# An Adaptive Dempster-Shafer Theory of Evidence Based Trust Model in Multiagent Systems
## 5 Simulation
### The Overall Trust Model
The agent's credibility is updated at interaction 30 (\(Cn1=30\)), where it is redefined as a malicious information provider. As a result, we can recognize that its average credibility from other  
Figure 8: Change in average credibility for each information sharing agent. clients' perspectives is degraded and, therefore, rational information demanders decrease the confidence towards the provided information. Forty rounds later, that malicious agent reverts to being honest and restarts telling the truth. As a result, its credibility is gradually rebuilt, and the other agents begin to trust its recommendations. However, we also find that the average credibility decreases quickly (in approximately 40 rounds, it decreases from 0.75 to 0.15) and increases very slowly (it takes approximately 130 rounds to increase from 0.15 to 0.7), reflecting the fact that trust is easy to lose and hard to achieve. In more detail, we find that only one cloud manufacturing agent out of ten information provider agents has changed its performance in round 30; that is to say, most agents also provide correct information to facilitate making the right decisions. As explained, some trust estimation models assume that most information providers are honest and present correct information [2]. As a result, in round 30, we force five honest cloud manufacturing agents to become malicious and begin sharing incorrect information. Figure 9 shows the result. As is shown, the average credibility of the five agents (coloured in dark blue, pink, red, purple and light green all decreased from round 30 and increased at round 70. That is to say, a rational evaluator can distinguish honest information providers, and if they keep telling an untruth, the information providers would no longer be trustworthy. However, if they give correct information, their provider information would become valuable again. As a comparison, in terms of applying the Dempster-Shafer theory of evidence to trust estimation in a system of multiagents, ref. [28; 29] take personal credibility into consideration. However, in [28], we do not discuss if the BPAs generation is appropriate, especially in a dynamic and open MAS; we focus on how the weight of the third-party agents is updated. As explained, the weight is updated by \(w_{i}^{\prime}=\theta*w_{i}\), where \(w_{i}\) is the agent's weight, and \(\theta<1\). Thus, the credibility of third-party agents decreases gradually. As a \begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Value** & **Explanation** \\ \hline \(Wnum\) & 10 & Number of cloud manufacturing client agents \\ \(Pnum\) & 25 & Number of cloud manufacturing provider agents \\ \(Snum\) & 7 & Number of selected agents to share information \\ \(Cnum\) & 3 & Number of selected provider agents to conduct interactions \\ \(Cn1\) & 30 & From when some agents become malicious \\ \(Cn2\) & 70 & From when the malicious return to being honest \\ Rounds & 200 & Total round \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters in the simulation. Figure 9: Change in average credibility for each information sharing agent when five agents change their performance. result, it seems inappropriate in an open and dynamic system. In [29], the agents' weight is defined as increasing gradually to one if positive feedback is received; on the contrary, the weight decreases linearly for negative feedback. Although evidence consistency, as well as evidence certainty, are not considered in this literature, we also simulate it in the same test as explained in Table 4. Figure 10 indicates the average credibility of each information provider from the other perspective. As we can see, even if agent \(a_{1}\) (coloured in blue) chose to present dishonest information from round 30 to round 70, its average credibility increased. That is to say, no evaluator has recognized this cheater. The main reason for receiving increasing average credibility is that most honest agents share correct information that has helped the decision maker select the well-evaluated service provider.