This paper seeks to get behind specific contextual referents of risky situations to consider characteristics of risk that apply to many situations. It is guided by previous theoretical and empirical research in perceived risk, and focuses on the joint effects on risk of loss probability and the distribution of losses. The approach taken follows modern axiomatic theory by proposing conditions on a relation “is at least as risky as” between pairs of probability distributions over an outcome variable. Several sets of axioms for risk that characterize different forms for risk measurement are presented.

Independence axioms similar to those proposed for multiattribute von Neumann–Morgenstern linear utility theory are examined in the context of new nonlinear utility theories developed by Chew and MacCrimmon, and Fishburn. These new theories weaken the independence axiom of von Neumann and Morgenstern, and Fishburn's theory does not require preferences to be transitive. The paper shows that axioms for independence between attributes lead to decompositions of utility for the nonlinear theories that are related to standard decompositions for linear utility.

This paper shows how an operational method for solving dynamic programs can be used, in some cases, to solve the problem of maximizing a firm's market value. The problem is formulated as a Markov decision problem that can be solved via linear programming. The paper shows how to calculate (or estimate) the state-contingent prices that are used to value the firm. In addition, the paper points out how states can be aggregated to make the solution technique more practical. The paper's final section contains a specific example.

The normative solution concepts of game theory try to provide a clear mathematical characterization of what it means to act rationally in a game where all players expect each other to act rationally. Kadane and Larkey reject the use of these normative solution concepts. Yet, this amounts to throwing away an important piece of information to the effect that the players are rational and expect each other to be rational. Even in situations where the players do not expect each other to act with complete rationality, normative game theory can help them heuristically to formulate reasonable expectations about the other players' behavior.

Most office equipment is maintained by service organizations consisting of teams of service representatives. An important problem for the service organizations is the determination of the spare parts inventory to be carried by each service representative. This note presents a model for finding the spare parts kit that has the minimum inventory investment for a specified job completion criterion. The model is shown to be equivalent to a binary knapsack problem. The model is contrasted with the model developed by Smith, Chambers and Shlifer for a similar problem.

This paper develops two distinct models for studying inventory systems with continuous production and perishable items. The perishable items have a deterministic usable life after which they must be outdated. For each of the models, analytical expressions derived from queueing theory, are found for the steady-state distribution of system inventory. Knowledge of this steady-state behavior may be used for evaluation of system performance, and for consideration of alternatives for improving system performance. Both models assume that inventory is replenished by a continuous production process. The first model, assuming continuous inventory units, has Poisson demand requests with the size of each request distributed as an exponential random variable. The second model has Poisson demand requests with all demands being for a single unit. The analysis for both models exploits the similarity of the inventory system with a single-server queueing system.

John C. Harsanyi's rejoinder to comments and replies to Kadane, J. B., P. D. Larkey. 1982. Subjective probability and the theory of games. Management Sci. 28 (2) 113–120 and Kadane, J. B., P. D. Larkey. 1982. Reply to Professor Harsanyi. Management Sci. 28 (2) 124.

This paper proposes and tests a procedure for decomposing a large scale production planning problem modeled as a mixed-integer linear program. We interpret this decomposition in the context of Hax and Meal's hierarchical framework for production planning. The procedure decomposes the production planning problem into two subproblems which correspond to the aggregate planning subproblem and a disaggregation subproblem in the Hax-Meal framework. The linking mechanism for these two subproblems is an inventory consistency relationship which is priced out by a set of Lagrange multipliers. The best values for the multipliers are found by an iterative procedure which may be interpreted as a feedback mechanism in the Hax-Meal framework. At each iteration, the procedure finds both a lower bound on the optimal value to the production planning problem and a feasible solution from which an upper bound is obtained. Our computational tests show that the best feasible solution found from this procedure is very close to optimal. For thirty-six test problems the percentage deviation from optimality never exceeds 4.4%, and the average percentage deviation is 2.2%. In addition, these best feasible solutions dominate the corresponding solutions obtained by a hierarchical procedure.

A method is developed to calculate an (s, S) policy that minimizes the average stationary cost in an inventory system with: constant lead time, fixed order cost, linear holding cost per unit time, linear penalty cost per unit short, discrete compound Poisson demand, and lost sales. It is assumed that at most one order is outstanding, or equivalently that S − s > s. The procedure is compared to several easier-to-compute approximate solutions, including one that assumes backordering. Examples are given that reveal that although the backorder solution is different than the lost sales one, the cost penalty of using it in the lost sales situation is small.

Since the early 1970s, it has been realized that rapid economic development in developing countries leads to an acute inequality in income distribution. To prevent massive dissatisfaction among their citizens, developing countries were urged to achieve economic growth (particularly industrial growth) with distribution of income as their development goal. A good way of promoting growth and dispersal of industrial activities is the establishment of industrial estates in the locations where such activities are desired. This paper formulates the problem of optimal development of industrial estates, with the incorporation of specified minimum levels of development in poverty (priority) sites as distributive targets, as encountered by a Malaysian state government. The linear programming problem so formulated is then shown to be equivalent to a transportation problem, enabling it to be solved and parametically analyzed efficiently. Computational results, obtained using real-life data, show that the subsidy incurred in fulfilling the distributive targets is small compared to the total revenue generated. This justifies the imposition of the distributive targets on the development process. Further, the optimal policy was found to involve decisions to be taken in the initial years of the planning horizon that are fairly insensitive to variation in demand and cost parameters, thereby demonstrating the relative “goodness” of the optimal policy for industrial estate development in the state.