Economics and Information Theory

This is Professor Bailey's fourth book dealing with various mathematical aspects of biology. Like the others, it is confined mostly to the biometrical, probabilistic, and statistical aspects. Even so, many common, underlying mathematical principles and points of view are skillfully and somewhat leisurely expounded, in close relationship to many interesting and pertinent examples. The book may be criticized by strict determinists because of its preoccupation with stochastic representations. However, where deterministic and stochastic models occur as alternatives, he does a creditable job of presenting, relating, and comparing both. The book is divided into two parts. The first serves as a general introduction to probabilistic biomathematics and, more particularly, to the selected mathematical models of part 2. Its six chapters are devoted to the need for mathematical representation in biological and other scientific studies, probabilistic and statistical reasoning, mathematics and the scientific method, the operational research method, the influence of computers,

The Mathematical Approach to Biology and Medicine

Mobility in science

The methods of maximum entropy and minimum information are employed in order to provide unbiased a priori estimates of the form of probability distributions, given limited prior knowledge. These a priori estimates, in turn, can be cast in the form of theoretical models of the phenomena of interest; models to be tested against real world data. As such, the methods of maximum entropy and minimum information have relevance to any scientific or social-scientific discipline wherein the mathematical modelling of probability distributions is a concern. At the same time, however, the methods are mathematically difficult and conceptually demanding. This paper therefore presents a pedagogic introduction to the methods of maximum entropy and minimum information in the hope of making them more accessible to those social scientists who might find them relevant to their work. Using a simple example, the paper works through the two methods, one at a time, adding more and more information to the estimation problem as the discussion proceeds. The focus herein is a geographic one, yet the example employed is easily transferable to alternative contexts.

Information theoretic methods of spatial model building: A guide to the unbiased estimation of the form of probability distributions†☆

Mobility and career outlook for physicists

Information theory is employed to look at certain aspects of migration. It is suggested that the dividedness of a population into “movers” and “stayers” is better assessed by migration entropy. The notion of “migration inequality” is introduced and the principle of minimum entropy suggested as a criterion for fitting migrations models. Canadian census data are utilized for illustration purposes.

Information theoretic approach to migration analysis

A deterministic model is presented to show that if diffusion of family planning ideas takes place on a person-to-person level they can be imparted more effectively to reach a large proportion of the community than if a constant source of diffusion is presented. The model assumes the latent period to be zero the infectious period large incubation period and serial interval zero. Incentives should be administered to those eligibles who practice family planning during the period when the information dissemination is taking place. In the field identification of the mixing groups and identification of the grass roots leaders is necessary for such a program to succeed. Since no experimental work has been done it is not known how well such a program would fare.

P. Krishnan

Papers

Information theoretic approach to migration analysis

Preliminary report on an epidemic model approach to the propagation of family planning ideas.