Tri-Phase Implementation of an Innovative Fuzzy Logic Approach for Decision-Making

This paper proposes a novel approach to decision-making based on a three-phase application of a new fuzzy logic model that embraces the principles of symmetry by balancing competing objectives in data collection and analysis. Our study, which employs a three-stage stratified random sample strategy with a randomized response technique, addresses the critical challenges of cost management and volatility reduction. Using the alpha-cut method, our model creates an effective allocation strategy that finds a balance between cost constraints and variance reduction objectives. We use numerical examples from real-world scenarios to demonstrate our approach's durability and practicality. Our revolutionary technique maintains data quality and cost-effectiveness while offering a game-changing answer to sensitive information acquisition concerns. By combining randomized response techniques and fuzzy logic, this study establishes a new standard for decision-making models that prioritizes both data-gathering precision and privacy preservation, encapsulating the essential principle of symmetry in balancing competing aims.