# A Model for Deriving Trust and Reputation on Blockchain-Based e-Payment System +
## 4 Evaluation via Simulation with Controlled Dataset  
To verify the effectiveness of the proposed model for deriving reputation and trust values, we first conduct controlled experiments with a dataset from our payment system consisting of various types of users. In the next section, we also evaluate the model using data from the actual Bitcoin transaction history. We compare our reputation and trust values with the naive \"_cumulative average of raw evaluation_\", which is used by most, if not all, e-commerce systems (e.g., average rating values on amazon.com, hotels.com, etc.). We expect that naive cumulative average of raw evaluation will not reflect the time factor and human tendency well. Note that the dataset generated on our local experimental setup are safely stored in a private blockchain-based storage built for the online payment system after applying our model. Table 1 lists the evaluation criteria of each simulated buyer who grades sellers in the given range. In the table, Buyers A and B represent generous users who always give 7 to 10 as evaluation scores. On the other hand, Buyers C and D are strict in grading, giving only 1 to 4. Buyers E, F, G, and H stand for general users who evaluate transactions with wider ranges with a mean close to 5. Lastly, Buyers I and J grade others from 1 to 2 or 9 to 10, which represent extreme cases such as owners, advertisers, or those with decisive characteristics that could possibly exist in our real life. Furthermore, Table 2 shows five different simulated sellers that represent various types of providers. Specifically, the \"good to bad\" and \"bad to good\" sellers are expected to simulate their reputation changes over time according to decreasing and increasing evaluations they receive from counterparts, respectively. We generated twenty transactions for every buyer-seller pair, resulting in total of 1000 transactions. That is, each buyer and seller has evaluated hundred times and two hundred times, respectively, resulting in two-thousand transaction evaluations (ratings) in total. \begin{table}
\begin{tabular}{c c} \hline \hline
**User** & **EV.range** \\ \hline Buyer A, B & 7–10 \\ Buyer C, D & 1–4 \\ Buyer E, F & 3–8 \\ Buyer G, H & 1–10 \\ Buyer I, J & 1–2 or 9–10 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ten buyers’ evaluation criteria. Figure 7: Distribution of latency measurements for calculating trust and reputation with and without the local cache for blockchain DB. Note that the units of x-axis is three orders of magnitude different (seconds vs. milliseconds). Figure 8a,b plots the rating values made over time by a generous and strict buyer, respectively, together with the corresponding NE, PEC average, and naive cumulative average of rating values. Two figures demonstrate the normalization process of the rating values according to each user's grading tendency. When it comes to generous and strict users, even though raw rating values are extremely high or low, they become (almost) uniformly distributed into the range of 0 to 1 after the normalization process. On the other hand, naive cumulative average values remains either high or low, providing less meaningful information to other customers. Eventually, we can see that each user's criteria is well reflected on the NE although every user has different tendency. Figure 9a,b plot how the reputation of \"bad to good\" and \"good to bad\" sellers change over time, according to the evaluations they receive from counterparts. The seller's reputation starts from a neutral value of 0.5 at the beginning. In Figure 9a, we observe that the reputation of seller D goes up to 0.9 approximately for six months and goes down drastically after 3 months losing all her reputation. However, naive cumulative average value does not follow that tendency well, and tends to lag behind due to large amount of out-dated history. On the other hand, seller E's reputation shown in Figure 9b goes down for the first six months, and then goes up gradually after which it starts to receive good rating values.