Current trust and reputation models continue to have significant limitations, such as the inability to deal with agents constantly entering or exiting open multi-agent systems (open MAS), as well as continuously changing behaviors. Our study is based on CA, a previously proposed decentralized computational trust model from the trustee's point of view, inspired by synaptic plasticity and the formation of assemblies in the human brain. It is designed to meet the requirements of highly dynamic and open MAS, and its main difference with most conventional trust and reputation models is that the trustor does not select a trustee to delegate a task; instead, the trustee determines whether it is qualified to successfully execute it. We ran a series of simulations to compare CA model to FIRE, a well-established, decentralized trust and reputation model for open MAS under conditions of continuous trustee and trustor population replacement, as well as continuous change of trustees' abilities to perform tasks. The main finding is that FIRE is superior to changes in the trustee population, whereas CA is resilient to the trustor population changes. When the trustees switch performance profiles FIRE clearly outperforms despite the fact that both models' performances are significantly impacted by this environmental change. Findings lead us to conclude that learning to use the appropriate trust model, according to the dynamic conditions in effect could maximize the trustor's benefits.

翻译：当前的信任与声誉模型仍存在显著局限，例如无法处理智能体持续进入或退出开放多智能体系统（open MAS）以及行为持续变化的问题。本研究基于CA模型——一种先前提出的、受突触可塑性及人脑神经集群形成启发的去中心化计算信任模型（从受托者视角出发）。该模型专为满足高度动态和开放多智能体系统的需求而设计，其与大多数传统信任与声誉模型的核心区别在于：委托者不选择受托者来委派任务，而是由受托者自行判断其是否有能力成功执行该任务。我们通过一系列仿真实验，将CA模型与FIRE模型（一种成熟的、适用于开放多智能体系统的去中心化信任与声誉模型）进行比较，实验条件包括：受托者与委托者群体的持续更替，以及受托者执行任务能力的连续变化。主要发现是：FIRE模型对受托者群体变化具有更优性能，而CA模型对委托者群体变化表现出鲁棒性。当受托者切换其性能配置文件时，尽管两种模型的性能均受到该环境变化的显著影响，但FIRE模型仍明显表现更优。这些发现表明：根据实际动态条件学习使用恰当的信任模型，能够最大化委托者的收益。