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This note presents a description of a time-sharing computer program written to implement a man-machine interactive algorithm for the solution of the multiple criteria problem. The interactive algorithm was suggested in a recent paper by Geoffrion, "Vector Maximal Decomposition Programming," Working Paper No. 164, Western Management Science Institute, University of California, Los Angeles, September 1970. A unique feature of this program is the man-machine dialog which obtains information from the decision-maker through a series of simple, ordinal comparisons. [ABSTRACT FROM AUTHOR]

In a team decision problem there are two or more decision variables, and these different decisions can be made to depend upon different aspects of the environment, or information variables, the resulting payoff being a random variable. The choice of optimal rules for selecting information variables and for making decisions is the central problem of the economic theory of teams. This paper shows, by means of an example, how linear programming can be applied to obtain optimal team decision functions in the case in which the payoff to the team is a convex polyhedral function of the decision variables. [ABSTRACT FROM AUTHOR]

This paper throws doubt on the direct relationship between the difficulty of a decision problem and decision time. A formal theory is proposed, hypothesizing that decision makers take longer to choose from four alternatives when two of the alternatives are easily rejected than when all four alternatives are equal. The results of laboratory experiments support this hypothesis, and suggest that decision behavior is related to personality factors. [ABSTRACT FROM AUTHOR]

A critical problem, continually faced by managers in developing "effective" resource allocation plans, is that only limited partial information is available about the system under consideration on which to base these plans. This is particularly true in planning involving the limited information typically available on the relevant behavior and characteristics of large human populations. This paper develops the insight that partial information--which at first scrutiny appears to contain only severely limited information about the system--in many important cases actually contains (or stores) a great deal of the relevant total system information needed for planning (or control). This insight leads to powerful methods for retrieving the needed information about the behavior and characteristics of the total system from the information contained in the partial information data. In the present paper, we illustrate these information retrieval methods by developing and applying them to partially specified media audience exposure systems to retrieve certain critical total system information needed for media planning. More specifically, partial information on the audience of a combination of advertising media vehicles consisting of only two information quantities--the sum of the individual audiences, and the sum of the audiences of all pairs of media vehicles in the combination--is shown, in fact, to contain a significant amount of the information about the audience exposure pattern of the combination that is needed for media planning evaluation. Theory and methods are developed which retrieve, from these two quantities, operationally useful information on the unduplicated audience, the average exposure frequency, and the frequency distribution of audience exposure of the combination. The retrieval methods developed in this paper have been successfully applied to evaluating and developing "effective" media selection programs. These retrieval methods... [ABSTRACT FROM AUTHOR]

In linear programming under uncertainty the two-stage problem is handled by assuming that one chooses a first set of constrained decision variables; this is followed by observations of certain random variables after which another set of decisions must be made to adjust for any constraint violations. The objective is to optimize an expected value functional defined relative to the indicated choices. This paper shows how such problems may always be replaced with either constrained generalized medians or hypermedians in which all random elements appear only in the functional. The resulting problem is called a deterministic equivalent for the original problem since (a) the originally defined objective replaces all random variables by corresponding expected values and (b) the remaining constraints do not contain any random terms. Significant classes of cases are singled out and special attention is devoted to the structure of the constraint matrices for these purposes. Numerical examples are supplied and related to the previous literature. Other properties of these models are also examined and related to types of problems which are often of interest. For instance the hypermedian and generalized median formulations involve minimizations over absolute value terms in the functional. These, in turn, are developed for their possible pertinence in problems where minimizations are to be over the maximum of a set of functions under inequality constraints. Utilizing Moore-Penrose (generalized) inverses, other characterizations are also secured in which all relevant weights and coefficients are stated explicitly in terms of original data. [ABSTRACT FROM AUTHOR]

This paper describes a dynamic programming model that has been developed to determine an optimal expansion plan for the generating capacity of an electric power system. The optimization model determines the least-cost mix of capacity between hydro, nuclear, thermal and peaking turbine plants, the size of the plants to add to the system, and the timing of these additions. We show how the computational requirements of this four-state-variable, four-decision-variable problem can be substantially reduced, resulting in a computationally feasible model. The techniques developed are applicable to a large class of capital budgeting problems under uncertainty. Reference is also made to the actual application of the model and an example is presented. [ABSTRACT FROM AUTHOR]

Information-processing is an integral part of decision-making; yet the state of knowledge concerning the individual problem solver in his role as an information processor is limited. This paper presents a conceptual framework for examining individual, task-oriented information-processing as a multi-stage process. Relationships between variables in the model are investigated at a research site which provides a number of the advantages of both a laboratory and a field environment. The "information element" is used as the basic unit of analysis. Data are examined via the multivariate statistical technique of canonical analysis. The implications of the study are also discussed. [ABSTRACT FROM AUTHOR]

The general linear programming problem is considered in which the coefficients of the objective function to be maximized are assumed to be random variables with a known multinormal distribution. Three deterministic reformulations involve, respectively, maximizing the expected value, the α,-fractile (α fixed, 0 < α < ½), and the probability of exceeding a predetermined level κ of payoff. In this paper the author's previous work on "hi-criterion programs" is specialized to give an algorithm for routinely and efficiently solving the second and third reformulations. A by-product of the calculations in each ease is the tradeoff-curve between the criterion being maximized and expected value. The intimate relationships between all three reformulations are illuminated, with the cumulative effect of considerably lessening the burden on the decision-maker to preselect with finality a particular value of α or κ. [ABSTRACT FROM AUTHOR]

A general procedure is given for evaluating various alternatives (e.g., investments, projects, systems, etc.) involving two criteria of evaluation. The method involves the detailed questioning of decision makers as to preferences between various combinations of pre-selected sets of alternatives under various conditions. From the responses to such questions, a mathematical function which can be applied repeatedly for implementing choices between other alternatives is established. An example of the application of the procedure is given, along with discussion of certain ramifications of the example. In addition to the specific application, potential extensions of the technique are discussed briefly. [ABSTRACT FROM AUTHOR]

The use of goal programming for the solution of the multiple criteria problem is discussed. An extension of this use is presented in the form of an algorithm which requires interaction with the relevant decision-maker, in order to obtain certain information regarding his utility function defined over the permissible values of the criteria. This algorithm provides a bridge between goal programming and recently suggested interactive strategies for the optimization of the multiple criteria problem. [ABSTRACT FROM AUTHOR]

A model is developed that portrays certain types of interactions as well as main effects in consumer evaluation tasks. The model is illustrated with menu preference data and its potential use in other types of multi-attribute choice situations is described. [ABSTRACT FROM AUTHOR]

A situation is described in which large amounts of physician time are being spent reviewing patient records. In order to promote efficient use of their time, careful selection of the cases they review is called for. A methodology is developed to elicit criteria from physicians for the use of nonphysicians in case selection. The methodology, called the direct approach to approximate decision models, involves the use of interviews and questionnaires asking the physician directly about the criteria he uses, rather than inferring his criteria by indirect means. He is asked to interpret these criteria in a way that will allow a nonphysician to use them for case selection so that the resulting model is an approximation to the physician's decision process. [ABSTRACT FROM AUTHOR]

In this study, five major criteria (realism, flexibility, capability, ease of use, and cost) for evaluating the suitability of management science models for R&D project selection are established, through personal interviews with R&D administrators and management scientists. A suitability rating system, based on these criteria, is used by the author to rate twenty-six management science models for their suitability of use in R&D project selection decision making. Profitablility index and scoring types of models were found to have generally higher ease of use and lower cost performances than the other types. The linear and nonlinear models had generally higher flexibility, while the linear, nonlinear and zero-one models had generally higher realism than the other model types. [ABSTRACT FROM AUTHOR]

Presents news briefs on new social science programs at U.S. and Canadian universities as of February 2004. The University of South Carolina College of Business Administration will sponsor a seminar on Multiple Criteria Decision Making on October 26-27, 1972. The major goal of the seminar is to stimulate further research and practical developments in areas including the multiple objectives of the corporation in modern society, the theory of macroeconomic stabilization policy, multi-attributed utility theory, and behavioral aspects of decision making under multiple goals. The University of North Carolina at Greensboro announces a seminar workshop on New Pressures on Family Relationships on June 12-23, 1972. Two hours graduate credit will be offered to qualified students. The University of Alberta is offering a Master of Arts program in corrections. Background in law or social sciences preferred. Work experience in a Canadian correctional or social work setting highly desirable. Program is interdisciplinary but emphasizes law and sociology. Yale University Program in Law and Modernization offers fellowships to both senior scholars and graduate students for pursuing research and study in the area.