Abstract
We introduce three different approaches for decision making under uncertainty, if (I) there is only partial (both cardinal and ordinal) information on an agent’s preferences and (II) the uncertainty about the states of nature is described by a credal set. Particularly, (I) is modeled by a pair of relations, one specifying the partial rank order of the alternatives and the other modeling partial information on the strength of preference. Our first approach relies on criteria that construct complete rankings of the acts based on generalized expectation intervals. Subsequently, we introduce different concepts of global admissibility that construct partial orders by comparing all acts simultaneously. Finally, we define criteria induced by suitable binary relations on the set of acts and, therefore, can be understood as concepts of local admissibility. Whenever suitable, we provide linear programming based algorithms for checking optimality/admissibility of acts.
Item Type: | Conference or Workshop Item (Paper) |
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Faculties: | Mathematics, Computer Science and Statistics > Statistics Mathematics, Computer Science and Statistics > Statistics > Chairs/Working Groups > Foundations of Statistics and their Applications |
Subjects: | 500 Science > 510 Mathematics |
Language: | English |
Item ID: | 43028 |
Date Deposited: | 09. Apr 2018, 05:54 |
Last Modified: | 25. May 2018, 05:18 |
Available Versions of this Item
- Concepts for Decision Making under Severe Uncertainty with Partial Ordinal and Partial Cardinal Preferences. (deposited 09. Apr 2018, 05:54) [Currently Displayed]