Explainable Decision Support in Multi-Agent AI Systems Using L-Valued Information Flow and Shapley Aggregation

Purbita Jana ()
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Purbita Jana: Assistant Professor and Chair of M.Sc. Data Science Programme, Madras School of Economics, Chennai, India.

Working Papers from Madras School of Economics,Chennai,India

Abstract: Modern AI systems increasingly rely on distributed architectures in which mul-tiple agents, tools, and reasoning modules interact under uncertainty to produce col-lective decisions. However, existing approaches to decision aggregation and explain-ability often lack a unified semantic foundation and typically rely on post hoc attribu-tion methods. This paper introduces an explainable multi-agent decision framework based on an L-valued extension of information flow theory. By modeling subsys-tems as graded semantic classifications connected through structure-preserving infor-mation channels, the framework enables coherent aggregation of uncertain informa-tion while preserving interpretability. Shapley-value-based attribution is integrated directly into the semantic architecture, yielding intrinsic explanations of subsystem contributions to global decisions. The proposed framework unifies uncertainty mod-eling, distributed reasoning, and explainable AI within a compositional mathematical structure, with applications to multi-agent systems, tool-augmented language models, and intelligent decision-support systems.

Keywords: Explainable AI; Decision Support Systems; Multi-agent systems; Shapley value; Information flow; Fuzzy logic; L-valued systems; Uncertainty modeling; Human-AI decision making (search for similar items in EconPapers)
JEL-codes: C45 C63 C65 D81 D83 (search for similar items in EconPapers)
Pages: 38 pages
Date: 2026-05
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