A Fuzzy Collaborative Intelligence Approach to Group Decision-Making: a Case Study of Post-COVID-19 Restaurant...

Area of Science:

• Decision Sciences
• Artificial Intelligence
• Fuzzy Logic

Background:

• Existing group decision-making methods struggle with lack of consensus among experts.
• Forcing judgment modification or ignoring uncertainty can lead to unreasonable outcomes.
• Fuzzy group decision-making requires robust methods to handle expert disagreement.

Purpose of the Study:

• To propose a novel fuzzy collaborative intelligence approach for achieving expert consensus.
• To address the limitations of existing methods in handling decision maker uncertainty.
• To develop a methodology that respects individual judgments even when uncertain.

Main Methods:

• Application of biologically inspired fuzzy logic to group tasks (fuzzy collaborative intelligence).
• Decomposition of individual fuzzy judgment matrices into submatrices.
• Identification of consensus from aggregated fuzzy judgment submatrices.

Main Results:

• The proposed fuzzy collaborative intelligence approach successfully identified consensus in a post-COVID-19 restaurant transformation case.
• Existing methods failed to identify consensus for the restaurant transformation due to employee uncertainty.
• The optimal transformation plan included physical distancing, protective screens, and improved air circulation.

Conclusions:

• A new fuzzy collaborative intelligence approach effectively identifies consensus in group decision-making.
• Decomposing fuzzy judgment matrices offers a viable alternative to traditional consensus-building methods.
• This methodology provides a more reasonable approach to decision-making under uncertainty.