An opponent model for agent-based shared decision-making via a genetic algorithm - Lin, Kai-Biao; Wei, Ying; Liu, Yong; Hong, Fei-Ping; Yang, Yi-Min; Lu, Ping (Frontiers in Psychology, 2023)
Shared decision-making (SDM) has received a great deal of attention as an effective way to achieve patient-centered medical care. SDM aims to bring doctors and patients together to develop treatment plans through negotiation. However, time pressure and subjective factors such as medical illiteracy and inadequate communication skills prevent doctors and patients from accurately expressing and obtaining opponent preferences. This problem leads to SDM being in an incomplete information environment, which significantly reduces the efficiency of the negotiation and even leads to failure. In this study, we integrated a negotiation strategy that predicts opponent preference by a genetic algorithm with an SDM auto-negotiation model constructed based on fuzzy constraints, thereby enhancing the effectiveness of SDM by addressing the problems posed by incomplete information environments and rapidly generating treatment plans with high mutual satisfaction. A variety of negotiation scenarios are simulated in experiments, and the proposed model is compared with other excellent negotiation models. The results indicated that the proposed model better adapts to multivariate scenarios and maintains a higher mutual satisfaction.