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Can I read Biases in Multi-criteria, Satisficing Decisions Due to Data Errors on EtoBox?
Biases in Multi-criteria, Satisficing Decisions Due to Data Errors by Irit Askira Gelman is a Computer Science article available to read on EtoBox.
What is Biases in Multi-criteria, Satisficing Decisions Due to Data Errors about?
This inquiry centers on an asymmetry, or bias, in the accuracy of multi-criteria, conjunctive, and disjunctive decisions, which originates from fundamental properties of the logical conjunction and disjunction operations. A mathematical-statistical analysis indicates that, as we keep adding criteria to a multi-criteria conjunctive or disjunctive decision rule, errors in the data produce decision errors asymmetrically. As a result, in conjunctive decisions, the probability of a false negative increases while the probability of a false positive decreases. In contrast, in disjunctive decisions, as we keep adding criteria, the probability of a false positive increases while that of a false negative decreases. For instance, in a conjunctive business decision rule, the probability of overlooking a bargain can be far greater than the probability of misjudging an unattractive offer to be a good one. A series of Monte Carlo simulations validates the analytical findings and explores the contribution of several additional factors.
Who reads Biases in Multi-criteria, Satisficing Decisions Due to Data Errors?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Irit Askira Gelman
- Publisher
- ACM
- Published
- 2012
- Language
- EN
- Field
- Computer Science (Physical Sciences)