A method for measuring decision assumptions
The research reported here deals with finding why people make some choices rather than others, why different people make different decisions in objectively similar situations. The book requires that its reader have some basic knowledge of statistical methods, and, since, it cuts across normally separate fields, it requires an adventuresome spirit. But, in return, the reader may expect to gain the use of a powerful tool that can be applied in his own practical projects and social science research.
The message is on two levels. On one, the work is a practical handbook for application. On the other, it discusses some fundamental issues in the theory of decision-making and the social sciences.
The book presents an application method for measuring assumptions realistic enough for use in management context. In a test-case study, the author uncovered startling diversity in the attributes investors use in picking stocks. More generally, such measures of assumptions are useful in managerial planning and control to aid in decision-making consistence, in learning to revise decision assumptions, and in designing information systems to support decision-making. They are also useful in improving joint decision-making and communication. Still other important applications are possible in consumer market research and in operations research modeling of decision processes. These applications are described with suggestive examples.
To the management scientist the author seeks to show the benefits of extending explicitness beyond the traditional bounds of information systems into the realm of subjective decision assumptions. That is, subjective assumptions made explicit in a practical manner are employed as useful inputs to managerial information systems.
Such measurement methods as reported here may also have widespread use in building social theory. Individual decision assumptions are key variables in microeconomics, in political science, in organization theory, and in the sociology of knowledge. Their measures play an analogous role in social science to that of thermometers in the development of thermodynamics.
The material is developed as follows: First, the problem of discovering the assumptions which underlie decisions is sketched broadly. Alternative possible measurement approaches and theories are then described in logical order. An outline of the method for measuring assumptions is followed by the account of its use in a case study of stock market participants. It is this narrative that provides a practical handbook for the reader's use. A number of prototype applications are shown in some detail. The final chapters propose uses of the method for research in the social sciences and in accounting and the financial markets.
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aggregate algorithm alternatives applications Asamera assumption measures attri attribute factor butes canonical correlation chapter choice set choice-set representation coded coefficient cognitive correlation deci decision assumptions decision nets decision process decision-maker decision-maker's dependent variable derived dimensions earnings eigenvalues estimated example expected explanatory variables explicit F-test factor analysis Factor IE factor loadings factor measures factor scores factor structure Figure growth improved individual interview investment investors labels large number linear Linhart test list of stocks measurement of decision measuring assumptions measuring decision assumptions method microeconomic multicollinearity multidimensional scaling objects observations obtained omitted perceptual defenses potential predictive preference ratings principal components principal components analysis problem procedure Questionnaire raw attribute real-world regression models rejected relevant attributes role repertory test rotated factors sample semantic differential significant sion statistical stepwise regression stock market participants stock price study of stock Subject 22 Table theory tions transform validation volatile