Hesitant and uncertain linguistics based executive decision making using risk and regret aversion: Methods,...

Area of Science:

• Decision Sciences
• Operations Management
• Fuzzy Logic Applications

Background:

• Global events, exemplified by the COVID-19 pandemic, significantly disrupt high-stake company operations.
• Maintaining market relevance and achieving internal goals amidst crises necessitates robust decision-making frameworks.
• Existing models may not adequately address the inherent hesitancy and risk associated with unprecedented disruptions.

Purpose of the Study:

• To introduce a novel linguistic decision-making model for high-stake companies facing operational disruptions.
• To incorporate human cognition, risk, and regret into a structured decision-making process.
• To address inherent hesitancy in decision-making using advanced fuzzy set techniques.

Main Methods:

• Development of the THREAD (Theory of Regret-based Hesitant-fuzzy linguistic decision-making) model.
• Utilizes regret theory principles to inform optimal choices.
• Employs interval type-2 fuzzy sets (IT2 FS) and hesitant fuzzy linguistic term sets for handling uncertainty and vagueness.

Main Results:

• The THREAD model provides a computational framework for decision-making under conditions of uncertainty and hesitancy.
• Demonstrates the integration of regret theory with fuzzy linguistic techniques for practical application.
• Offers a method to compute optimal decisions considering potential risks and regrets.

Conclusions:

• The THREAD model offers a valuable tool for high-stake companies to navigate complex decision-making during global crises.
• Effective decision-making in disruptive environments requires accounting for cognitive factors like regret and hesitancy.
• Fuzzy linguistic methodologies, specifically IT2 FS and hesitant fuzzy sets, are effective for modeling such complex decision problems.