Public Policy Problems

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


The essence of decision-aiding software is that it is designed to process a set of:
  1. 1.

    goals to be achieved

  2. 2.

    alternatives for achieving them, and

  3. 3.

    relations between goals and alternatives

in order to choose the best alternative, combination, or resource allocation in light of the goals, alternatives, and relations.


Affirmative Action Payoff Matrix Relation Score Plea Bargain Political Feasibility 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Notes and References

  1. 1.
    For further details on optimizing in terms of goals, policies, relations and conclusions, see S. Nagel, Public Policy: Goals, Means, and Methods ( New York: St Martin’s Press, 1984 )Google Scholar
  2. and Edward Quade, Analysis for Public Decisions (Amsterdam: North-Holland, 1983 ). These two books could be referred to for any of the eight principles. The first book uses numerous legal examples.Google Scholar
  3. Other books that take an optimizing perspective toward law include Richard Posner, Economic Analysis of Law (Boston, Mass.: Little, Brown, 1977)Google Scholar
  4. and Gordon Tullock, The Logic of the Law ( New York: Basic Books, 1971 ).Google Scholar
  5. 2.
    On dealing with non-monetary benefits and costs, see Mark Thompson, Benefit-Cost Analysis for Program Evaluation ( Beverly Hills, Calif.: Sage, 1980 );Google Scholar
  6. and Edward Gramlich, Benefit-Cost Analysis of Government Programs (Englewood Cliffs, N.J.: Prentice-Hall, 1981), although they may overemphasize monetizing non-monetary variables, rather than working with them in their original form or close to it.Google Scholar
  7. Also see S. Nagel, “Nonmonetary Variables in Benefit-Cost Evaluation”, Evaluation Review, 7: 37–64 (1983)CrossRefGoogle Scholar
  8. and S. Nagel, “Economic Transformations of Nonmonetary Benefits in Program Evaluation”, in James Catterall (ed.), Economic Evaluation of Public Programs ( San Francisco: Jossey-Bass, 1985 ).Google Scholar
  9. 3.
    On dealing with missing information without having to gather additional information, see Mark Thompson, Decision Analysis for Program Evaluation ( Cambridge, Mass.: Ballinger, 1982 );Google Scholar
  10. and Clifford Harris, The Break-Even Handbook ( Englewood Cliffs, N.J.: Prentice-Hall, 1978 ).Google Scholar
  11. Also see S. Nagel “Dealing with Unknown Variables in Policy/Program Evaluation”, Evaluation and Program Planning 6: 7–18 (1983)CrossRefGoogle Scholar
  12. and S. Nagel, “New Varieties of Sensitivity Analysis”, Evaluation Review, 9: 772–9 (1986).CrossRefGoogle Scholar
  13. 4.
    On diverse methods for dealing with the multiplicity of alternatives in allocation problems, see Philip Kotler, Marketing Decision Making: A Model Building Approach (New York: Holt, 1971) (calculus and statistical analysis);Google Scholar
  14. Claude McMillan, Mathematical Programming: An Introduction to the Design and Application of Optimal Decision Machines (New York: Wiley, 1970) (reiterative guessing and operations research);Google Scholar
  15. and S. Nagel, Policy Evaluation: Making Optimum Decisions (New York: Praeger, 1982) (variations on part/whole percentaging in Chapters 10–13).Google Scholar
  16. Also see S. Nagel, “Optimally Allocating Federal Money to Cities”, Public Budgeting and Finance, 5: 39–50 (1985).CrossRefGoogle Scholar
  17. 6.
    On the matter of simplicity in drawing and presenting conclusions in evaluation analysis see S. Nagel, “Comparing Multi-Criteria Decision Making and P/G% with Traditional Optimizing”, in Yoshikazu Sawaragi (ed.), Multiple-Criteria Decision Making (Berlin: Springer-Verlag, 1987) and “Simplifying Basic Methods”, in Nagel, Public Policy: Goals, Means, and Methods.Google Scholar
  18. On the subject of taking away abused children, see 160 “Neglect”, in George Cooper, et al., Law and Poverty: Cases and Materials ( St Paul: West, 1977 ).Google Scholar
  19. 7.
    On Policy/Goal Percentaging and the P/G% software, see Benjamin Radcliff, “Multi-Criteria Decision Making: A Survey of Software”, Social Science Microcomputer Review, 4: 38–55 (1986);CrossRefGoogle Scholar
  20. S. Nagel, “P/G% Analysis: An Evaluating-Aiding Program”, Evaluation Review, 9: 209–14 (1985);CrossRefGoogle Scholar
  21. and S. Nagel, “A Microcompuier Program for Dealing with Evaluation Problems”, Evaluation and Program Planning, 9: 159–68 (1987).CrossRefGoogle Scholar
  22. On the applicability of P/G% to all five analytic problems, see S. Nagel, Evaluation Analysis with Microcomputers ( Greenwich, Conn.: JAI Press, 1988 ).Google Scholar

Copyright information

© Policy Studies Organisation 1991

Authors and Affiliations

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

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