# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
* Combined Dynamic Factors: When all dynamic factors are in effect, CA_NEW maintains superior performance over both CA_OLD and FIRE across all interactions, demonstrating its robustness in complex environments. Overall, CA_NEW is a significant improvement over CA_OLD, with better resilience and adaptability, although there are indications that CA_OLD may be a better choice for old service providers that do not change their behavior. While FIRE exhibits some advantages in extreme environmental changes, CA_NEW remains highly competitive across diverse scenarios. Building on previous research [30], where we examined how trustors can identify environmental dynamics and select the optimal trust model (CA or FIRE) to maximize utility, a natural direction for future work is to explore how trustees can recognize environmental changes and assess their own self-awareness to determine whether CA_OLD or CA_NEW would yield the best performance. Adopting this comprehensive RL approach could enhance adaptability and effectiveness across a wide range of scenarios. ## 7 Towards a Comprehensive Evaluation of the CA Model

In [31], [PERSON] et al. define quality of trust (QoT) as the quality of trust models and propose several evaluation criteria including subjectivity, dynamicity, context awareness, privacy preservation, scalability, robustness, overhead, explainability, and user acceptance. Table 7.5 (**a**) Performance comparison of CA_NEW and CA_OLD when all dynamic factors are in effect: CA_NEW outperforms. (**b**) Performance comparison of CA_NEW and FIRE when all dynamic factors are in effect: CA_NEW outperforms. However, they highlight the absence of a uniform standard. Indeed, [PERSON] et al. in [32], focusing on edge-based IoT systems, provide a different set of evaluation criteria (accuracy, security, availability, reliability, Heterogeneity, Lightweightness, flexibility, and scalability), referring to them as trust management requirements, whereas in [33], integrity, computability, and flexibility are identified as the key properties of trust models. To evaluate the CA model comprehensively, we selected thirteen trust model criteria synthesized from diverse but widely cited publications [31; 32; 33]. These criteria capture both technical properties (e.g., robustness, scalability, overhead) and human-centric qualities (e.g., explainability, user acceptance). Our selection reflects the recurring themes across the literature and the pressing demand for open, dynamic MAS environments. While all thirteen criteria are important, their relative priority may vary based on application needs. For example, scalability and overhead are critical in IoT settings with resource constraints, whereas explainability and user acceptance are more relevant in human-agent collaboration scenarios. This section outlines a comprehensive evaluation of the CA model based on the thirteen evaluation criteria as follows. **Decentralization**. Centralized cloud-based trust assessment has significant drawbacks, including a single point of failure, scalability issues, and susceptibility to data misuse and manipulation by the companies that own cloud servers. As a response, decentralized-distributed solutions are becoming the norm in trust management [34]. The CA model follows the decentralized approach by allowing each trustee to compute trust independently, eliminating reliance on a central authority. **Subjectivity**. Trust is inherently subjective, as different service requesters may have different service requirements in different contexts, affecting their perception for the trustworthiness of the same service provider [33]. Therefore, trust models should take into account the trustor's subjective opinion [31].