Standard
Decision Theory Meets Linear Optimization Beyond Computation. /
Jansen, Christoph; Schollmeyer, Georg; Augustin, Thomas.
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings. ed. / Alessandro Antonucci; Laurence Cholvy; Odile Papini. Cham: Springer, 2017. p. 329-339 (Lecture Notes in Computer Science; Vol. 10369).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Jansen, C, Schollmeyer, G & Augustin, T 2017,
Decision Theory Meets Linear Optimization Beyond Computation. in A Antonucci, L Cholvy & O Papini (eds),
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10369, Springer, Cham, pp. 329-339.
https://doi.org/10.1007/978-3-319-61581-3_30
APA
Jansen, C., Schollmeyer, G., & Augustin, T. (2017).
Decision Theory Meets Linear Optimization Beyond Computation. In A. Antonucci, L. Cholvy, & O. Papini (Eds.),
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings (pp. 329-339). (Lecture Notes in Computer Science; Vol. 10369). Springer.
https://doi.org/10.1007/978-3-319-61581-3_30
Vancouver
Jansen C, Schollmeyer G, Augustin T.
Decision Theory Meets Linear Optimization Beyond Computation. In Antonucci A, Cholvy L, Papini O, editors, Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings. Cham: Springer. 2017. p. 329-339. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-61581-3_30
Author
Jansen, Christoph ; Schollmeyer, Georg ; Augustin, Thomas. /
Decision Theory Meets Linear Optimization Beyond Computation. Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 14th European Conference, ECSQARU 2017, Lugano, Switzerland, July 10–14, 2017, Proceedings. editor / Alessandro Antonucci ; Laurence Cholvy ; Odile Papini. Cham : Springer, 2017. pp. 329-339 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{0b49493460df45dcad49f151881498c7,
title = "Decision Theory Meets Linear Optimization Beyond Computation",
abstract = "The paper is concerned with decision making under complex uncertainty. We consider the Hodges and Lehmann-criterion relying on uncertain classical probabilities and Walley{\textquoteright}s maximality relying on imprecise probabilities. We present linear programming based approaches for computing optimal acts as well as for determining least favorable prior distributions in finite decision settings. Further, we apply results from duality theory of linear programming in order to provide theoretical insights into certain characteristics of these optimal solutions. Particularly, we characterize conditions under which randomization pays out when defining optimality in terms of the Gamma-Maximin criterion and investigate how these conditions relate to least favorable priors.",
author = "Christoph Jansen and Georg Schollmeyer and Thomas Augustin",
year = "2017",
month = jun,
day = "15",
doi = "10.1007/978-3-319-61581-3_30",
language = "English",
isbn = "9783319615806",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "329--339",
editor = "Alessandro Antonucci and Cholvy, {Laurence } and Odile Papini",
booktitle = "Symbolic and Quantitative Approaches to Reasoning with Uncertainty",
}
RIS
TY - GEN
T1 - Decision Theory Meets Linear Optimization Beyond Computation
AU - Jansen, Christoph
AU - Schollmeyer, Georg
AU - Augustin, Thomas
PY - 2017/6/15
Y1 - 2017/6/15
N2 - The paper is concerned with decision making under complex uncertainty. We consider the Hodges and Lehmann-criterion relying on uncertain classical probabilities and Walley’s maximality relying on imprecise probabilities. We present linear programming based approaches for computing optimal acts as well as for determining least favorable prior distributions in finite decision settings. Further, we apply results from duality theory of linear programming in order to provide theoretical insights into certain characteristics of these optimal solutions. Particularly, we characterize conditions under which randomization pays out when defining optimality in terms of the Gamma-Maximin criterion and investigate how these conditions relate to least favorable priors.
AB - The paper is concerned with decision making under complex uncertainty. We consider the Hodges and Lehmann-criterion relying on uncertain classical probabilities and Walley’s maximality relying on imprecise probabilities. We present linear programming based approaches for computing optimal acts as well as for determining least favorable prior distributions in finite decision settings. Further, we apply results from duality theory of linear programming in order to provide theoretical insights into certain characteristics of these optimal solutions. Particularly, we characterize conditions under which randomization pays out when defining optimality in terms of the Gamma-Maximin criterion and investigate how these conditions relate to least favorable priors.
U2 - 10.1007/978-3-319-61581-3_30
DO - 10.1007/978-3-319-61581-3_30
M3 - Conference contribution/Paper
SN - 9783319615806
T3 - Lecture Notes in Computer Science
SP - 329
EP - 339
BT - Symbolic and Quantitative Approaches to Reasoning with Uncertainty
A2 - Antonucci, Alessandro
A2 - Cholvy, Laurence
A2 - Papini, Odile
PB - Springer
CY - Cham
ER -
