates

published

2011-06-01

online

2013-07-02

Contributors

author

Victor K. Finn

• All-Russian Institute for Scientific and Technical Information Usievitcha 20 125190 Moscow, Russia

author

Maria A. Mikheyenkova

• All-Russian Institute for Scientific and Technical Information Usievitcha 20 125190 Moscow, Russia

References

• [1] Anshakov, O.M., Finn, V.K., Skvortsov, D.P., “On axiomatization of many-valued logics associated with formalization of plausible reasoning”, Studia Logica XLVIII, 4 (1989): 423-448.
• [2] Anshakov, O.M., Finn, V.K., Skvortsov, D.P., “Plausible reasoning and its logics”, pages 87-91 in: International Conference on Intelligent Sys-tems and Semiotics: A Learning Perspective, Gaithersburg, MD, 1997.
• [3] Anshakov, O.M., Gergely, T., Cognitive Reasoning, Springer, Heidelberg, Dordrecht, London - New York 2010.
• [4] Barwise, J. (ed.): Handbook of Mathematical Logic, Pt. 1, Theory of Mod- els. North-Holland, Amsterdam - New - York - Oxford (1977).
• [5] Blinova, V.G., et al., “Toxicology analysis by means of JSM method”, Bioinformatics 19, 10 (2003): 1201-1207.[PubMed]
• [6] Bolc, L., Borowik, P., Many-Valued Logics, vol. 1, “Theoretical Founda- tions”, pp. 123-142, Springer-Verlag, 1992.
• [7] Burch, R.W., ‘Semeiotic data fusion”, pages 67-73 in: 3rd InternationalConference on Information Fusion, Paris, 2000.
• [8] Finn, V.K., “A form of argumentation logic”, Automatic Documentationand Mathematical Linguistic 30, 3 (1996): 3-27.
• [9] Finn, V.K., “The synthesis of cognitive procedures and the problem of induction”, Automatic Documentation and Mathematical Linguistic 43, 3 (2009): 149-195.
• [10] Finn, V.K.: “Toward structural cognitology: Phenomenology of con- sciousness form the point of view of artificial intelligence”, Russian Journalof Communication II, 1/2 (2009): 81-104.
• [11] Finn, V.K., Mikheyenkova, M.A., “Quantofrenia by P. Sorokin and for- malized qualitative analysis”, pages 189-192 in: V.A. Mansurov (ed.), European Society or European Societies: a View from Russia, Moscow - Lisbon, 2009.
• [12] Finn, V.K., Mikheyenkova, M.A., Introduction to Logic and IntelligentData Analysis for Sociologists (in Russian), preprint, 2010.
• [13] Josephson, J.R., Josephson, S.G. (eds.), Abductive Inference: Computa-tion, Philosophy, Technology. Cambridge Univ. Press, 1994.
• [14] Kelle, U. (ed.), Computer-aided Qualitative Data Analysis: Theory, Meth-ods and Practice. London. Thousand Oaks, New Delhi: SAGE Publica- tion, 1998.
• [15] Mikheyenkova, M.A., et al., “Application of JSM method of automatic hypotheses generation to some problems of sociology”, pages 22-25 in: Workshop of Applied Semiotics, ECAI-96, Budapest, August 12-16, 1996.
• [16] Mikheyenkova, M.A., “On the logical tools of intelligent sociological data analysis” (in Russian), Artificial Intelligence and Decision Making 1 (2010): 20-32.
• [17] Mill, J. S., A System of Logic Ratiocinative and Inductive, Being a Con-nected View of the Principles of Evidence and the Methods of ScientificInvestigation, London, Parker, Son and Bowin, 1843 (1st edition).
• [18] Peirce, C. S., “Abduction and induction”, pages 150-156 in: L. Buchler (ed.) Philosophical Writings of Peirce, Dover Publications, no 4, 1995.
• [19] Ragin, C.C., The Comparative Method: Moving beyond Qualitative andQuantitative Strategies, University of California Press, Berkley, Los An- geles and London, 1987.
• [20] Ragin, C.C., Fuzzy-Set Social Science, University of Chicago Press, Chicago, 2000.
• [21] Rihoux, B., “Qualitative comparative analysis and related systematic comparative methods”, International Sociology 21, 5 (2006).[Crossref]
• [22] Rosser, J.B., Turquette, A.R., Many-Valued Logics, Amsterdam, North- Holland Publishing Company, 1958
• [23] Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.), Advanced in Knowledge Discovery and Data Mining, Menlo Park, Califor- nia, 1996.
• [24] Skvortsov, D.P., “On some methods of constructing the languages with quantifiers over tuples” (in Russian), Semiotics and Informatics 20 (1983): 102-126.
• [25] Smullyan, R.M., First Order Logic, N.Y., Springer-Verlag, 1968.
• [26] Strauss, A., Corbin, J., Basics of Qualitative Research, Thousand Oaks, London, New Dehli, SAGE Publications, 1998.
• [27] Sudman S., Bradburn N., Schwartz N., Thinking about Answers: TheApplication of Cognitive Processes to Survey Methodology, San Francisco, Jossey-Bass Inc. Publishers, 1996.
• [28] Vinogradov, D.W., “Formalizing plausible arguments in predicate logic”, Automatic Documentation and Mathematical Linguistic 34, 6 (2000): 6-10.
• [29] Zerubavel, E., Social Mindscape. An Invitation to Cognitive Sociology, Harvard Univ. Press, 1997.

Identifiers

DOI

10.12775/LLP.2011.006