This paper examines Markovian decision processes in which the transition probabilities corresponding to alternative decisions are not known with certainty. The processes are assumed to be finite-state, discrete-time, and stationary. The rewards axe time discounted. Both a game-theoretic and the Bayesian formulation are considered. In the game-theoretic formulation, variants of a policy-iteration algorithm are provided for both the max-min and the max-max cases. An implicit enumeration algorithm is discussed for the Bayesian formulation where upper and lower bounds on the total expected discounted return are provided by the max-max and max-min optimal policies. Finally, the paper discusses asymptotically Bayes-optimal policies.

The introduction of new contraceptive technologies has great potential for expanding contraceptive choice, but in practice, benefits have not always materialized as new methods have been added to public-sector programs. In response to lessons from the past, the UNDP/UNFPA/WHO/World Bank Special Programme of Research, Development, and Research Training in Human Reproduction (HRP) has taken major steps to develop a new approach and to support governments interested in its implementation. After reviewing previous experience with contraceptive introduction, the article outlines the strategic approach and discusses lessons from eight countries. This new approach shifts attention from promotion of a particular technology to an emphasis on the method mix, the capacity to provide services with quality of care, reproductive choice, and users' perspectives and needs. It also suggests that technology choice should be undertaken through a participatory process that begins with an assessment of the need for contraceptive introduction and is followed by research and policy and program development. Initial results from Bolivia, Brazil, Burkina Faso, Chile, Myanmar, South Africa, Vietnam, and Zambia confirm the value of the new approach.

This is a study of finite state discrete time discounted Markovian decision process when the states are probabilistically observed. A model of this process is formulated, and an implicit enumeration algorithm is presented which optimizes the total expected discounted reward given the initial state. Several numerical examples are presented.

The Indian family welfare program has offered financial incentives since the early 1960s to both family planning motivators and acceptors of sterilization and the IUD. This article reviews the available evidence regarding the impact of incentives on the quality and quantity of family planning services in India. Administrative concerns related to the implementation of incentive programs are discussed, and the current debate on disincentives, as well as the brief period when disincentives were used, is summarized. The studies reviewed, though few in number and varying in quality and methodology, indicate that incentives to acceptors help to increase the level of contraceptive acceptance, especially when they are part of a well designed strategy of service delivery and client motivation. Incentives do not appear to have an adverse effect on quality of services and acceptors, and they do not seem to influence method choice. Disincentives, if they are used, should not impinge on fundamental individual rights of either the parents or the child.

Considerable attention is being paid to improving quality of care in public health systems because of both the need to respond to customer concerns and cost consider ations. Many governments, following industrial organisations, have been attempting to implement total quality management (TQM). The experience of TQM implementa tion has been mixed, however. The paper reviews the Malaysian experience of imple menting TQM. It also reviews the experience of interventions to improve quality of care in family planning and reproductive health programmes to illustrate what is needed to improve quality of care for public health programmes. Based on the review the paper suggests that an incremental approach, realistic expectations, building on what exists, and persistence would result in reduced frustrations in improving quality of care and ultimately lead to TQM.