# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
To counter this threat, blockchain technology can be utilized as a safeguard to ensure data integrity and prevent tampering. ## 9 Conclusions and Future Work

Various approaches have been developed to assess trust and reputation in real-world MASs, such as peer-to-peer (P2P) networks, online marketplaces, pervasive computing, Smart Grids, and the Internet of Things (IoT). However, existing trust models face significant challenges, including agent mobility, dynamic behavioral changes, the continuous entry and exit of agents, and the cold start problem. To address these issues, we introduced the Create Assemblies (CA) model, inspired by synaptic plasticity in the human brain, where trustees (service providers) can evaluate their own capabilities and locally store trust information, allowing for improved agent mobility handling, reduced communication overhead, resilience to disinformation, and enhanced privacy. Previous work [7] comparing CA with FIRE, a well-known trust model, revealed that CA adapts well to consumer population fluctuations but is less resilient to provider population changes and continuous performance shifts. This work built on these findings and used a semi-formal analysis to identify performance pitfalls, which were then addressed by allowing service providers to self-assess whether their performance falls below a certain threshold, thereby ensuring faster reaction and better adaptability in dynamic environments. The simulation results confirm that CA_NEW outperforms the original CA_OLD, with greatly improved resilience and adaptability, although CA_OLD may still be preferable in scenarios of consumer population change, where long-standing service providers maintain stable performance. While FIRE has certain advantages in extreme environmental changes, CA_NEW remains highly competitive across a wide and diverse variety of environmental conditions. Building on prior research [30], where we explored how trustors can detect environmental dynamics and select the optimal trust model (CA or FIRE) to maximize utility, we propose an obvious direction for future work: exploring how trustees can detect the dynamics of environments and their self-awareness level to choose between CA_OLD and CA_NEW for optimal performance. This paper also analyzed CA with respect to established evaluation criteria for trust models and discussed its resilience to most well-known trust-related attacks, proposing countermeasures for dishonest behaviors. While the CA model meets key criteria such as decentralization, subjectivity, context awareness, dynamicity, availability, integrity, and transparency, it requires further research and improvements. Table 10 summarizes the CA model's evaluation across all thirteen criteria, providing a concise overview of its current strengths and areas for future enhancement. The model's holistic evaluation not only highlights its strengths but also demonstrates a commitment to continuous refinement, positioning it as a highly promising foundation for future trust management solutions. **Author Contributions:** Conceptualization, Z.L. and D.K. ; methodology, Z.L. and D.K. ; software, Z.L. ; validation, Z.L. and D.K. ; formal analysis, Z.L. and D.K. ; investigation, Z.L. and D.K. ; resources, Z.L. ; data curation, Z.L. ; writing--original draft preparation, Z.L. ; writing--review and editing, Z.L. and D.K. ; visualization, Z.L. and D.K. ; supervision, D.K. ; project administration, D.K. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The research data will be made available on demand. **Conflicts of Interest:** The authors declare no conflicts of interest. ## Appendix A

### Appendix A.1

**Proof of Proposition 1. ** It suffices to show by mathematical induction that the \(n\)-th connection for \(\text{task}_{1}\) will be initialized to the value of 0.45. Base Case. (\(n=2\)).