Keywords
trust-aware computing
adaptive systems
artificial intelligence
decision intelligence
explainable AI
Human-AI collaboration
mathematical decision model.
How to Cite
Abstract
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.References
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