Belief Induced by the Partial Knowledge of the Probabilities

TL;DR

This paper develops a belief function framework to quantify an agent's beliefs about events when only partial knowledge of the underlying probability distribution is available.

Contribution

It introduces a method to construct belief functions based on partial probability information, advancing the understanding of belief modeling under uncertainty.

Findings

Provides a formal construction of belief functions from partial probability data

Enables quantification of beliefs with incomplete probabilistic information

Enhances decision-making models under uncertainty

Abstract

We construct the belief function that quantifies the agent, beliefs about which event of Q will occurred when he knows that the event is selected by a chance set-up and that the probability function associated to the chance set up is only partially known.