🤖 AI Summary
This paper addresses the modeling challenge of “unconceivable uncertainty”—events unforeseeable yet possible—arising in social and life sciences, where classical probability theory fails due to its inherent reliance on known, enumerable possibilities. We propose an extended evidence-theoretic framework that formally distinguishes between uncertainties within and beyond the agent’s cognitive boundary, integrating imprecise probabilities, subadditive measures, and non-standard information-theoretic approaches. Crucially, we establish a novel interface between this framework and multi-agent systems, rigorously differentiating representable from unrepresentable uncertainty sources. The resulting formalism provides a new mathematical foundation and analytical paradigm for studying complex socio-biological systems, particularly in risk perception and cultural information diffusion. (128 words)
📝 Abstract
While Evidence Theory (also known as Dempster-Shafer Theory, or Belief Functions Theory) is being increasingly used in data fusion, its potentialities in the Social and Life Sciences are often obscured by lack of awareness of its distinctive features. In particular, with this paper I stress that an extended version of Evidence Theory can express the uncertainty deriving from the fear that events may materialize, that one is not even able to figure out. By contrast, Probability Theory must limit itself to the possibilities that a decision-maker is currently envisaging. I compare this extended version of Evidence Theory to sophisticated extensions of Probability Theory, such as imprecise and sub-additive probabilities, as well as unconventional versions of Information Theory that are employed in data fusion and transmission of cultural information. A further extension to multi-agent interaction is outlined.
