Reasoning maps for decision aid: an integrated approach for problem-structuring and multi-criteria evaluation

Case Oriented Paper

Abstract

This paper proposes a tool for multi-criteria decision aid to be referred to as a Reasoning Map. It is motivated by a desire to provide an integrated approach to problem structuring and evaluation, and in particular, to make the transition between these two processes a natural and seamless progression. The approach has two phases. In the first one, the building of a Reasoning Map supports problem structuring, capturing a decision maker's reasoning as a network of means and ends concepts. In the second phase, this map is enhanced, employing a user-defined qualitative scale to measure both performances of decision options and strengths of influence for each means–end link. This latter phase supports the decision maker in evaluating the positive and negative impacts of an action through synthesis of the qualitative information. A case study, which investigates the use of the method in practice, is also presented.

Keywords

cognitive mapping multiple criteria evaluation qualitative decision analysis soft-hard OR integration 

References

  1. Ackermann F and Belton V (1999) Mixing methods: Balancing equivocality with precision. Working Paper 99/4, Department of Management Science, University of Strathclyde, Glasgow, UK.Google Scholar
  2. Ackermann F and Eden C (2004). Using causal mapping—individual and group, traditional and new. In: Pidd M. (ed). Systems Modelling—Theory and Practice. Wiley: Chichester, pp. 127–145.Google Scholar
  3. Axelrod R (ed) (1976). Structure of Decision: The Cognitive Maps of Political Elites. Princeton University Press: Princeton.Google Scholar
  4. Bana e Costa CA, Ensslin L, Corrêa EC and Vansnick JC (1999). Decision Support Systems in action: Integrated application in a multi-criteria decision aid process. Eur J Opl Res 113: 315–335.CrossRefGoogle Scholar
  5. Belton V (1985). The use of a simple multiple criteria model to assist in selection from a shortlist. J Opl Res Soc 36: 265–274.Google Scholar
  6. Belton V, Ackermann F and Shepherd I (1997). Integrated support from problem structuring through to alternative evaluation using COPE and V·I·S·A. J Multi-Criteria Decis Anal 6: 115–130.CrossRefGoogle Scholar
  7. Belton V and Stewart T (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Kluwer: Dordrecht.CrossRefGoogle Scholar
  8. Bryson JM, Ackermann F, Eden C and Finn CB (2004). Visible Thinking: Unlocking Causal Mapping for Practical Business Results. Wiley: Chichester.Google Scholar
  9. Budescu DV and Wallsten TS (1985). Consistency in interpretation of probabilistic phrases. Org Behav Hum Decis Process 36: 391–405.CrossRefGoogle Scholar
  10. Buss AR (1978). Causes and reasons in attribute theory: A conceptual critique. J Pers Soc Psychol 36: 1311–1321.CrossRefGoogle Scholar
  11. Chen L-S and Cheng H-S (2005). Selecting IS personnel use fuzzy GDSS based on metric distance method. Eur J Opl Res 160: 803–820.CrossRefGoogle Scholar
  12. Cossette P and Audet M (1992). Mapping of an idiosyncratic schema. J Mngt Stud 29: 325–348.Google Scholar
  13. Domingo-Ferrer J and Torra V (2003). Median based aggregation operators for prototype construction in ordinal scales. Int J Intell Syst 18: 633–655.CrossRefGoogle Scholar
  14. Eden C (2004). Analyzing cognitive maps to help structure issues or problems. Eur J Opl Res 159: 673–686.CrossRefGoogle Scholar
  15. Eden C and Ackermann F (1998). Analysing and comparing idiographic causal maps. In: Eden C and Spender JC (eds). Managerial and Organizational Cognition. Sage: London, pp. 192–209.Google Scholar
  16. Ensslin L, Dutra A and Ensslin SR (2000a). MCDA: A constructivist approach to the management of human resources at a governmental agency. Intl Trans Op Res 7: 79–100.CrossRefGoogle Scholar
  17. Ensslin L, Montibeller G and Lima MVA (2000b). Constructing and implementing a DSS to help evaluate perceived risk of accounts receivable. In: Haimes YY and Steuer RE (eds). Research and Practice in Multiple Criteria Decision Making. Springer: Berlin, pp. 248–259.CrossRefGoogle Scholar
  18. Figueira J, Greco S and Ehrgott M (2005). Multiple Criteria Decision Analysis—State of the Art Surveys. Springer: New York.CrossRefGoogle Scholar
  19. Godo L and Torra V (2000). On aggregation operators for ordinal qualitative information. IEEE Trans Fuzzy Syst 8: 143–154.CrossRefGoogle Scholar
  20. Goodwin P and Wright G (2004). Decision Analysis for Management Judgement, 3rd edition. Wiley: Chichester.Google Scholar
  21. Gordon SE and Gill RT (1992). Knowledge acquisition with question probes and conceptual graph structures. In: Lauer TW, Peacock EG and Graesser AC (eds). Questions and Information Systems. Lawrence Erlbaum: Hillsdale, pp. 29–46.Google Scholar
  22. Gutman J (1982). A means–end chain model based on consumer categorization processes. J Marketing 46: 60–72.CrossRefGoogle Scholar
  23. Heckerman D and Shachter R (1995). Decision-theoretic foundations for causal reasoning. J Artif Intell Res 3: 405–430.Google Scholar
  24. Herrera F and Herrera-Viedma E (2000). Linguistic decision analysis: Steps for solving decision problems under linguistic information. Fuzzy Sets Syst 115: 67–82.CrossRefGoogle Scholar
  25. Huff AS and Jenkins M (eds) (2002). Mapping Strategic Knowledge. Sage: London.Google Scholar
  26. Huizingh EKRE and Vrolijk HCJ (1997). A comparison of verbal and numerical judgements in the Analytic Hierarchy Process. Org Behav Hum Decis Process 70: 237–247.CrossRefGoogle Scholar
  27. Keeney RL (1992). Value-Focused Thinking: A Path to Creative Decision-making. Harvard University Press: Cambridge.Google Scholar
  28. Kosko B (1986). Fuzzy cognitive maps. Int J Man-Machines Stud 25: 65–75.CrossRefGoogle Scholar
  29. Kosko B (1992). Neural Networks and Fuzzy Sets. Prentice-Hall: Englewood Cliffs.Google Scholar
  30. Larichev OI (1992). Cognitive validity in design of decision-aiding techniques. J Multi-Criteria Decis Anal 1: 127–138.CrossRefGoogle Scholar
  31. Marchant T (1999). Cognitive maps and fuzzy implications. Eur J Opl Res 114: 626–637.CrossRefGoogle Scholar
  32. Merkhofer MW (1990). Using influence diagrams in multi-attribute utility analysis—improving effectiveness through improving communication. In: Olivier RM and Smith JQ (eds). Influence Diagrams, Belief Nets and Decision Analysis. Wiley: Chichester, pp. 297–317.Google Scholar
  33. Mingers J (1997). Multi-paradigm multimethodology. In: Mingers J and Gill A (eds). Multimethodology. Wiley: Chichester, pp. 1–20.Google Scholar
  34. Montibeller G (2000). Reasoning maps for decision aiding. PhD Dissertation, Federal University of Santa Catarina, Department of Production Engineering, Brazil (in cooperation with the Department of Management Science, University of Strathclyde, Scotland) (in Portuguese)..Google Scholar
  35. Montibeller G and Belton V (2006). Causal maps and the evaluation of options—a review. J Opl Res Soc 57: 771–779.CrossRefGoogle Scholar
  36. Montibeller G, Belton V and Lima MV (2006). Supporting factoring transactions in Brazil using reasoning maps: A language-based DSS for evaluating accounts receivable. Decis Support Syst, doi: 10.1016/j.dss.2004.11.011.Google Scholar
  37. Nadkarni S and Shenoy PP (2001). A Bayesian network approach to making inferences in causal maps. Eur J Opl Res 128: 479–498.CrossRefGoogle Scholar
  38. Nadkarni S and Shenoy PP (2004). A causal mapping approach to constructing Bayesian networks. Decis Support Syst 38: 259–281.CrossRefGoogle Scholar
  39. Rosenhead J and Mingers J (eds) (2001). Rational Analysis for a Problematic World Revisited. Wiley: Chichester.Google Scholar
  40. Roy B (1993). Decision science or decision-aid science? Eur J Opl Res 66: 184–203.CrossRefGoogle Scholar
  41. Schon DA (1984). The Reflective Practitioner: How Professionals Think in Action. Basic Books: New York.Google Scholar
  42. Slovic P (1995). The construction of preference. Am Psychol 50: 364–371.CrossRefGoogle Scholar
  43. Spyridakos A, Siskos Y, Yannacopoulos D and Skouris A (1991). Multicriteria job evaluation for large organizations. Eur J Opl Res 130: 357–387.Google Scholar
  44. Sterman JD (2000). Business Dynamics. Irwin McGraw-Hill: Boston.Google Scholar
  45. Watson SR and Buede DM (1987). Decision Synthesis. Cambridge University Press: Cambridge.Google Scholar
  46. Wellman MP (1994). Inference in cognitive maps. Math Comput Simulation 36: 137–148.CrossRefGoogle Scholar
  47. Yager RR (1995). An approach to ordinal decision making. Int J Approx Reason 12: 237–261.CrossRefGoogle Scholar
  48. Yager RR (1998). Fusion of ordinal information using weighted median aggregation. Int J Approx Reason 18: 35–52.CrossRefGoogle Scholar
  49. Zhang W, Chen S and Bezdek JC (1989). Pool2: A generic system for cognitive map development and decision analysis. IEEE Trans Syst Man Cybernet 19: 31–39.CrossRefGoogle Scholar

Copyright information

© Palgrave Macmillan Ltd 2007

Authors and Affiliations

  • G Montibeller
    • 1
    • 1
  • V Belton
    • 2
  • F Ackermann
    • 2
  • L Ensslin
    • 3
  1. 1.London School of EconomicsLondonUK
  2. 2.University of StrathclydeGlasgowUK
  3. 3.Federal University of Santa Catarina (UFSC)FlorianópolisBrazil

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