Eliciting Hybrid Probability-Possibility Functions and Their Decision Evaluation Models
Abstract
We focus on a decision tree model under uncertainty using so-called hybrid probability-possibility functions. They allow to handle behaviours lying between possibilistic decision making and probabilistic decision making while keeping the good properties of both approaches namely Dynamic Consistency, Consequentialism and Tree Reduction. We shed light on the various utility functionals in this setting. More precisely, in this paper, we investigate the question of parameterizing the compromise between possibilistic and probabilisic models in different contexts. To this end, we outline elicitation methods.
Cite
Text
Dubois et al. "Eliciting Hybrid Probability-Possibility Functions and Their Decision Evaluation Models." Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, 2023.Markdown
[Dubois et al. "Eliciting Hybrid Probability-Possibility Functions and Their Decision Evaluation Models." Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications, 2023.](https://mlanthology.org/isipta/2023/dubois2023isipta-eliciting/)BibTeX
@inproceedings{dubois2023isipta-eliciting,
title = {{Eliciting Hybrid Probability-Possibility Functions and Their Decision Evaluation Models}},
author = {Dubois, Didier and Guillaume, Romain and Rico, Agnès},
booktitle = {Proceedings of the Thirteenth International Symposium on Imprecise Probability: Theories and Applications},
year = {2023},
pages = {200-209},
volume = {215},
url = {https://mlanthology.org/isipta/2023/dubois2023isipta-eliciting/}
}