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chore: updating comments fro mfeedback

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Daniel Rosel
2026-03-31 09:06:15 +02:00
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@@ -48,14 +48,14 @@ These behavioral signals serve as inputs for a Distributionally Robust Reinforce
\begin{description}
\item[Agent $A$] A non-human actor, typically an LLM-driven system that executes web actions toward a goal.
\item[Human $H$] A human participant interacting with the platform to complete a task.
\item[Actor Type $\theta$] A latent class parameter describing whether a session is generated by a human or an agent profile.
\item[Actor Class $Y$] A latent class parameter describing whether a session is generated by a human or an agent profile.
\item[Platform] A web interface exposing purchasable items and their offered prices.
\item[Session $s$] A bounded interaction record tied to one actor and one session identifier.
\item[Event $e_{s,k}$] A single interaction tuple in a session, including action, item target, and timestamp.
\item[Trajectory $\tau_s$] The ordered sequence of events generated within a session.
\item[Demand Proxy $\hat{q}_{t,i}$] A weighted aggregate of observed actions used as an operational substitute for latent demand.
\item[Action Weight Function $\omega(a)$] A mapping from action type to signal strength in the demand proxy.
\item[True Demand $d(p;\theta)$] The latent purchase response as a function of price and actor type.
\item[True Demand $d(p\mid Y,\theta)$] The latent purchase response as a function of price, actor class, and latent type.
\item[Contamination $\alpha$] The proportion of agent-generated traffic in the session mixture.
\item[Non-stationary Noise $\epsilon_t$] Time-varying residual variation not explained by the actor mixture.
\item[Pricing Policy $\pi(\tau)$] A function mapping observed interaction history to an offered price.