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The article explores the logic of project planning by priorities and relates these to planners' preferences. Priorities, in the simplest case, are derived independently of resource-allocation considerations and override payoffs defined in terms of resources. The article indicates only summarily potentialities of the proposed approach. The principal claim made on its behalf is that priority rankings are empirically ascertainable and that the resulting quantized preference maps can therefore be regarded as practically usable planning tools. They should be particularly useful in planning-programming-budgeting tasks. The approach, moreover, can be extended to other decision problems insofar as these are either naturally quantized or amenable to an approximation in these terms. Such an extension should open the way to empirical work in consumer demand theory, especially in the field of expenditures on durables and major services and in the behavioral theory of the firm, especially in regard to expansion, diversification and merger decisions.

The reduction of population growth rates through family planning programs is being attempted in many of the developing nations of the world. This activity lends itself aptly to mathematical modeling. Building from the well-known difference equation model of population growth, a model is constructed which integrates population dynamics, program activities, and resource consumption. The model may be used predictively to assess the outcome of various program activities. Alternatively, it may be used to determine the pattern of activities which yields the greatest reduction in births under the projected resource constraints. A further use of the model is the identification of the parameters to which predictions are most sensitive; such information provides valuable insights to those gathering the input data. The model is here applied to a family planning program currently in progress. An evaluation of the feasibility of that program's goals is provided, as well as information on limiting resources, data sensitivity, and the most important ages for contraceptive acceptance. [ABSTRACT FROM AUTHOR]