From 8c77d8de1786ef38103fd15e6a16b0ee1b1eb11d Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Mon, 15 Dec 2025 20:19:48 +0100 Subject: [PATCH] adding significant weight in prices --- paper/src/chapters/02-literature-review.tex | 2 ++ paper/src/chapters/03-methodology.tex | 7 +++++-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/paper/src/chapters/02-literature-review.tex b/paper/src/chapters/02-literature-review.tex index fc9d0e4..04634d8 100644 --- a/paper/src/chapters/02-literature-review.tex +++ b/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 $A$. diff --git a/paper/src/chapters/03-methodology.tex b/paper/src/chapters/03-methodology.tex index 58048fd..4f20df9 100644 --- a/paper/src/chapters/03-methodology.tex +++ b/paper/src/chapters/03-methodology.tex @@ -50,7 +50,7 @@ The architectuer of this platform begins with the deployed web-apps posting inte \subsubsection{Online Dynamic Pricing} -The dynamic pricing done is handled by a pipeline which computes a demand estimate on a per-product basis of a specific window of the data, defined by the period $T$ which by default is 5 mintues. This dynamic pricing pipeline computes a demand estimate vector $\hat{q} \in \mathbb{R}^N$ by a weighted sum of interactions for each product, it additionally computes a price elasticity vector $\hat{\epsilon}$ in the same dimensions as our demand. The final features matrix is of the size $N \times 2$ which we translate to a new price vector $\hat{p} \in \mathbb{R}^N$. The transformation that governs this dynamic pricing is a very simple surge-based pricing: +The dynamic pricing done is handled by a pipeline which computes a demand estimate on a per-product basis of a specific window of the data, defined by the period $T$ which by default is 5 mintues. This dynamic pricing pipeline computes a demand estimate vector $\hat{q} \in \mathbb{R}^N$ by a weighted sum of interactions for each product, it additionally computes a price elasticity vector $\hat{\epsilon}$ in the same dimensions as our demand. The final features matrix is of the size $N \times 2$ which we translate to a new price vector $\hat{p} \in \mathbb{R}^N$. The transformation that governs this dynamic pricing is a very simple surge-based pricing (a special case of our later defined policy $\pi$): \begin{equation} \hat{p}_i = \begin{cases} @@ -114,7 +114,10 @@ R &= \text{revenue} - \text{COI} - \text{UX frinction index} $$ -As part of our reward engineering we want to take inot account the cost of information in our reward with a weight. Our pricing engine can be modeled by the mapping: +As part of our reward engineering we want to take inot account the cost of information in our reward with a weight. + + +Our pricing engine can be modeled by the mapping: $$ \pi : \mathbb{R}^N_+ \times H_t \to \mathbb{R}_+^N $$