A Biologically Inspired Trust Model for Open Multi-Agent Systems That Is Resilient to Rapid Performance Fluctuations

Trust management provides an alternative solution for securing open, dynamic, and distributed multi-agent systems, where conventional cryptographic methods prove to be impractical. However, existing trust models face challenges such as agent mobility, which causes agents to lose accumulated trust when moving across networks; changing behaviors, where previously reliable agents may degrade over time; and the cold start problem, which hinders the evaluation of newly introduced agents due to a lack of prior data. To address these issues, we introduced a biologically inspired trust model in which trustees assess their own capabilities and store trust data locally. This design improves mobility support, reduces communication overhead, resists disinformation, and preserves privacy. Despite these advantages, prior evaluations revealed the limitations of our model in adapting to provider population changes and continuous performance fluctuations. This study proposes a novel algorithm, incorporating a self-classification mechanism for providers to detect performance drops that are potentially harmful for service consumers. The simulation results demonstrate that the new algorithm outperforms its original version and FIRE, a well-known trust and reputation model, particularly in handling dynamic trustee behavior. While FIRE remains competitive under extreme environmental changes, the proposed algorithm demonstrates greater adaptability across various conditions. In contrast to existing trust modeling research, this study conducts a comprehensive evaluation of our model using widely recognized trust model criteria, assessing its resilience against common trust-related attacks while identifying strengths, weaknesses, and potential countermeasures. Finally, several key directions for future research are proposed.