# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
The exact NSIR values are displayed in the graphs illustrating the experimental results. The effectiveness of each algorithm in identifying trustworthy provider agents is measured by the utility gain (UG) achieved by consumer agents during simulations. Throughout each simulation run, the testbed records the UG for each consumer interaction, along with the algorithm used (FIRE, CA_OLD, or CA_NEW). After completing all simulation runs, we calculate the average UG for each interaction per consumer group. We then apply a two-sample \(t\)-test for mean comparison [29] with a 95% confidence level to compare the average UG between the following:

1. CA_OLD and CA_NEW;
2. FIRE and CA_NEW. Each experiment's results are displayed using two two-axis graphs: one comparing CA_OLD and CA_NEW and another comparing FIRE and CA_NEW. In each graph, the left \(y\)-axis represents the UG means for consumer groups per interaction, while the right \(y\)-axis displays the performance rankings produced by the UG mean comparison using the \(t\)-test. The ranking is denoted with the prefix \"R\" (e.g., R.CA), where a higher rank indicates superior performance. If two groups share the same rank, their performance difference is statistically insignificant. For instance, in Figure 0(a), at the 17 th interaction (\(x\)-axis), consumer agents in the CA_NEW group achieve an average UG of 6.15 (left \(y\)-axis), and according to the \(t\)-test ranking, the CA_NEW group holds a rank of 2 (right \(y\)-axis). Figure 1: (**a**) A performance comparison of CA_NEW and CA_OLD, when providers change performance with 10% probability per round: CA_NEW achieves better performance, especially in early interactions. (**b**) A performance comparison of CA_NEW and FIRE, when providers change performance with 10% probability per round: CA_NEW excels in early interactions (except for the first interaction), while FIRE adapts and slightly outperforms over time. All experiments use a \"typical\" provider population, as defined in [27], consisting of 50% beneficial providers (yielding positive UG) and 50% harmful providers (yielding negative UG, including intermittent providers). To maintain consistency, we use the same experimental values as in [27], detailed in Table 7. Additionally, the default parameters for FIRE and CA are presented in Tables 8 and 9, respectively. concern scenarios where service provider performance fluctuates over time. Experiment 3 (Section 6.2) is conducted in a static environment. Experiments 4-8 (Section 6.3) test the impact of a gradually changing provider population increasing up to 30%. Experiments 9-11 (Section 6.4) examine the effects of a consumer population change up to 10%. Experiments 12 and 13 (Section 6.5) explore changes in the locations of consumers and providers. Experiment 14 (Section 6.6) evaluates performance where all dynamic factors change simultaneously. Section 6.7 summarizes the findings of all experiments. ### The Performance of the New CA Algorithm in Dynamic Trustee Profiles

This section presents the results of two experiments, in which we compare the performance of the updated CA algorithm (CA_NEW) with the performance of the old version (CA_OLD) and the performance of FIRE, when the service providers' performance varies over time. **Experiment 1**. A provider may alter its average performance at a maximum of 1.0 UG unit with a probability of 10% in every round (\(p\mu C=0.10\), \(M=1.0\)). The results are shown in Figure 1. Figure 1a shows that CA_NEW generally outperforms CA_OLD, with the most significant improvement observed in the initial interactions. Figure 1b shows that CA_NEW, except for the first interaction, outperforms FIRE in the initial interactions. However, FIRE adapts as the number of interactions increases and eventually achieves slightly better performance. **Experiment 2**. A provider may switch into a different performance profile with a probability of 2% in every round (\(p_{profileSwitch}=0.02\)).