## 1 Introduction  
Conventional cryptographic methods, such as the use of certificates, digital signatures, and Public Key Infrastructure (PKI), depend on a static, centralized authority for certificate verification. However, this approach may be impractical or insecure in open, dynamic, and highly distributed networks [1]. Additionally, entities in such networks may have limited resources, making it difficult for them to handle the computational demands of cryptographic protocols [2]. To overcome these challenges, trust management provides an alternative approach, enabling entities to gather reliable and accurate information from their surrounding network. Various methods for evaluating trust and reputation have been developed for real-world distributed networks that can be considered multi-agent systems (MASs), such as peer-to-peer (P2P) networks, online marketplaces, pervasive computing, Smart Grids, the Internet of Things (IoT), and many more. However, existing trust management approaches still face major challenges. In open MASs, agents frequently join and leave the system, making it difficult for most trust models to adapt [3]. Additionally, assigning trust values in the absence of evidence and detecting dynamic behavioral changes remain unresolved issues [4]. Trust and reputation models, which rely on data-driven methods, often struggle when assessing new agents with no prior interactions. This issue, known as the cold start problem, commonly arises in dynamic groups where agents may have interacted with previous neighbors but lack connections to newly introduced agents. Moreover, in dynamic environments, agents' behaviors can change rapidly, necessitating that consumer agents recognize these changes to select reliable providers. The challenge in trust assessment lies in the fact that behavioral changes occur at varying speeds and times, making conventional methods, such as forgetting old data at a fixed rate, ineffective [4]. To address these challenges, we previously introduced a novel trust model for open MASs, inspired by synaptic plasticity, the process that enables neurons in the human brain to form structured groups known as assemblies, which is why we named our model \"Create Assemblies (CA)\". CA's distinguishing characteristic is that, unlike traditional trust models where the trustor selects a trustee, CA allows the trustee to determine whether it has the necessary skills to complete a given task. To briefly describe the CA approach, we note that activities are initiated by a service requester, the trustor, which broadcasts a request message that includes task details such as its category and specific requirements. Upon receiving the request, potential trustees (i.e., service providers within the service requester's vicinity) store the message locally and establish a connection with the trustor if one does not already exist. Each connection is assigned a weight, a value between 0 and 1, representing the trust level or the probability of the trustee successfully completing the task. In the CA model, trust update is event-driven. After the task's completion, the trustor provides performance feedback, which the trustee uses to adjust the connection weight--increasing it for successful execution and decreasing it otherwise. The CA approach offers several key advantages in open and highly dynamic MASs due to its unique design, where the trustor does not select a trustee, and all trust-related data concerning the trustee are stored locally within the trustee. First, since each trustee retains its own trust information, these data can be readily accessed and utilized across different applications or systems the agent joins. This provides a major advantage in handling mobility, an ongoing challenge in MASs [5]. By allowing a new service provider to estimate its performance for a given task based on past executions in other systems, the CA model effectively addresses the cold start problem.