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<p>Binaural-beat audio is sometimes marketed online as “digital drugs” that can mimic psychoactive experiences. This creates a policy problem: regulators and platforms need practical strategies, but evidence is uncertain, and stakeholder judgments are often linguistic rather than numeric. This study proposes a Fermatean fuzzy multi-criteria decision-making (MCDM) framework to evaluate and rank ten regulatory strategies under uncertainty. We model expert assessments using Fermatean fuzzy numbers, compute objective criteria weights via an entropy scheme, and rank strategies using TOPSIS in the same fuzzy environment. The evaluation considers seven main criteria and 28 sub-criteria spanning public health, ethics, social impact, economics, and technical feasibility. In the reported case study, Public Health Impact receives the strongest global influence, and research-driven adaptive regulation achieves the best overall closeness to the ideal solution. Robustness is examined through sensitivity scenarios and comparative baselines against classical fuzzy and intuitionistic fuzzy settings, as well as alternative MCDM rankers. The framework supports transparent, auditable policy prioritization for families, platforms, and public agencies dealing with emerging digital audio harms.</p>

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Regulatory strategy evaluation for digital drugs based on binaural beats marketed for psychoactive-like effects: a Fermatean fuzzy MCDM approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月31日

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  • Ahmed M. Ali,
  • Mohamed Abdel-Basset,
  • Adam Slowik,
  • Ibrahim Alrashdi

摘要

Binaural-beat audio is sometimes marketed online as “digital drugs” that can mimic psychoactive experiences. This creates a policy problem: regulators and platforms need practical strategies, but evidence is uncertain, and stakeholder judgments are often linguistic rather than numeric. This study proposes a Fermatean fuzzy multi-criteria decision-making (MCDM) framework to evaluate and rank ten regulatory strategies under uncertainty. We model expert assessments using Fermatean fuzzy numbers, compute objective criteria weights via an entropy scheme, and rank strategies using TOPSIS in the same fuzzy environment. The evaluation considers seven main criteria and 28 sub-criteria spanning public health, ethics, social impact, economics, and technical feasibility. In the reported case study, Public Health Impact receives the strongest global influence, and research-driven adaptive regulation achieves the best overall closeness to the ideal solution. Robustness is examined through sensitivity scenarios and comparative baselines against classical fuzzy and intuitionistic fuzzy settings, as well as alternative MCDM rankers. The framework supports transparent, auditable policy prioritization for families, platforms, and public agencies dealing with emerging digital audio harms.