The politics of population

This article suggests some of the principal factors behind the decisions by an increasing number of countries deciding that the achievement of their national objectives required a policy for population, and the way that they are likely to work out. By 1983, 35 developing countries had an official policy to reduce their population growth rate, and in 34 others, the government supported family planning activities--usually for reasons of health or as a human right. The number is remarkable given the many compelling reasons that governments have for not attempting anything so difficult as to modify demographic trends. The future results of population programs, in social and economic terms, are very difficult to quantify, thus defying cost-benefit analysis of the desirability of investing resources in this area, rather than in something else. There are also powerful political reasons why a government might well hesitate before embarking on a policy to reduce the nation's fertility. At the very least, it implies government interference in the most private and personal of human relations, an invasion of human rights, and a disturbance of the traditional patterns of society and behavior. For many countries that are pursuing a policy to limit population growth, the decision has been taken only after the grievous consequences of not having such a policy have already become manifest. The critical question is how soon a government will make the connection among political disobedience, economic and social distress, and the population explosion, and adopt a population policy. Although the number of developing countries that have officially proclaimed a strongly pro-natalist population policy is relatively small, many have Marxist governments. Overall, governments have several strategies at their disposal: 1) improving the accessability and the quality of the service; 2) promoting population education and family planning motivation (with the assistance of the media, folk art, and personal persuasion); and 3) applying a judicious mixture of incentives and disincentives.     

Continuous-time markov decision processes with nonzero terminal reward

In this article we consider a continuous-time Markov decision process with a denumerable state space and nonzero terminal rewards. We first establish the necessary and sufficient optimality condition without any restriction on the cost functions. The necessary condition is derived through the Pontryagin maximum principle and the sufficient condition, by the inherent structure of the problem. We introduce a dynamic programming approximation algorithm for the finite-horizon problem. As the time between discrete points decreases, the optimal policy of the discretized problem converges to that of the continuous-time problem in the sense of weak convergence. For the infinite-horizon problem, a successive approximation method is introduced as an alternative to a policy iteration method.     

An analysis of constrained robust regression estimators

Multicollinearity and nonnormal errors are problems often encountered in the application of linear regression. Estimators are proposed for dealing with the simultaneous occurrence of both multicollinearity and nonnormality. These estimators are developed by combining biased estimation techniques with certain robust criteria. An iteratively reweighted least-squares procedure is used to compute the estimates. The performance of the combined estimators is studied empirically through Monte Carlo experiments structured according to factorial designs. With respect to a mean-squared-error criterion, the combined estimators are superior to ordinary least-squares, pure biased estimators, and pure robust estimators when multicollinearity and nonnormality are present. The loss in efficiency for the combined estimators relative to least squares is small when these problems do not occur. Some guidelines for the use of these combined estimators are given.     

Multiechelon repairable-item provisioning in a time-varying environment using the randomization technique

Multiechelon repairable-item provisioning systems are considered under a time-varying environment. Such conditions could arise, for example, in a military context where a shift from peacetime operation to wartime operation takes place; or, in a civilian setting where a public transit system decides to increase its hours of operation or frequency of service. Exact Markovian models, incorporating a finite population of repairable components and limited repair capacity (nonample service), are treated, with transient solutions obtained using the randomization technique. The exact models are compared with the approximate Dyna-METRIC model which assumes an infinite population of components and ample repair capacity.