This study addresses the bilateral matching of data assets with expected levels in digital innovation ecosystems, incorporating regret-avoidance behavior. First, given the potential hesitation between two parties throughout the matching process, expressing preference information using probability hesitant fuzzy sets is reasonable. Second, the Lance scoring function best captures the gap in expectation and satisfaction between the matching parties. Based on regret theory, we develop a matching strategy that considers both parties’ utilities and satisfaction levels. We construct an optimization model to determine criteria weights using a novel Lance distance metric. Subsequently, a multi-objective optimization model is formulated to maximize satisfaction while ensuring stability in the supply–demand matching process. A numerical example underscores the suggested method's effectiveness and shows its practical applicability in data asset matching scenarios. This study advances the field by integrating psychological factors and sophisticated fuzzy set theory into the decision-making process for allocating data assets in digital ecosystems.

中文翻译：

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数字创新生态系统中的双边数据资产匹配：一种遗憾理论方法


本研究探讨了数据资产与数字创新生态系统中预期水平的双边匹配，包括后悔避免行为。首先，考虑到在整个匹配过程中双方之间可能存在犹豫，使用概率犹豫模糊集来表达偏好信息是合理的。其次，Lance 评分函数最能捕捉匹配方之间的期望和满意度差距。基于遗憾理论，我们制定了一个匹配策略，该策略考虑了双方的效用和满意度。我们构建了一个优化模型，以使用新颖的 Lance 距离指标来确定标准权重。随后，制定多目标优化模型，以最大限度地提高满意度，同时确保供需匹配过程的稳定性。一个数值示例强调了所建议方法的有效性，并展示了其在数据资产匹配场景中的实际适用性。本研究通过将心理因素和复杂的模糊集理论整合到数字生态系统中分配数据资产的决策过程中，推动了该领域的发展。