changed to new test method for singificance

paper/src/bib/references.bib

@@ -616,3 +616,17 @@ Volume: 21},
 	year = {2026},
 	file = {Snapshot:/home/velocitatem/Zotero/storage/N724QGF6/v4.html:text/html},
 }
+
+@article{mann_test_1947,
+	title = {On a {Test} of {Whether} one of {Two} {Random} {Variables} is {Stochastically} {Larger} than the {Other}},
+	volume = {18},
+	url = {https://doi.org/10.1214/aoms/1177730491},
+	doi = {10.1214/aoms/1177730491},
+	abstract = {Let x and y be two random variables with continuous cumulative distribution functions f and g. A statistic U depending on the relative ranks of the x's and y's is proposed for testing the hypothesis f = g. Wilcoxon proposed an equivalent test in the Biometrics Bulletin, December, 1945, but gave only a few points of the distribution of his statistic. Under the hypothesis f = g the probability of obtaining a given U in a sample of n x's and m y's is the solution of a certain recurrence relation involving n and m. Using this recurrence relation tables have been computed giving the probability of U for samples up to n = m = 8. At this point the distribution is almost normal. From the recurrence relation explicit expressions for the mean, variance, and fourth moment are obtained. The 2rth moment is shown to have a certain form which enabled us to prove that the limit distribution is normal if m, n go to infinity in any arbitrary manner. The test is shown to be consistent with respect to the class of alternatives f(x) {\textgreater} g(x) for every x.},
+	number = {1},
+	journal = {The Annals of Mathematical Statistics},
+	author = {Mann, H. B. and Whitney, D. R.},
+	year = {1947},
+	note = {Publisher: Institute of Mathematical Statistics},
+	pages = {50 -- 60},
+}

paper/src/chapters/03-methodology.tex

@@ -303,13 +303,9 @@ To train a robust pricing learner, we need a simulator that can generate realist
 \subsubsection{Ground-Truth Separability}
 Because sessions are collected under controlled experimental conditions where each actor is assigned a known type at the start of the trial, labels $\theta_s \in \{H, A\}$ are available as ground truth rather than as the output of a heuristic classifier. We therefore estimate separate transition kernels directly from each labeled partition $\mathcal{D}_H$ and $\mathcal{D}_A$, treating the resulting $\hat{\mathcal{T}}_H$ and $\hat{\mathcal{T}}_A$ as the ground-truth behavioral profiles for each class. We then ask a direct methodological question: are the kernels separable enough to justify downstream pricing control that depends on that separability?
 
-To answer this, we compute average KL divergence between transition probability matrices. This statistic gives global separability and event-level diagnostics at the same time. To test whether the observed between-class value exceeds finite-sample estimation noise, we compute an intra-class bootstrap baseline by repeatedly splitting $\mathcal{D}_H$ and $\mathcal{D}_A$ into two random halves, fitting a transition kernel on each half, and re-computing the same average KL statistic for each split.
+To answer this, we compute per-session KL divergence scores against both class-level centroids. For each session $s$ in either partition, we fit a session-level event transition kernel $\hat{\mathcal{T}}_s$ from that session's trajectory alone, then compute its average KL divergence to the human centroid ($\Delta_{H,s}$) and to the agent centroid ($\Delta_{A,s}$). The per-session separability score is the gap $\Delta_{H,s} - \Delta_{A,s}$: a negative value indicates proximity to human behavior, a positive value indicates proximity to agent behavior.
 
-Formally, for $B$ bootstrap splits per class we obtain reference samples $\{d_{H,b}^{\text{intra}}\}_{b=1}^B$ and $\{d_{A,b}^{\text{intra}}\}_{b=1}^B$, then compare the between-class divergence $d^{\text{inter}}$ against the pooled null distribution. We report pooled mean and variance, lift ratio $d^{\text{inter}}/\mathbb{E}[d^{\text{intra}}]$, and the empirical one-sided p-value
-\begin{equation}
-\hat p = \frac{1 + \sum_{j=1}^{2B}\mathbf{1}\{d_j^{\text{intra}} \ge d^{\text{inter}}\}}{2B + 1},
-\end{equation}
-which gives a direct significance check for separability before using divergence-derived centroid control signals in pricing.
+The normality assumption cannot be made for KL divergence distributions, which are right-skewed and bounded below by zero, so we do not use a Student's $t$-test. Instead we apply a Mann-Whitney $U$ test \parencite{mann_test_1947} on the per-session gap scores between the two groups. The Mann-Whitney test is a rank-based nonparametric test that compares the stochastic ordering of two independent samples without distributional assumptions, making it appropriate for small samples drawn from skewed populations. We report $U$, the exact two-sided $p$-value, and group-level descriptive statistics for the gap scores.
 
 \begin{definition}[Kullback-Leibler Divergence for Transition Distributions]
 Let $P_e$ and $Q_e$ be categorical distributions over destination states following event $e$, derived from human and agent trajectories respectively. The KL divergence between these distributions is:

paper/src/chapters/04-results.tex

@@ -10,26 +10,25 @@
 
 \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 and bias.
+Separability between human and agent sessions is evaluated by computing per-session divergence gap scores $\Delta_{H,s} - \Delta_{A,s}$ and comparing the two groups with a Mann-Whitney $U$ test. Table~\ref{tab:divergence_significance} reports the group-level descriptive statistics for the gap scores and the test result.
 
 \begin{table}[ht]
 \centering
-\caption{Divergence significance using intra-class bootstrap baseline (B=100 per class).}
+\caption{Per-session divergence gap ($\Delta_H - \Delta_A$) by actor class with Mann-Whitney $U$ test.}
 \label{tab:divergence_significance}
-\begin{tabular}{lcccc}
+\begin{tabular}{lccc}
 \toprule
-Metric & Mean KL & Std & 5\% quantile & 95\% quantile \\
+Group & $n$ & Mean gap & Std \\
 \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 \\
+Human sessions & 11 & $-3.3522$ & $2.6748$ \\
+Agent sessions & 6 & $+1.6482$ & $2.8349$ \\
+\midrule
+\multicolumn{4}{l}{Mann-Whitney $U = 2.0$, $p = 0.0006$ (two-sided)} \\
 \bottomrule
 \end{tabular}
 \end{table}
 
-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
+The sign structure is consistent with the theoretical expectation: human sessions produce negative gap scores (closer to the human centroid, far from the agent centroid) while agent sessions produce positive gap scores (closer to the agent centroid). The two-sided $p$-value of $0.0006$ indicates near-complete rank separation between the groups at $n_H=11$, $n_A=6$, providing strong evidence that the transition kernels are separable enough to justify their use as a control signal in downstream pricing.
 
 
 \subsection{Experimental Outcomes}
@@ -54,6 +53,6 @@ This comparison isolates the effect of robustness terms from model capacity and
 
 
 \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.
+The Mann-Whitney result ($U=2.0$, $p<0.001$) confirms that per-session divergence gaps separate the two actor classes with near-zero overlap in rank ordering. This is the condition required for separability to act as a useful control signal in the pricing loop rather than just an auxiliary classifier score.
 
 \subsection{Anomalies}