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<p>This paper presents a groundbreaking integration of Multiple Criteria Decision Making (MCDM) with explainable artificial intelligence (XAI) for tourism accommodation performance assessment, addressing fundamental limitations in traditional preference elicitation methods. We introduce the <i>XAI-Enhanced MCDM Convergence Theorem</i> that establishes theoretical foundations for combining classical MCDM methods with machine learning explanations, providing objective, data-driven criterion weights that eliminate subjective bias inherent in expert judgments. Our methodology extends TOPSIS, PROMETHEE, and AHP by incorporating Shapley values, Integrated Gradients, and Expected Gradients to derive interpretable multi-criteria rankings. Applied to Lower Aosta Valley accommodation data, our framework demonstrates 18% improvement in ranking accuracy over traditional MCDM approaches while revealing critical sustainability threshold effects previously undetected. The proposed XAI-enhanced framework addresses the longstanding challenge of criterion weight elicitation in MCDM through empirically-derived attribution scores, representing a paradigm shift from subjective to objective multi-criteria analysis in economic decision-making contexts.</p>

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Explainable Multi-criteria Decision Making for tourism economics: integrating XAI with MCDM for a robust accommodation performance assessment

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月2日

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  • Tiziana Ciano,
  • Massimiliano Ferrara

摘要

This paper presents a groundbreaking integration of Multiple Criteria Decision Making (MCDM) with explainable artificial intelligence (XAI) for tourism accommodation performance assessment, addressing fundamental limitations in traditional preference elicitation methods. We introduce the XAI-Enhanced MCDM Convergence Theorem that establishes theoretical foundations for combining classical MCDM methods with machine learning explanations, providing objective, data-driven criterion weights that eliminate subjective bias inherent in expert judgments. Our methodology extends TOPSIS, PROMETHEE, and AHP by incorporating Shapley values, Integrated Gradients, and Expected Gradients to derive interpretable multi-criteria rankings. Applied to Lower Aosta Valley accommodation data, our framework demonstrates 18% improvement in ranking accuracy over traditional MCDM approaches while revealing critical sustainability threshold effects previously undetected. The proposed XAI-enhanced framework addresses the longstanding challenge of criterion weight elicitation in MCDM through empirically-derived attribution scores, representing a paradigm shift from subjective to objective multi-criteria analysis in economic decision-making contexts.