Modeling trust in central bank communication under uncertainty: A machine learning–driven fractal fuzzy decision support mechanism
Array, cilt.31, 2026 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 31
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.array.2026.101113
- Dergi Adı: Array
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Compendex, Directory of Open Access Journals
- Anahtar Kelimeler: Behavioral finance perspectives, Central bank communication, Decision-making under uncertainty, Expectation management, Institutional credibility, Trust formation mechanisms
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
This study aims to prioritize communication strategies that enhance the impact of central bank communication on market confidence by proposing an integrated artificial intelligence-supported fuzzy decision-making framework. A total of 12 criteria and 6 communication strategies are evaluated based on expert judgments, where expert weights are determined through a machine learning-based dimensionality reduction approach. Criterion weights are computed using the SIWEC method, while the RATGOS technique is employed to rank alternative strategies. The robustness of the findings is validated through RAM and MUNRA methods, confirming the stability of the results across different ranking mechanisms. The findings reveal that the contribution to the perception of political independence and the clarity of forward guidance have the highest importance weights (.181 and .134, respectively), indicating their dominant role in trust formation. In addition, the crisis-focused rapid communication mechanism and transparency-oriented strategies achieve the highest performance scores among alternatives. The proposed model offers a methodological contribution by integrating Koch Snowflake fuzzy sets with machine learning-based expert weighting, enabling a more realistic representation of uncertainty and heterogeneous expert opinions compared to conventional fuzzy and MCDM approaches. Unlike existing studies, this research provides a systematic and quantitative prioritization framework that links communication dimensions directly to trust formation, offering actionable insights for policymakers.