# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 5 Simulation
### The Overall Trust Model  
In this section, a simulation test is designed to estimate the proposed trust model in cloud manufacturing for service provider selection. The test raises interactions between different cloud manufacturing agents, and each interaction would evaluate the different cloud manufacturing providers and filter with which agent to interact. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \(\mathbf{(4,0)}\) & \(\mathbf{(3,1)}\) & \(\mathbf{(2,2)}\) & \(\mathbf{(1,3)}\) & \(\mathbf{(0,4)}\) \\ \hline [PERSON] and [PERSON] [27] & 0 & 0 & 0 & 0 & 0 \\ \hline [PERSON] et al. [65] & 0.8 & 0.8 & 0.8 & 0.8 & 0.8 \\ \hline [PERSON] and [PERSON] [20,66] & 0.54 & 0.35 & 0.29 & 0.35 & 0.54 \\ \hline Proposed entropy-based approach & 0.69 & 0.62 & 0.59 & 0.62 & 0.69 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Certainty computed by different approaches for different satisfactory degrees with a fixed amount of interactions at 4. The simulation is assumed to have \(Wnum\) cloud manufacturing client agents and \(Pnum\) cloud manufacturing provider agents. Once the interaction is launched, each client first interacts with each of the providers and collects and records the feedback three times. This information will later be used as evidence and shared with other information demanders. Simulation returns tell how to utilize our model, and the system can adjust to fluctuations in the information providers' behavior. At the beginning of the simulation, all cloud manufacturing clients are assumed to be honest. That is to say, once the other cloud manufacturing client agents demand recommendations, an honest agent would contribute whatever it has to the demander to help it make correct decisions. However, after several interactions \(Cn1\) (for example, 30 interactions), some of the agents (for example, the client agents \(a_{1}\) and \(a_{2}\)) become malicious and become entirely dishonest in sharing information. In other words, they would present excellent evaluations of the bad-performance agents, and vice versa. By using the proposed method, the malicious information providers are recognized, and their average credibility decreases gradually, which means that their contribution or recommendation will less affect the final decision. Although these malicious agents are always providing recommendations, a rational decision maker would never trust or rely on the recommendations to make decisions. After a while (for example, at \(Cn2\) rounds and \(Cn2=70\)), the malicious agents decide to become normal and contribute honest information again. When it decides to change its performance, its average credibility increases and their contributions are more influential. Simulation results and figures are discussed in detail in the following paragraph. Table 4 explains the detailed parameters in the simulation. As we can see, there are 10 resource client agents and 25 service provider agents. All agents act honestly in the first 30 rounds (\(Cn1=30\)). From round 30, some agents become dishonest and share complete incorrect evidence, and later they become honest again (\(Cn2=70\)). Each round, a client agent might decide to initiate interaction with a provider with a probability of 50%. One of the ten client agents is appointed as the evaluator each round, and seven agents (\(Snum=7\)) are selected to share their information, and the evaluator might be included in the information providers; \(Cnum\) best-evaluated service providers are selected to carry interactions. The evaluation process is conducted 200 times, and each of the evaluations is managed ten times. We find the same results, and Figure 8 is used to show the results of the clients' average credibility. Figure 8 shows the average credibility of the cloud manufacturing service clients, and one of which, defined as \(a_{1}\), coloured in blue, changes its performance from honest to malicious or the other way around, in round \(Cn1\) and \(Cn2\), respectively.