adding significant weight in prices

paper/src/chapters/02-literature-review.tex

@@ -9,6 +9,8 @@ A specific class or taxon of this \textit{machina economicus}, the Large Languag
 
 We must however acknowledge the current SOTA as presented by OSWORLD simulations in \cite{Xie} have demonstrated that multi-modal tasks across desktop and web interaction modes, have a top-performing score of only 12.24\% sucess, whereas humans have a higher 72\% sucess rate. This weakness matters for this research because it clarifies the near-term threat model: practical exploitation does not require a fully competent ``computer assistant'', only enough automation to perform high-volume reconnaissance actions (search/filter/open product pages, probe availability/price boundaries) that can contaminate behavioral signals. With the expected growth of these capabilities, this threat only becomes more perilous to revenue management systems.
 
+We model an agent session as producing some events with lower in-session conversion levels relative to humans, this we state in our assumption that $P(\text{purchase} \vert A) \ll P\text{purcahse} \vert H)$ but with a potentially higher volatility in $\hat{q}$, which we observe through the look-to-book metrics in our simulation.
+
 \subsection{Economic Agents: From Homo Economicus to Machina Economicus}
 
 Existing behvarioal economic models tend to be criticized for the assumption of rational behavior, as is embodied in the term of homo economicus. The definition of a machina economicus by \cite{Parkes2015} is quite apropriate for our case, particularly becuase these assumptions of rationality have been argued to be a very adequeate reference for AI research by \cite{Varian}. For modeling this behavior, the trajectories of these agents can be formally defined to be partially observable Markov decision processes. \cite{Xie} Agents are however not to be confused with web-bots which have previously been known as automated software applications or scrapers which are set with a purpose of carrying out specific tasks on the internet, without a higher level of internal judgement. \cite{Imperva2025} In our research, we refer to this actor simply as an Agent belonging to the distribution $A$.