Knowledge that Transforms

A process-theoretic approach, seldom used but not without promise for organizational behavior research, is employed to postulate a process model of the natural logic evident in organizational policy making. The model is used to explain how the policies of a sample firm (for which 20 years of data are available) became adopted and how, together with critical events, this caused the firm to evolve in particular directions rather than others. Implications of the study are put forward in terms of identifying pathologies of the policy making process. Some prescriptions are put forward for the proper control of organizations by supervisory bodies, such as boards of directors. It is suggested that Management Science, in the form of systematic procedures for adaptive organizational design and updatable cause maps, may have an important future role to play in senior management affairs.

A key issue in designing a computer-based decision support system is how best to depict the data. Since most systems use either graphical or tabular displays, this experiment compares the latter two when used as decision aids for the production scheduling problem modeled by Holt, Modigliani, Muth, and Simon. The tabular aids and the graphical aids yield equally costly decisions. When the erratic components of the decisions are reduced, the tabular aids outperform the graphical aids.

One of the primary concerns of urban police departments is the effective use of patrol cars. In large cities, police assigned to patrol cars typically account for more than 50% of total police manpower and their allocation has become particularly crucial in light of recent fiscal cutbacks. The police patrol system is a complex multi-server queueing system, and recently many urban police departments have been using queueing models to estimate delays in responding to calls for police assistance. The magnitude of these delays is usually one basis for measuring system efficiency as well as for determining allocations of patrol cars among precincts. A major limitation of these models is that they assume that only a single unit is dispatched to each call. In general, this is not the case, particularly in police departments with one-officer patrol cars. This paper describes a model that has been developed to represent patrol car operations more accurately. It is a multi-priority queueing model that explicitly reflects multiple car dispatches. Its purpose is not only to provide a better basis for the efficient allocation of patrol cars, but to enable police officials to gauge the effects of policies, such as one-officer patrol cars, which affect the number of cars dispatched to various types of incidents.

A comment on the note “ Optimal Ordering Quantity to Realize a Predetermined Level on Profit” by E. Sankarasubramanian and S. Kumaraswamy (in the April 1983 issue).

In previous work, thresholds have been introduced to describe situations where the consumer is indifferent between any two alternatives. Krishnan (Krishnan, K. S. 1977. Incorporating thresholds of indifference in probabilistic choice models. Management Sci. 23 (July) 1224–1233.) formalised thresholds as minimum perceivable differences between the utilities of the alternatives compared and incorporated such thresholds into the binary logit model. The present paper provides an extension and application of the theory to situations where the decision maker is confronted with more than two alternatives. In such situations thresholds can lead to systematic and predictable intransitivity of indifference, violating a basic postulate of utility theory. Explicit probabilities of transitive and intransitive choices are derived within the framework of the multinomial logit model and the stochastic consumer theory associated with this model. The empirical applicability and managerial usefulness of the proposed theory is illustrated using a transportation example with three alternatives. The data employed reflect actual travellers' use of interurban transport modes in Greece. Parameters are estimated by a maximum-likelihood technique, based on a gradient search method. The results support the existence of thresholds. The application has important policy implications.

A lower bound on the total error in an accounting population is required, in conjunction with the point estimate of the total error amount and the upper bound, when adjusting an account to determine the amount of the adjustment. This paper extends the multinomial methodology for obtaining an upper bound on the total overstatement (or understatement) error in an accounting population to the determination of a lower bound on the total overstatement (or understatement) error. The methodology for obtaining a lower multinomial bound differs in several important respects from that for obtaining an upper bound. The proposed lower bound may be computed for up to 25 errors in the sample and provides tighter limits than the widely used Stringer bound.

Health and safety risks are compared with financial risks, and the differences pertinent for risk evaluation and management are described. Such differences arise from the equities of risk distribution, the multiple stakeholders in the decision process, the opportunities for risk diversification, the interpretation of social values relating to risk choices, and in the political objectives in the decision process. Health and safety regulators are faced with the problem of making decisions involving large scientific uncertainties, public perceptions at odds with professional perceptions, uncertain and conflicting public values expressed through the political process, and difficult judgments of equity when risk and benefit are borne by different groups. The effect of highly publicized risk controversies on such regulatory decisions is described.

This paper is a comprehensive study of methods for assessing unidimensional expected utility functions. The paper describes the utility assessment process in decision analysis and then reviews problem formulation, sources of bias in preference judgments, and the analysis of risk attitudes. Two dozen utility assessment methods of which half appear for the first time are critically examined. These methods are grouped into preference comparison methods, probability equivalence methods, value equivalence methods, certainty equivalence methods, hybrid methods, paired-gamble methods, and other approaches. The paper emphasizes the nature of judgmental biases in comparing different assessment procedures. Since most multiattribute utility functions are decomposed into single-attribute functions, this study should facilitate such applications. The paper concludes with several directions for further developmental, empirical, and applied research.

Blood Inventory Management has attracted significant interest from the Operations Research profession during the last 15 years. A number of methodological contributions have been made in the areas of inventory theory and combinatoric optimization that can be of use to other products or systems. These contributions include the development of exact and approximate ordering and issuing policies for an inventory system, the analysis of LIFO or multi-product systems, and various forms of distribution scheduling. In addition, many of these results have been implemented, either as decision rules for efficient blood management in a hospital, or as decision support systems for hierarchical planning in a Regional Blood Center. In this paper we attempt a review of the recent Operations Research contributions to blood inventory management theory and practice. Whereas many problems have been solved, others remain open and new ones keep being created with advances in medical technology and practices. Our approach is not to present an exhaustive review of all the literature in the field, but rather to address several important issues from a unified perspective of theory and practice, and point out new areas for further research.

In the study of large, complex computer simulation models the user is often overwhelmed by the vast number of input variables. Moreover, he or she is usually confused about how to make an effective analysis of the model without performing an excessive number of runs, which tend to be costly and time consuming. Factor screening methods, which attempt to identify the more important variables, can be extremely useful in the study of such models. This paper presents and evaluates two screening strategies based upon random balance sampling. Both strategies are applicable when there are more variables to be screened than there are available screening runs. The results provide guidance in using these strategies in particular screening applications.