\section{Results}
\begin{figure}[ht]
    \centering
    \input{chapters/figures/supra.tex}
    \caption{Evolution of price distributions over experiment steps. The heatmap illustrates the density of price offerings. This is an early baseline simulation which demonstrates supra-competitive price-setting in deep learning agents such as SAC as can be clearly seen by the high density at the highest available price.}
    \label{fig:supra_heatmap}
\end{figure}

\subsection{Behavioral Analysis}

The transition-kernel analysis is evaluated with both between-class divergence and an intra-class bootstrap null baseline. This allows us to separate real behavioral differences from finite-sample estimation noise.

\begin{table}[ht]
\centering
\caption{Divergence significance using intra-class bootstrap baseline (B=100 per class).}
\label{tab:divergence_significance}
\begin{tabular}{lcccc}
\toprule
Metric & Mean KL & Std & 5\% quantile & 95\% quantile \\
\midrule
Between-class (Human vs Agent) & 5.3067 & -- & -- & -- \\
Human intra-class split & 2.5271 & 1.2501 & 0.6845 & 4.6015 \\
Agent intra-class split & 1.2065 & 1.2607 & 0.2177 & 4.2345 \\
\bottomrule
\end{tabular}
\end{table}

For this run ($n_H=11$, $n_A=7$, $B=100$), the pooled lift ratio is $2.84\times$ and the empirical one-sided p-value is $0.0149$, both computed as defined in Section~\ref{sec:tpe}. This places the between-class divergence clearly above the intra-class null and supports the use of divergence-derived contamination signals in downstream pricing control.


\subsection{Experimental Outcomes}

To evaluate robustness contributions, we compare two policies on the same environment family: (i) robust pricing with COI-aware reward and adversarial contamination step, and (ii) non-robust baseline with revenue-only reward (\texttt{--no-robust}).

\begin{table}[ht]
\centering
\caption{Pricing policy benchmark for robust vs non-robust training.}
\label{tab:pricing_benchmark}
\begin{tabular}{lcccc}
\toprule
Policy & Eval reward & Eval revenue & COI leakage & Margin collapse rate \\
\midrule
Robust policy & \textit{TBD} & \textit{TBD} & \textit{TBD} & \textit{TBD} \\
Non-robust baseline (\texttt{--no-robust}) & \textit{TBD} & \textit{TBD} & \textit{TBD} & \textit{TBD} \\
\bottomrule
\end{tabular}
\end{table}

This comparison isolates the effect of robustness terms from model capacity and optimization settings, and provides the benchmark needed for interpreting the value of COI-aware control.


\subsection{Interpretation and Insights}
Between-class divergence substantially above the intra-class null indicates that the two actor classes are behaviorally separable at the transition-kernel level. In pricing experiments, this is the condition required for separability to act as a useful control signal rather than just an auxiliary classifier score.

\subsection{Anomalies}
