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adding significant weight in prices

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Daniel Rosel
2025-12-15 20:19:48 +01:00
parent 9a02a04117
commit 8c77d8de17
2 changed files with 7 additions and 2 deletions
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paper/src/chapters/02-literature-review.tex
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@@ -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 $A$.

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paper/src/chapters/03-methodology.tex
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@@ -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
$$