Information flow and analysis: Theory, simulation, and experiments. I. Basic theoretical and conceptual development
Article first published online: 22 MAR 2007
Copyright © 1981 Wiley Periodicals, Inc., A Wiley Company
Journal of the American Society for Information Science
Volume 32, Issue 3, pages 187–202, May 1981
How to Cite
Yovits, M. C., Foulk, C. R. and Rose, L. L. (1981), Information flow and analysis: Theory, simulation, and experiments. I. Basic theoretical and conceptual development. J. Am. Soc. Inf. Sci., 32: 187–202. doi: 10.1002/asi.4630320305
- Issue published online: 22 MAR 2007
- Article first published online: 22 MAR 2007
- Manuscript Accepted: 18 AUG 1980
- Manuscript Revised: 3 JUN 1980
- Manuscript Received: 18 MAR 1980
- National Science Foundation. Grant Number: GN41628, IST 7621949, and IST 7908327.
This series of articles describes research which has been underway at The Ohio State University in an effort to develop a fundamental and general theory of information flow and analysis. More specifically, the research attempts to (1) identify and quantify important variables and parameters in the information flow process; (2) establish relationships among these variables; (3) apply the theory to practical situations and to examine the resulting implications; and (4) develop models, both simulation and experimental, to utilize and validate the theory. The basis for our work treats information as data of value in decision-making. This, in turn, leads to a powerful model of a Generalized Information System. We have now made considerable progress and have developed the basic elements comprising a generalized theory. In particular, we have been able to establish quantitative definitions for and relationships among: quantity of information, value of information, effectiveness of information, decision-maker effectiveness, decision-maker performance, and other terms. By dealing with “average” decision-makers, we establish unique relationships for specific decision situations. Typical relationships and curves are presented in the articles. We describe a flexible, sophisticated simulation model which permits the examination of the interrelationship between information and decision-making for a wide variety of different situations. We present detailed simulation results for two specific examples-a typical farmer and a mathematical example. We describe prototype experiments which place human decision-makers in an interactive decision-making situation. We are able to obtain data on their decision-making behavior in the light of our conceptual model. Preliminary analysis of the data is described.