Adaptive Trust-Aware Multi-Criteria Decision Method for Artificial Intelligence Systems: A Conceptual Framework and Mathematical Model
DOI:
https://doi.org/10.5281/zenodo.22913096Аннотация
The rapid proliferation of artificial intelligence systems across safety-critical domains has exposed fundamental
limitations in existing decision-making methodologies. Current multi-criteria decision-making (MCDM) approaches operate
under static assumptions, trust-aware architectures lack formal mathematical grounding, and adaptive mechanisms fail to
unify both dimensions within a coherent framework. This paper introduces the Adaptive Trust-Aware Multi-Criteria Decision
Method (ATMCDM), a novel conceptual framework and mathematical model designed to address these interrelated
deficiencies. ATMCDM unifies trust calibration, dynamic weight adaptation, and multi-criteria optimization within a single
mathematically rigorous architecture. The proposed methodology introduces six formal constructs: a multidimensional
state vector, a context-sensitive trust function, a knowledge accumulation function, an adaptive weight mechanism, a
composite decision function, and a dynamic reward-driven update rule. A corresponding algorithm is presented with
computational complexity analysis. Through critical comparison with existing MCDM methods, trust-aware models, and
adaptive decision algorithms, the paper demonstrates that ATMCDM overcomes the rigidity of conventional approaches
while providing theoretical guarantees for convergence and consistency
Ключевые слова
multi-criteria decision making; trust-aware computing; adaptive systems; artificial intelligence; decision intelligence; explainable AI; human-AI collaboration; mathematical decision modelБиблиографические ссылки
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