Title: Beliefs based on conflicting and uncertain evidence: connecting Dempster-Shafer theory and the topology of evidence

Abstract:
One problem to solve in the context of information fusion, decision-making, and other artificial intelligence challenges is to compute justified beliefs based on evidence. In real-life examples, this evidence may be inconsistent, incomplete, or uncertain, making the problem of evidence fusion highly non-trivial. In this talk, I will present a new model for measuring degrees of beliefs based on possibly inconsistent, incomplete, and uncertain evidence, by combining tools from Dempster-Shafer Theory and Topological Models of Evidence. Our belief model is more general than the aforementioned approaches in two important ways: (1) it can reproduce them when appropriate constraints are imposed, and, more notably, (2) it is flexible enough to compute beliefs according to various standards that represent agents’ evidential demands. The latter novelty allows to compute an agent’s (possibly) distinct degrees of belief, based on the same evidence, in situations when, e.g, the agent prioritizes avoiding false negatives and when it prioritizes avoiding false positives. Finally, I will discuss further research directions and philosophical loose ends, time permitting, introduce a logic for comparing strengths of evidence and belief.

This talk is joint work between the speaker (Aybüke Özgün), Daira Pinto Prieto and Ronald de Haan, and is based on the underlying topological formalism developed in collaboration with Alexandru Baltag, Nick Bezhanishvili, and Sonja Smets.

The speaker notes that “The talk will not assume any background in Dempster-Shafer theory or topological models for evidence.” And that “[she] will focus on the motivation and overall structure of the proposed framework rather than the technical details.”