Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/221331 
Autor:innen: 
Erscheinungsjahr: 
1991
Schriftenreihe/Nr.: 
Discussion Paper No. 972
Verlag: 
Northwestern University, Kellogg School of Management, Center for Mathematical Studies in Economics and Management Science, Evanston, IL
Zusammenfassung: 
Consider a decision maker who must coordinate his decision with the occurrence of some phenomenon. In order to behave "optimally," the circumstances surrounding the occurrence of the phenomenon must be learned. However, there are natural bounds on the capabilities of perception. More specifically, only a fixed number of attributes may be focused on and observed in each instance. This paper models this problem in the framework of learning concepts from positive examples involving bounded perception. For clarity and simplicity, it is assumed that for each positive example the decision maker may only observe on of its attributes. The analysis concentrates on finding optimal ways of specifying what attributes should be observed. With certain assumptions of independence we show that a class of local "hillclimbing" algorithms are essentially the only optimal ones. Additionally it is shown that patterns in the observation behavior emergence asymptotically. The results underscore the importance of diversifying attention when acquiring knowledge.
Dokumentart: 
Working Paper

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