## 2 Materials and Methods
### System Design
#### 2.1.3 Additional Features  
Another benefit of the multi-agent approach to classification versus a traditional monolithic neural network is the ease at which new input features can be added to the computation. While some convolutional networks can accept variable length inputs, most traditional networks can only be expanded vertically with new layers while holding existing weights stable. They cannot expand horizontally, i.e., to include previously unknown input feature(s), at the input layer without re-training the network [25]. While each of our agents has a fixed number of inputs at creation, an internal MLP network restriction, the interactive and swarm steps of the overall system do not have restrictions on the number of participating agents. As noted in Section 2.1.1, agents with missing data simply do not participate. A natural extension to the variable number of participating agents is the ability to include newly generated agents representing the new data, a feature that does not require the re-training of the existing agents.  
When new features become available, we generate new agents using a combination of the new features and the existing features. Combining the new and existing features in the newly generated agents solves two problems; (1) new agents need to know about the features that the existing agents are trained on, and (2), the existing agents need a way to determine trust_score and prior_performance metrics for the newly generated agents. Both the social interaction arena and swarm components of the system rely on social variables built around agent prior performance, trust, and existing knowledge of features. Newly generated agents derive estimated prior_performance and trust_scores values from the existing features they receive. The estimates come from averaging these values from existing agents that have similar features. The averaging step does have some shortcomings, particularly when agents with similar, but not identical, features are used to generate the estimates; the estimated values may not accurately reflect the correct trust_score and prior_performance of the newly generated agents. However, the new agents have shorter interaction and prior performance histories compared with existing agents, which allows for larger initial modifications to these metrics during the interaction step, typically correcting any poor estimates within a few interaction periods.  
Incremental features addition was tested using two datasets, one attempting to predict breast cancer metastasis status by including additional measurements of cellular morphology data derived from whole-slide images of a representative hematoxylin and eosin (H&E) stained slide of the primary tumor. These data are in addition to the primary dataset as described in Section 2.2.1. The second dataset that was used for testing attempted to predict the success of Hollywood movies. The initial predictions were made with a combined dataset from The Movie Database (TMDb) and MovieLens using only data available in both datasets. Additional features were included from MovieLens, which included cast information beyond the leading cast member, as well as information about the production company and producers. More information about these data are available in Section 2.2.2.