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Decision making with state-dependent preference systems

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Decision making with state-dependent preference systems. / Jansen, Christoph; Augustin, Thomas.
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham: Springer, 2022. ( Communications in Computer and Information Science; Vol. 1601).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Jansen, C & Augustin, T 2022, Decision making with state-dependent preference systems. in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Communications in Computer and Information Science, vol. 1601, Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_59

APA

Jansen, C., & Augustin, T. (2022). Decision making with state-dependent preference systems. In Information Processing and Management of Uncertainty in Knowledge-Based Systems ( Communications in Computer and Information Science; Vol. 1601). Springer. https://doi.org/10.1007/978-3-031-08971-8_59

Vancouver

Jansen C, Augustin T. Decision making with state-dependent preference systems. In Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham: Springer. 2022. ( Communications in Computer and Information Science). doi: 10.1007/978-3-031-08971-8_59

Author

Jansen, Christoph ; Augustin, Thomas. / Decision making with state-dependent preference systems. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Cham : Springer, 2022. ( Communications in Computer and Information Science).

Bibtex

@inproceedings{62509ecacdc248459f1786e6fea39a4c,
title = "Decision making with state-dependent preference systems",
abstract = "In this paper we present some first ideas for decision making with agents whose preference system may depend on an uncertain state of nature. Our main formal framework here are commonly scalable state-dependent decision systems. After giving a formal definition of those systems, we introduce and discuss two criteria for defining optimality of acts, both of which are direct generalizations of classical decision criteria under risk. Further, we show how our criteria can be naturally extended to imprecise probability models. More precisely, we consider convex and finitely generated credal sets. Afterwards, we propose linear pogramming-based algorithms for evaluating our criteria and show how the complexity of these algorithms can be reduced by approximations based on clustering the preference systems under similar states. Finally, we demonstrate our methods in a toy example.",
author = "Christoph Jansen and Thomas Augustin",
year = "2022",
month = jul,
day = "4",
doi = "10.1007/978-3-031-08971-8_59",
language = "English",
isbn = "9783031089701",
series = " Communications in Computer and Information Science",
publisher = "Springer",
booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems",

}

RIS

TY - GEN

T1 - Decision making with state-dependent preference systems

AU - Jansen, Christoph

AU - Augustin, Thomas

PY - 2022/7/4

Y1 - 2022/7/4

N2 - In this paper we present some first ideas for decision making with agents whose preference system may depend on an uncertain state of nature. Our main formal framework here are commonly scalable state-dependent decision systems. After giving a formal definition of those systems, we introduce and discuss two criteria for defining optimality of acts, both of which are direct generalizations of classical decision criteria under risk. Further, we show how our criteria can be naturally extended to imprecise probability models. More precisely, we consider convex and finitely generated credal sets. Afterwards, we propose linear pogramming-based algorithms for evaluating our criteria and show how the complexity of these algorithms can be reduced by approximations based on clustering the preference systems under similar states. Finally, we demonstrate our methods in a toy example.

AB - In this paper we present some first ideas for decision making with agents whose preference system may depend on an uncertain state of nature. Our main formal framework here are commonly scalable state-dependent decision systems. After giving a formal definition of those systems, we introduce and discuss two criteria for defining optimality of acts, both of which are direct generalizations of classical decision criteria under risk. Further, we show how our criteria can be naturally extended to imprecise probability models. More precisely, we consider convex and finitely generated credal sets. Afterwards, we propose linear pogramming-based algorithms for evaluating our criteria and show how the complexity of these algorithms can be reduced by approximations based on clustering the preference systems under similar states. Finally, we demonstrate our methods in a toy example.

U2 - 10.1007/978-3-031-08971-8_59

DO - 10.1007/978-3-031-08971-8_59

M3 - Conference contribution/Paper

SN - 9783031089701

T3 - Communications in Computer and Information Science

BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems

PB - Springer

CY - Cham

ER -