A Symmetric Perception Decision Framework Based on Credibility-Based Interval Hesitant Fuzzy Information: An Adaptive Asymmetric Adjustment DEMATEL–TODIM Approach

In complex decision-making environments, the uncertainty and hesitancy of evaluation information, coupled with differences among evaluators, lead to asymmetric characteristics in decision information and preferences. Traditional methods struggle to effectively handle scenarios where interval uncertainty and hesitant information coexist, nor can they suppress asymmetric biases caused by extreme evaluations or imbalanced information distributions. To address this, this paper proposes a Symmetric Perception Decision Framework based on credibility-based interval hesitant fuzzy information. First, a Robust Credibility-Based Interval Hesitant Fuzzy Score Function (R-CHFSF) is constructed. This function quantifies asymmetric information by integrating interval width, distribution dispersion, and hesitancy characteristics. An adaptive penalty mechanism is introduced to suppress unreasonable asymmetric amplification effects caused by anomalous intervals or extreme evaluations. Second, the R-CHFSF is embedded into DEMATEL and TODIM methods to construct an integrated model combining causal analysis and ranking decisions, forming a closed-loop decision mechanism that simultaneously regulates information asymmetry and preference asymmetry. Empirical analysis using online movie reviews demonstrates that this framework effectively suppresses interference from excessively asymmetric evaluations, enhances the robustness and interpretability of ranking results, and validates its effectiveness in asymmetry regulation and decision stability.