Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/56588 
Year of Publication: 
2011
Series/Report no.: 
MAGKS Joint Discussion Paper Series in Economics No. 15-2011
Publisher: 
Philipps-University Marburg, Faculty of Business Administration and Economics, Marburg
Abstract: 
Many decision problems in various fields of application can be characterized as diagnostic problems trying to assess the true state (of the world) of given cases. The investigation of assessment criteria improves the initial information according to observed signal outcomes, which are related to the possible states. Such sequential investigation processes can be analyzed within the framework of statistical decision theory, in which prior probability distributions of classes of cases are updated, allowing for a sorting of particular cases into ever smaller subclasses. However, receiving such information causes investigation costs. Besides the question about the set of relevant criteria, this defines two additional problems of statistical decision problems: the optimal stopping of investigations and the optimal sequence of investigating a given set of criteria. Unfortunately, no solution exists with which the optimal sequence can generally be determined. Therefore, the paper characterizes the associated problems and analyzes existing heuristics trying to approximate an optimal solution.
Subjects: 
decision-making
uncertainty
information
Bayesian analysis
statistical decision theory
JEL: 
D80
D81
C11
C44
Document Type: 
Working Paper

Files in This Item:
File
Size
306.73 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.