The Need for Simplicity in Spite of All That Multiplicity

  • Stuart S. Nagel
Part of the Policy Studies Organization Series book series (PSOS)


The purpose of this chapter is to explain how traditional optimizing can be improved by multiple criteria decision-making (MCDM) in general and the Policy/Goal Percentaging (P/G%) variation of MCDM in particular.


Decision Matrix Traditional Optimize Multiple Alternative Reciprocal Causation MCDM Approach 
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Notes and References

  1. 1.
    On Policy/Goal Percentaging, see S. Nagel, “P/G% Analysis: An Evaluation Aiding Program”, Evaluation Review, 9: 209–14 (1985);CrossRefGoogle Scholar
  2. S. Nagel, Evaluation Analysis with Microcomputers ( Beverly Hills, Calif.: Sage, 1987 );Google Scholar
  3. and Benjamin Radcliff, “Multi-Criteria Decision-Making: A Survey of Software”, Social Science Microcomputer Review, 4: 38–55 (1986).CrossRefGoogle Scholar
  4. 2.
    Traditional optimizing methodologies are described in such books as Samuel Richmond, Operations Research for Management Decisions ( New York: Ronald, 1968 );Google Scholar
  5. Warren Erickson and Owen Hall, Computer Models for Management Science ( Reading, Mass.: Addison-Wesley, 1986 );Google Scholar
  6. Elwood Buffa and James Dyer, Management Science/Operations Research: Model Formulation and Solution Methods ( New York: Wiley, 1981 )Google Scholar
  7. and Sang Lee and Laurence Moore, Introduction to Decision Science (Princeton, N. J.: Petrocelli/Charter, 1975 ).Google Scholar
  8. 3.
    The P/G% approach is consistent with incrementalism in policy analysis in view of the emphasis in P/G% on trial-and-error and sensitivity analysis. See Aaron Wildaysky, Speaking Truth to Power: The Art and Craft of Policy Analysis (Boston, Mass.: Little, Brown, 1979 )Google Scholar
  9. and Charles Lindblom, The Policy-Making Process (Englewood Cliffs, N.J.: Prentice-Hall, 1980 ). This approach is also consistent with rationalism in policy analysis in view of its emphasis on systematically determining goals to be achieved, alternatives for achieving them, and relations between goals and alternatives in order to choose the best alternative, combination, or allocation.Google Scholar
  10. See Edward Quade, Analysis for Public Decisions ( Amsterdam: North-Holland, 1983 )Google Scholar
  11. and Duncan MacRae and James Wilde, Policy Analysis for Public Decisions (N. Scituate, Mass.: Duxbury, 1979 ).Google Scholar
  12. 4.
    For further details on MCDM and P/G% applied to prediction rather than prescription, see S. Nagel “Microcomputers and Improving Social Science Prediction”, Evaluation Review, 10: 635–60 (Symposium on “Microcomputers and Evaluation Research”, 1986 );CrossRefGoogle Scholar
  13. S. Nagel “Using Microcomputers and P/G% to Predict Court Cases”, Akron Law Review, 19: 541–74 (1985);Google Scholar
  14. and Samuel Bodily, “Spreadsheet Modeling as a Stepping Stone”, Interfaces, 16: 34–42 (1986).CrossRefGoogle Scholar

Copyright information

© Policy Studies Organisation 1991

Authors and Affiliations

  • Stuart S. Nagel
    • 1
  1. 1.University of IllinoisUSA

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