Information flow and analysis: Theory, simulation, and experiments. I. Basic theoretical and conceptual development

Authors

  • M. C. Yovits,

    1. Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210
    Current affiliation:
    1. University School of Science, Indiana University-Purdue University at Indianapolis, Indianapolis, Indiana 46205
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  • C. R. Foulk,

    1. Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210
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  • L. L. Rose

    1. Department of Computer and Information Science, The Ohio State University, Columbus, OH 43210
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Abstract

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.

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