English

Weighted regret-based likelihood: a new approach to describing uncertainty

Artificial Intelligence 2013-09-06 v1

Abstract

Recently, Halpern and Leung suggested representing uncertainty by a weighted set of probability measures, and suggested a way of making decisions based on this representation of uncertainty: maximizing weighted regret. Their paper does not answer an apparently simpler question: what it means, according to this representation of uncertainty, for an event E to be more likely than an event E'. In this paper, a notion of comparative likelihood when uncertainty is represented by a weighted set of probability measures is defined. It generalizes the ordering defined by probability (and by lower probability) in a natural way; a generalization of upper probability can also be defined. A complete axiomatic characterization of this notion of regret-based likelihood is given.

Keywords

Cite

@article{arxiv.1309.1228,
  title  = {Weighted regret-based likelihood: a new approach to describing uncertainty},
  author = {Joseph Y. Halpern},
  journal= {arXiv preprint arXiv:1309.1228},
  year   = {2013}
}

Comments

Appeared in 12th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU)}, 2013, pp. 266--277