The Goal-Structure Approach to Qualitative Valuation
Table of contents
Share
Metrics
The Goal-Structure Approach to Qualitative Valuation
Annotation
PII
S042473880000016-6-1
DOI
10.7868/s0424738818020085
Publication type
Article
Status
Published
Authors
Viktor Istratov 
Affiliation: Cemi RAS
Address: Russian Federation
Pages
104-126
Abstract
The author analyses qualitative valuation: while being frequently used in economic literature, very little attention is paid to the study of qualitative estimates themselves. As a consequence, qualitative assessments are often inconvenient to use because of the vagueness of their wording, which, in turn, generates discrepancies and misunderstandings. In addition, the ambiguity of the wording leads to the impossibility of comparing qualitative estimates, or substantially limits this possibility. Both ambiguity and incomparability are detrimental to the prospects for qualitative valuation as a scientific tool. In the current situation, it is necessary to develop a unified formulation approach (a language) to qualitative assessments to make them unambiguous and comparable. The author makes an attempt to formalize and standardize the presentation of qualitative values, and to find a method for their submission and formulation that differs from those already available. The article contains an overview of the approaches to qualitative assessments adopted in several areas of knowledge that are close to the computational economy and simulation modeling: in particular, in the economics, in artificial intelligence, and the others. The approach proposed in the article takes into account the goals with which a qualitative estimate is used. It involves the interpretation of a qualitative assessment as a complex element which structure plays the key role in understanding and applying the value. Qualitative assessment in this approach is not opposed to quantitative, and in some cases they are combined. The proposed approach may be useful in computer simulation of human behavior and socio-economic interactions.
Keywords
qualitative value, quality, economic valuation
Received
09.10.2017
Date of publication
29.06.2018
Number of purchasers
7
Views
612
Readers community rating
0.0 (0 votes)
Cite Download pdf 1 RUB / 0.0 SU

To download PDF you should sign in

1 123

References

1. Aristotle (1934). Metaphysics. Translate by A. V. Kubitsky. Moscow, Leningrad: Sotsekgiz (in Russian).

2. Azgaldov G. G. (1982) Theory and Practice of Product Quality Assessment (Fundamentals of Qualimetry). М.: Ekonomika (in Russian).

3. Bailey-Kellogg C., Zhao F. (2003). Qualitative Spatial Reasoning Extracting and Reasoning with Spatial Aggregates. AI Magazine, 24, 4, 47–60.

4. Bonet B., Geffner H. (1996). Arguing for Decisions: a Qualitative Model of Decision. Proceedings of the 12th Conference on Uncertainty in Artificial Intelligence (UAI), 98–105.

5. Boulding K. E. (1991). What is Evolutionary Economics? Journal of Evolutionary Economics, 1, 1, 9–17.

6. Boutilier C. (1994). Toward a Logic for Qualitative Decision Theory. Proceedings of the Fourth International Conference on Knowledge Representation and Reasoning (KR’94). San Mateo: Morgan Kaufmann, 75–86.

7. Bredeweg B., Forbus K. (2003). Qualitative Modeling in Education. AI Magazine, 24, 4, 35–46

8. Cabanac M. (1971). Physiological Role of Pleasure. Science, 173, 1103–1107.

9. Chaitin-Chatelin F., Traviesas-Cassan E. (2005). Qualitative Computing. In: B. Einarsson (ed.) “Accuracy and Reliability in Scientific Computing”. Philadelphia: SIAM Press, 77–92.

10. Coase R. H. (1960). The Problem of Social Cost. Journal of Law and Economics, 3, 1–44.

11. Dastani M., Hulstijn J., Torre L. van der (2001). BDI and QDT: a Comparison Based on Classical Decision Theory. Proceedings of AAAI Spring Symposium on Game Theoretic and Decision Theoretic Agents GTDT’01. Menlo Park: AAAI Press, 16–26.

12. Dastani M., Torre L. van der (2005). Decisions, Deliberation, and Agent Types CDT – QDT – BDI – 3APL – BOID. In: Shannon S. (ed.) “Artificial Intelligence and Computer Science”. N.Y.: Nova Science, 217–233.

13. Davis E. (1990). Order of Magnitude Reasoning in Qualitative Differential Equations. In: Weld D., Kleer J. de (eds.) “Qualitative Reasoning about Physical Systems”. San Mateo: Morgan Kaufmann, 422–434.

14. Doyle J., Thomason R. H. (1999). Background to Qualitative Decision Theory. AI Magazine, 20, 2, 55–68.

15. Drechsler W. (2004). Natural Versus Social Sciences: On Understanding in Economics. Reinert E. S. (ed.) “Globalization, Economic Development and Inequality: An Alternative Perspective”. Cheltenham: Edward Elgar Publishing, 71–87.

16. Dubois D., Fargier H., Perny P., Prade H. (2002). Qualitative Decision Theory: From Savage’s Axioms to Non- Monotonic Reasoning. Journal of the ACM, 49, 4, 455–495.

17. Easterlin R. A. (2001). Income and Happiness: Towards a Unified Theory. The Economic Journal, 111, 473, 465–484

18. Forbus K. D. (1984). Qualitative Process Theory. Artificial Intelligence, 24, 85–168.

19. Forbus K. D. (1996). Qualitative Reasoning. In: Tucker A. B. (ed.) “CRC Handbook of Computer Science and Engineering”. Boca Raton: CRC Press, 715–733.

20. Guest G., Namey E. E., Mitchell M. L. (2013). Collecting Qualitative Data. A Field Manual for Applied Research. Thousand Oaks: Sage Publishing.

21. Haefner J. W. (2005). Modeling Biological Systems: Principles and Applications. N.Y.: Springer.

22. Kleer J. de, Brown J. S. (1984). A Qualitative Physics Confluences. Artificial Intelligence, 24, 7–83.

23. Kuipers B. (2001). Qualitative Simulation. In: Meyers R. A. (ed.) “Encyclopedia of Physical Science and Technology”, 13. N.Y.: Academic Press, 287–300.

24. Luce R. D., Narens L. (1994). Fifteen Problems Concerning the Representational Theory of Measurement. In: Humphreys P. (ed.) “Patrick Suppes: Scientific Philosopher, Vol. 2: Philosophy of Physics, Theory Structure, Measurement Theory, Philosophy of Language, and Logic”. Dordrecht: Kluwer Academic Publishers, 219–245.

25. Luce R. D., Narens L. (2008). Measurement, theory of. In: Blume L., Durlauf S. N. (eds.) “The New Palgrave Dictionary of Economics”, 5. Basingstoke, Hampshire: Palgrave Macmillan, 523–533.

26. Luce R. D., Tukey J. W. (1964). Simultaneous Conjoint Measurement: A New Type of Fundamental Measurement. Journal of Mathematical Psychology, 1, 1–27.

27. Michell J. (1997). Quantitative Science and the Definition of Measurement in Psychology. British Journal of Psychology, 88, 355–383.

28. Michell J. (2010). The Quantity/Quality Interchange: A Blind Spot on the Highway of Science. In: Toomela A., Valsiner J. (eds.) “Methodological Thinking in Psychology: 60 Years Gone Astray?” Charlotte: Information Age Publishing, 45–68.

29. Michell J., Ernst C. (1996). The Axioms of Quantity and the Theory of Measurement. Translated from part I of Otto Hölder’s German text “Die Axiome der Quantität und die Lehre vom Mass”. Journal of Mathematical Psychology, 40, 235–252.

30. Narens L. (1981). A General Theory of Ratio Scalability with Remarks about the Measurement-Theoretic Concept of Meaningfulness. Theory and Decision, 13, 1, 1–70.

31. Narens L. (1988). Meaningfulness and the Erlanger Program of Felix Klein. Mathématiques Informatique et Sciences Humaines, 101, 61–71.

32. Narens L., Luce R. D. (2008). Meaningfulness and invariance. In: Blume L., Durlauf S. N. (eds.) “The New Palgrave Dictionary of Economics”, 5. Basingstoke, Hampshire; N.Y.: Palgrave Macmillan, 503–508.

33. Okrepilov V. V. (1998) Quality Management. М.: Ekonomika (in Russian).

34. Parisi F. (2008). Coase theorem. In: Blume L., Durlauf S. N. (eds.) “The New Palgrave Dictionary of Economics”, 1. Basingstoke, Hampshire; N.Y.: Palgrave Macmillan, 855–861

35. Pearl J. (1993). From Conditional Oughts to Qualitative Decision Theory. In: “UAI’93 Proceedings of the Ninth International Conference on Uncertainty in Artificial Intelligence”. San Francisco: Morgan Kaufmann, 12–20.

36. Pfanzagl J. (1976) Theory of measurement. М.: Mir (in Russian).

37. Pyka A., Grebel T. (2006). Agent-Based Modelling – a Methodology for the Analysis of Qualitative Development Processes. In: Billari F. C., Fent T., Prskawetz A., Scheffran J. (eds.), “Agent-Based Computational Modelling Applications in Demography, Social, Economic and Environmental Sciences”. Heidelberg: Physica-Verlag, 17–35.

38. Reinert E. S. (2014). How Rich Countries Got Rich and Why Poor Countries Stay Poor. Moscow: HSE Publishing House (in Russain).

39. Rozeboom W. W. (1966). Scaling Theory and the Nature of Measurement. Synthese, 16, 70–233.

40. Salles P., Bredeweg B. (2003). Qualitative Reasoning about Population and Community Ecology. AI Magazine, 24, 4, 77–90.

41. Salles P., Bredeweg B. (2003). Qualitative Reasoning about Population and Community Ecology. AI Magazine, 24, 4, 77–90.

42. Stevens S. S. (1946). On the Theory of Scales of Measurement. Science, 103, 2684, 677–680.

43. Stigler G. J. (1966). The Theory of Price. N.Y.: Macmillan.

44. Struss P., Price C. (2003). Model-Based Systems in the Automotive Industry. AI Magazine, 24, 4, 17–34.

45. Suppes P. (1951). A Set of Independent Axioms for Extensive Quantities. Portugaliae Mathematica, 10, 4, 163–172.