Titlebook: Response Models for Detection of Change; Amnon Rapoport,William E. Stein,Graham J. Burkheim Book 1979 Springer Science+Business Media Dord

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Introduction,malism are necessary. Didactically, however, it seems preferable to start our presentation with a few examples. Thus, without undue formalism, three examples are briefly described below both to motivate the entire enterprise and to illustrate the scope and some of the major features of the decision situations examined.

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Extensions,t .(.): є. is a Markov process and that for a given value of .(.), the process .(.) consists of a sequence of independent random variables. The DM is required to make sequential decisions to optimally control (with respect to a given loss structure, an objective criterion, and well-defined constraints) the process .(.), while observing only .(.).

使显得不重要

­ duction control situations, and share three major features. First, the states of the process are not directly observable but become gradually known with the sequential acquisition of fallible information over time. Second, the mechanism that generates the fallible information is not stationary; ra

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A Response Model with a Fixed Number of Successive Observations,example, is the case in the psychophysical experiment to be discussed in Chapter 8. To reduce the DM’s cognitive load, or perhaps better account for the decision rule that some DMs may adopt, the present chapter proposes another decision rule based on a .ixed .umber of successive .servations.

Synchronism

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A Response Model with a Fixed Number of Observations,hey have taken. It is the purpose of the present chapter to introduce another deterministic response model based on a .ixed .umber of .servation values, subsequently termed model FNOB, and to study its properties. An even simpler response model will be introduced and studied in Chapter 5.

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