Imprecise Probability

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9 papers0 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Imprecise Belief Fusion Improves Multi-agent Social Learning
Aug 2, 2026Zixuan Liu, Jonathan Lawry, Michael CrosscombeImprecise Probability
School of Engineering Mathematics and Technology, University of Bristol, Bristol, BS8 1TW, UK · Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-8902, Japan
Propagation of~~Interval Belief Structures and~~Imprecise Copulas for~Neural Network Verification
Jun 29, 2026Francesc Pifarre-Esquerda, Eric Goubault, Sylvie PutotNeural Network Verification Uncertainty Quantification
LIX · LIX, CNRS, École polytechnique, Institut Polytechnique de Paris, Palaiseau, France · X-DEP-INFO
Value of Information under Imprecise Probabilities: Decision-Rule-Specific Values and Fixed-Measure Envelopes on a Credal Set
Jun 26, 2026Rowan IskandarValue of Information Imprecise Probability
Medtronic Trading Sàrl, Tolochenaz, VD, Switzerland · Center for Evidence Synthesis in Health, Brown University School of Public Health, Providence, RI, USA
Unbiased Canonical Set-Valued Oracles Via Lattice Theory
Jun 24, 2026Jobst HeitzigImprecise Probability Performative Prediction
PIK-Potsdam
Conformalised imprecise inference for robust extrapolation under limited data
May 25, 2026Yu Chen, Scott FersonDistribution Shift Robustness Uncertainty Quantification
Institute for Risk and Uncertainty · University of Liverpool · Liverpool
Upper Entropy for 2-Monotone Lower Probabilities
Mar 23, 2026Tuan-Anh Vu, Sébastien Destercke, Frédéric PichonImprecise Probability Uncertainty Quantification
UMR CNRS 7253 Heudiasyc, Université de Technologie de Compiègne, France · Laboratoire de Génie Informatique et d’Automatique de l’Artois (LGI2A), Université d’Artois, UR 3926, France
The Geometry of Knowing: From Possibilistic Ignorance to Probabilistic Certainty -- A Measure-Theoretic Framework for Epistemic Convergence
Mar 13, 2026Moriba Kemessia JahImprecise Probability Epistemic Uncertainty
Black Swan Research Group, GaiaVerse, Ltd., Austin, TX 78728, USA · Jah Decision Intelligence Group, Aerospace Engineering & Engineering Mechanics The University of Texas at Austin, Austin, TX 78712
Verbalizing LLM's Higher-order Uncertainty via Imprecise Probabilities
Mar 11, 2026Anita Yang, Krikamol Muandet, Michele Caprio +2Imprecise Probability LLM Uncertainty Estimation
Lattice Lab, Toyota Motor Corporation, Japan · Department of Computer Science, University of Tokyo, Japan · Rational Intelligence Lab, CISPA, Helmholtz Center for Information Security, Germany +3
Incentive Aware AI Regulations: A Credal Characterisation
Mar 5, 2026Anurag Singh, Julian Rodemann, Rajeev Verma +2Mechanism Design Imprecise Probability
1Rational Intelligence Lab, CISPA Helmholtz Center for Information Security, Saarbrücken, Germany · Department of Statistics, LMU Munich, Germany · 3UvA-Bosch Delta Lab, University of Amsterdam, Netherlands +1

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Imprecise Belief Fusion Improves Multi-agent Social Learning

Zixuan Liu, Jonathan Lawry, Michael Crosscombe

School of Engineering Mathematics and Technology, University of Bristol, Bristol, BS8 1TW, UK · Graduate School of Arts and Sciences, The University of Tokyo, Tokyo, 153-8902, Japan

Imprecise Probability

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In social learning, agents learn not only from direct evidence but also through interactions with their peers. We investigate the role of imprecision in such interactions and ask whether it can improve the effectiveness of the collective learning process. To that end we propose a model of social learning where beliefs are equivalent to formulas in a propositional language, and where agents learn from each other by combining their beliefs according to a fusion operator. The latter is parametrised so as to allow for different levels of imprecision, where a more imprecise fusion operator tends to generates a more imprecise fused belief when the two combined beliefs differ. In this context we describe both difference equation models and agent-based simulations of social learning under a variety of conditions and with different initial biases. The results presented suggest that for populations with a strong initial bias towards incorrect beliefs some level of imprecision in fusion can improve learning accuracy across a range of learning conditions. Furthermore, such benefits of imprecision are consistent with a stability analysis of the fixed points of the proposed difference equation models.