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Daniel Rosel
2026-03-08 13:27:17 +01:00
parent ec880db444
commit 4b89b64674
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8
paper/src/chapters/04-results.tex
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@@ -6,9 +6,11 @@
\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.
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 and bias.
\begin{table}[ht]
\centering
@@ -25,7 +27,9 @@ Agent intra-class split & 1.2065 & 1.2607 & 0.2177 & 4.2345 \\
\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.
For this run ($n_H=11$, $n_A=7$, $B=100$), the empirical 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.
% TODO: instead could we do a simple t test to see the difference in the means in some way? That way we can yield a P value
\subsection{Experimental Outcomes}