An intuitionistic fuzzy automated negotiation model for personalized and efficient shared decision-making

Shared decision-making (SDM) is a healthcare decision-making model that integrates patient preferences with medical expertise. It seeks to foster active involvement, enhance satisfaction, and strengthen the doctor-patient relationship. However, SDM applications face three main challenges: the complexity of medical information, the ambiguity of patient preferences, and the uncertainty of treatment outcomes. These challenges lead to a negotiation process that is frequently time-consuming and complex. Although existing SDM tools have made progress in improving efficiency, they still face challenges in addressing all of these issues simultaneously. In response to these challenges, this paper introduces an Agent-based Auto-negotiation Model based on Intuitionistic Fuzzy Sets (AN-IFF). Specifically, AN-IFF tackles the uncertainties inherent in preference assessment and negotiation decision-making using intuitionistic fuzzy sets and fuzzy inference systems. The model also includes a time-discounting mechanism to dynamically adjust the concession strategy, generating an optimal counter-offer set. To further accommodate varying decision-making behaviors, AN-IFF introduces three distinct negotiation strategies that simulate the concessionary behaviors of decision-makers with optimistic, balanced, and pessimistic personalities. The model rationalizes the consultation process by fuzzy modeling physician and patient preferences while integrating multiple personality traits. Experimental results based on negotiation preference data show that AN-IFF effectively models the differences in concession behavior among decision-makers with diverse personalities in SDM contexts. Furthermore, AN-IFF demonstrates significant improvements in joint satisfaction, fairness, and the number of negotiation rounds compared to baseline methods, such as FCAN.