Stable International Environmental Agreements with a Stock Pollutant,Uncertainty and Learning

In this paper I address the question of how uncertainty about damage costs and the possibility of resolving that uncertainty in the future affects the incentives for countries to join an international environmental agreement. I use a two-period model with a stock pollutant where the number of countries generating pollution can be arbitrarily large. The stability concept employed is such that size of the stable IEA can be anywhere between 2 and the grand coalition of all countries depending on parameter values. The dynamic structure allows two different membership rules for an IEA: fixed (countries commit at the outset to be members for both periods) or variable (countries decide each period whether to join). I show that with fixed membership learning results in at least as high membership and global welfare as no learning (unless both the expected value and variance of damage costs are high). With variable membership, learning leads to higher membership (in the second period) but lower global welfare than no learning. For most parameter values variable membership results in higher global welfare than fixed membership.     

Assisted reproductive technologies (ART) in a model of fertility choice

We examine the relationship between assisted reproductive technologies (ART) and the microeconomics of fertility choice. Along the way, we develop a model consistent with between-country differences in overall fertility and fertility timing. Our analysis of ART centers around the distinction between biomedical and behavioral effects. While improvements in ART have the biomedical effect of raising fertility, they may cause some women who would otherwise have tried to have children earlier on in life to postpone childbirth to later in life when the conception success probability is lower. This behavioral effect of postponement may reduce the fertility rate.     

The application of dynamic programming in United Kingdom companies

Dynamic Programming (or DP as it is commonly known) is a mathematical programming method which would appear to be a very powerful technique for use in management decision problems. A great number of theoretical texts have been written on the mathematics of DP and a few articles have been published on the more practical aspects, but DP has remained very much on the theoretical shelf as far as practising management has been concerned. This paper gives the results of a survey carried out at the beginning of 1972 and is intended to provide some insight into the use of DP in real management problems in U.K. companies and to show what sort of problems are apparently restricting its use. The findings demonstrate that several firms have used DP in various applications with considerable success. There are also some enlightening comments on the difficulties involved, and on the future potential of DP in industry.