## 2 Materials and Methods
### System Design
#### 2.1.1 Interaction Period  
Table 1 presents the attributes which describe the internal state of an agent during the interaction period. Many of these values are available to other agents during interactions within the social interaction arena. The confidence attribute can be biased to favor accuracy, precision, or recall metrics depending on which metric is the most important for the given classification problem. For example, a classification task such as the breast cancer dataset we present in the results section may benefit from biasing towards recall to better avoid false negative predictions  
\[confidence=accuracy*0.25+precision*0.25+recall*0.50. \tag{1}\]  
The interaction arena can take any 2D shape comprised of square faces. In this work, the arena is a square with the number of available discrete spaces available to the agents equal to 2 times the number of participating agents. Agents are initialized randomly inside the arena and agents are not allowed to share spaces with other agents at initialization. After initialization, up to two agents can share the same space, which starts an interaction between those two agents. Following the interaction, agents are moved within the arena in a \"Manhattan-like\" fashion, moving either one space north, south, east, or west within the bounds of the arena. The direction is randomly selected and agents cannot interact with each other two times in a row or more than twice within five separate movements. Agents who cannot move because of these restrictions are 'teleported' to a randomly selected \begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & **Description** \\ \hline current\_prediction & Binary, class 0 or class 1. \\ \hline trust\_score & Updated during interaction based on \\  & agreement and performance. \\ \hline features & A list of features used by the agent’s classifier. \\ \hline prior\_performance & Long-term history of agent performance, varied between \\  & 0.7 and 1.3 where 1.0 is average performance. \\ \hline  & Initialized to MLP classifier performance, updated \\ certainty & during interaction period. represents how strongly \\  & the agent believes in the current prediction. \\ \hline eval\_accuracy & Initial classification accuracy. \\ \hline eval\_precision & Initial classification precision. \\ \hline eval\_recall & Initial classification recall. \\ \hline  & Biased value based on \\ confidence & accuracy, precision, and recall. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Internal scoring variables. empty space within the arena. Agents are also 'teleported' to a random empty space every 10-15 iterations to further facilitate information dispersal. The interaction period is operated in discrete iterations; each agent is moved during each iteration and all interactions between agents must complete before the next iteration can begin. The number of iterations is based on the number of participating agents where  
\[interaction_{iters}=num\_agents*0.10 \tag{2}\]  
to balance run time with information dispersal. Any agent with missing data for the current classification task does not participate; all agents in the interaction arena and subsequent steps have complete information for the features they were assigned for the current classification question. During the interaction period, two agents, agent \(a\) and agent \(b\), interact when sharing a space within the arena. The following equations govern the updates to the internal state of agents during the interaction and determine if and when an agent's prediction will change, and what confidence the agent has in its current prediction. The equations assume agent \(a\) is receiving information from agent \(b\), however during an interaction the inverse will also occur.