Advanced Decision-Making Strategies in Life 3.0 Through q-Spherical Fuzzy Rough Dombi Aggregation Operators

In the era of Life 3.0, as envisioned by Max Tegmark, artificial intelligence has evolved to a point where it can autonomously improve and redesign its capabilities. This transformation necessitates sophisticated decision-making frameworks capable of handling ambiguity, uncertainty, and complex human values. This paper aims to develop a robust decision-making model for advanced artificial intelligence systems, particularly suitable for scenarios involving conflicting objectives and ethical considerations, such as artificial intelligence governance. To achieve this, we propose integrating q-spherical fuzzy sets, rough set theory, and Dombi geometric operators. q-Spherical fuzzy sets provide a nuanced representation of uncertainty, while rough set theory addresses vagueness. Dombi geometric operators facilitate effective information aggregation. This study introduces two new aggregation operators: q-spherical fuzzy rough Dombi weighted geometric and q-spherical fuzzy rough Dombi ordered weighted geometric. These operators are applied in multiple attribute decision-making scenarios using q-spherical fuzzy rough data to generate insightful results. Through detailed theoretical analysis and illustrative case studies, we demonstrate how this novel approach can improve the accuracy, reliability, and ethical alignment of artificial intelligence decisions, contributing to a more human-centric artificial intelligence in the age of Life 3.0. Comparative and sensitivity analyses further validate the effectiveness and robustness of our proposed approach. This work offers new perspectives on gathering and understanding q-spherical fuzzy rough data, expanding the current knowledge base and providing a more flexible, sensitive, and long-lasting solution to the challenges of artificial intelligence and human coexistence.