## 1 Introduction
Second, in conventional trust models, trustors must collect and exchange extensive trust information (e.g., recommendations) before making a selection, leading to increased communication overhead. This is particularly problematic in resource-constrained networks, where communication is more expensive than computation [2]. [PERSON] and [PERSON] explained in [6] that task allocation in such settings is a complex combinatorial problem, requiring multiple message exchanges. In contrast, the CA model reduces this burden by limiting communication to request messages from service requesters and feedback messages containing performance ratings, significantly minimizing communication time and overhead. Third, since agents in the CA model do not share trust information, this approach is inherently more resistant to disinformation tactics, such as false or dishonest recommendations, which are common in other trust-based approaches. However, as we will discuss later, there are still some potential vulnerabilities that need to be addressed. Finally, privacy concerns often discourage agents from sharing trust-related data in traditional models. Since CA does not rely on recommendations, agents do not have to disclose how they evaluate others' services, thereby preserving their privacy. In our previous work [7], we proposed a CA algorithm as a solution for managing the constant entry and exit of agents in open MASs, as well as their dynamic behaviors. Through an extensive comparison between CA and FIRE, a well-known trust model for open MASs, we found the following:  
1. CA demonstrated stable performance under various environmental changes;
2. CA's main strength was its resilience to fluctuations in the consumer population (unlike FIRE, CA does not depend on consumers for witness reputation, making it more effective in such scenarios);
3. FIRE was more resilient to changes in the provider population (since CA relies on providers' self-assessment of their capabilities, newly introduced providers--who lack prior experience--must learn from scratch);
4. Among all environmental changes tested, the most detrimental to both models was frequent shifts in provider behavior, particularly when providers switched performance profiles, where FIRE exhibited greater resilience than CA. Motivated by these findings, this work begins with a semi-formal analysis aimed at identifying potential modifications to the CA algorithm that could enhance its performance when dealing with dynamic trustees' profiles. Based on this analysis, we introduce an improved version of the CA algorithm with a key modification: after providing a service, a service provider re-evaluates its performance, and if it falls below a predefined threshold, it classifies itself as a bad provider. This modification ensures that each provider maintains an up-to-date evaluation of its own performance, allowing for the immediate detection of performance drops that could harm consumers. Next, we conduct a series of simulation experiments to compare the performance of the updated CA algorithm against the previous version. The results demonstrate that the new version outperforms the original CA algorithm under all tested environmental conditions. In particular, when trustees change performance profiles, the improved CA algorithm surpasses both FIRE and its earlier version. We then present a comprehensive evaluation of the CA model based on thirteen widely recognized trust model criteria from the literature. While the model satisfies essential criteria such as decentralization, subjectivity, context awareness, dynamicity, availability, integrity, and transparency, further research and enhancements are identified. In this work, we make the assumption of willing and honest agents, but we acknowledge that our solution should be made generalizable to more realistic settings, where dishonesty and unwillingness are common conditions among agents' societies.