## 4 Discussion  
Our classification system has demonstrated competitive performance with other state-of-the-art classification methods for multiple different datasets with a varied number of samples and input features. While Random Forest and a Deep Neural Network implementation outperformed WoC-Bots in maximum accuracy on the tested datasets, WoC-Bots outperformed AdaBoost and Logistic Regression methods in two of the three tested datasets and performed similarly to all of the top methods in the third dataset. WoC-Bots' accuracy, precision, and recall metrics tracked similarly with the other classification methods; XGBost, AdaBoost, Random Forest, DNN, and WoC-Bots all showed higher recall scores and lower precision scores on the breast cancer dataset. Similarly, the same methods showed higher precision and lower recall scores for the Hollywood dataset, indicating WoC-Bots are learning similar information as the other classification methods. While WoC-Bots prediction runtime is the slowest of the methods shown in this paper, runtime optimization has not yet been a focus. This is something that can and should be improved in future work; for example, the interaction arena interactions can be optimized to a series of matrix multiplication operations for each pair of interacting agents. Further, this optimization step would also reduce the runtime during the swarming phase. The current system allows for easy behavioral modification while testing a new method, but this is an intended future optimization.  
The multi-agent design allows for new, previously unknown features to be included in any existing classification problem without re-training the full network (or set of existing agents). We demonstrated significant accuracy improvements when adding new features, while also demonstrating similar overall performance when compared with re-training the agents with all of the available features. This method is useful when the final set of features is unknown or constantly expanding. The real-world results we have shown with sports betting made use of this feature frequently to incorporate new data from recent games, as new data sources were discovered or new metrics were developed to track player and team performance. Individual WoC-Bots were quickly updated to include results from the most recent games and new bots were generated to represent novel data sources or statistical metrics.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Values** & **Spread Bets** & **Moneyline Bets** \\ \hline Units Risked & 48,159 & 80,408 \\ Units Returned & 4863.5 & 5364.5 \\ ROI & 10.1\% & 6.7\% \\ Total Bets & 2376 & 3225 \\ Winning Bets & 1174 & 1296 \\ Win Rate & 49.33\% & 40.19\% \\ \hline \hline \end{tabular}
\end{table}
Table 9: Meta-Swarm: Risk, Return and Win Rate (Accuracy) for Spread and Moneyline Bets.  
WoC-Bots can be distributed across multiple hardware nodes, with no requirement for a single powerful machine to train a very deep and wide monolithic neural network. We demonstrated improved runtime performance when adding compute nodes for both the interaction and swarming steps of the system. The'meta-swarm' system developed to represent the prediction of each of the nodes has greatly reduced the runtime of the distributed swarm step by allowing each agent to stay on a single node throughout the process. This system has shown similar accuracy to a single-node swarm in two of the three tested datasets.  
A by-product of developing the distributed'meta-swarm' was the ability to include additional prediction sources into the overall prediction, after the WoC-Bots have made their initial prediction. We have shown this reduces the overall variance in accuracy, precision, and recall in all of the datasets tested, while also producing the best accuracy of all methods tested on the Hollywood dataset. This system, primarily using the'meta-swarm' extension, has been applied to a real-world problem, making predictions about the outcomes of sporting events.