fixing grammar

paper/src/chapters/01-intro.tex

@@ -68,5 +68,4 @@ Extract final result $r$ from terminal state\;
 
 The previously described goal of distinguishability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$. We propose a robust optimization objective defined in our methodology, transforming the pricing problem into a form of distributionally robust optimization \parencite{kuhn_distributionally_2025} in which the learner guards against adversarial contamination in observed demand \emph{distributions}. The decision rule (in the policy) must perform when the data-generating mechanism is not a single known distribution but any member of an ambiguity set described only partially. Here that mechanism is a mixture whose weight and components need not be stationary.
 
-% The contributions of this thesis are best understood as a dependency chain centered on dynamic pricing under agent-mediated contamination. The work begins with a formal account of why non-human reconnaissance threatens pricing power, then constructs a controlled platform capable of generating the interaction data needed to study that threat empirically. On top of that substrate, session behavior is modeled to determine whether human and agent traffic can be separated from behavioral traces alone. The resulting contamination estimate is then translated into the pricing loop itself, where it serves as an operational signal for robust control under distributional uncertainty. The breadth of the thesis is therefore a consequence of the problem structure: the theoretical, behavioral, systems, and control components are not separate projects, but successive requirements of a single argument.
-Our work's contributions are best understoo as a dependency chain centered around dynamic pricing. The work begins with a formal account of why a non human mediator threatens pricing power, then we construct a platform capable of generating the intraction data needed for our study of that threat. On to of that \textit{substrate} we build behaioral models to determine whether human and agent traffic can be separated. The resulting contamination estimate is then translated into the pricing core itself, where it serves as key signal for robust control under distributionall uncertainty. The breadth of the thesis is therefore a consequence of the problem structure: the theoretical, behavioral, systems, and control components are not separate projects, but successive requirements of a single argument.
+Our work's contributions are best understood as a dependency chain centered around dynamic pricing. The work begins with a formal account of why a non-human mediator threatens pricing power, then we construct a platform capable of generating the interaction data needed for our study of that threat. On top of that \textit{substrate} we build behavioral models to determine whether human and agent traffic can be separated. The resulting contamination estimate is then translated into the pricing core itself, where it serves as a key signal for robust control under distributional uncertainty. The breadth of the thesis is therefore a consequence of the problem structure: the theoretical, behavioral, systems, and control components are not separate projects, but successive requirements of a single argument.

paper/src/chapters/03-methodology.tex

@@ -504,9 +504,10 @@ The inner minimization selects the contamination candidate that makes the penali
 
 For the baseline engine reported here, we intentionally use the constant query-tax surrogate to keep the mechanism minimal:
 \begin{equation}
-r_t = \ldots - \lambda\,f(\tau_t')\,c_{\text{info}}
+\label{eq:baseline_step_reward}
+r_t = R\!\left(p_t,\hat{Q}_t\right) - \lambda\,f(\tau_t')\,c_{\text{info}} - \eta_{\text{ux}}\,\text{UX}(\tau_t', p_t)
 \end{equation}
-with fixed $c_{\text{info}}>0$.
+with fixed $c_{\text{info}}>0$, matching the leakage term $\text{COI}_{\text{leak}}=f(\tau_t')\,c_{\text{info}}$ and the user-experience penalty already introduced in~\eqref{eq:robust_policy}.
 
 
 Another possible extension is to adapt the ambiguity radius online, e.g., $\epsilon(\Delta_H)$, so the Wasserstein ball changes with live divergence. We keep this as future work and retain a fixed-radius setup because Wasserstein ambiguity already handles heavy-tail and ``black swan'' behavior without absolute continuity assumptions \parencite{kuhn_wasserstein_2024}.

paper/src/chapters/06-conclusion.tex

@@ -22,4 +22,4 @@ Our work has yielded a broad set of dependencies which we carefully orchestrated
 
 Several constraints are intentional and could be relaxed later. Action weights in the demand proxy are hand-set; learning them from data is an obvious next step. The Stackelberg interface assumes a clean alternation between platform move and market response; richer histories (multi-agent, multi-platform) would need a less rigid state definition. Non-perishable catalog supply in the simulator widens the sim-to-real gap for inventory-constrained domains. Within-session contamination is modeled as stable; time-varying $\alpha$ inside a session would better match some attack patterns.
 
-Before any deployment, human baselines should grow beyond the convenience sample used here, and catalog scaling laws should be re-checked when transition matrices grow with SKU count. For the deployment of this methodology presented in our work.
+Before any deployment, human baselines should grow beyond the convenience sample used here, catalog scaling laws should be re-checked when transition matrices grow with SKU count, and the full pipeline should be re-validated under production traffic volumes, governance constraints, and product mixes.