Abstract
This paper examines the ambiguity of subjective judgments, which are represented by a system of pairwise preferences over a given set of alternatives. Such preferences are valued with respect to a set of reasons, in favor and against the alternatives, establishing a complete judgment, or viewpoint, on how to solve the decision problem. Hence, viewpoints entail particular decisions coming from the system of preferences, where the preference-based reasoning of a given viewpoint holds according to its soundness or coherence. Here we explore such a coherence under the frame of ambiguity measures, aiming at learning viewpoints with highest preference-score and minimum ambiguity. We extend existing measures of ambiguity into a multi-dimensional fuzzy setting, and suggest some future lines of research towards measuring the coherence or (ir)rationality of viewpoints, exploring the use of information measures in the context of preference learning.
Supported by the Carolina Foundation (short postdoctoral research scholarship), the Government of Spain (grant TIN2015-66471-P), the Government of Madrid (grant S2013/ICE-2845, CASICAM-CM), and Complutense University research group (910149).
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Franco, C., Rodríguez, J.T., Montero, J., Gómez, D., Yager, R.R. (2019). Ambiguity Measures for Preference-Based Decision Viewpoints. In: Seki, H., Nguyen, C., Huynh, VN., Inuiguchi, M. (eds) Integrated Uncertainty in Knowledge Modelling and Decision Making. IUKM 2019. Lecture Notes in Computer Science(), vol 11471. Springer, Cham. https://doi.org/10.1007/978-3-030-14815-7_4
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