From 31c65a8c6abbd5e2daa81d51dc23751599898a89 Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Tue, 16 Dec 2025 19:14:09 +0100 Subject: [PATCH] fixing erro and thiknig about big picture --- paper/src/chapters/01-intro.tex | 2 +- paper/src/chapters/03-methodology.tex | 13 ++++++++++++- 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/paper/src/chapters/01-intro.tex b/paper/src/chapters/01-intro.tex index bbbc719..d7b3027 100644 --- a/paper/src/chapters/01-intro.tex +++ b/paper/src/chapters/01-intro.tex @@ -8,7 +8,7 @@ \section{Introduction} -In this paper we present an exploration and defense against the presence of new commercial entities in digitally powered platforms. This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across a large variety of digital systems, construction of a discriminative model (to prove separability) as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and finally establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in the presence of such non-human learners. +In this paper we present an exploration and defense against the presence of new commercial entities in digitally powered platforms, preserving market equilibrium in the age of AI. This research establishes the following contributions: definition and formalization of non-human transactors in e-commerce platforms, development of a testing-ground for capturing the behavioral essence of these transactors across a large variety of digital systems, construction of a discriminative model (to prove separability) as a strong learner for downstream mitigation of contamination by non-human entities, translation of such learned separability into existing dynamic pricing machine learning loops, and finally establishment of a high-level KPI-affecting causal effect and cost-saving framework for the future of internet commerce in the presence of such non-human learners. This research effort touches a large variety of domains, spanning behavioral economics for understanding the rationality of behavior as theorized by the concept of homo economicus, agent-based modeling to translate our learned separability into disjoint dynamic pricing systems, reinforcement learning which serves as the SOTA for price-learners, and dynamic pricing and market equilibrium theory to understand the risks of possible supra-competitive pricing phenomena in cases of adversarial pricing systems driving the market out of equilibrium. diff --git a/paper/src/chapters/03-methodology.tex b/paper/src/chapters/03-methodology.tex index 3f1f6ee..3e2f599 100644 --- a/paper/src/chapters/03-methodology.tex +++ b/paper/src/chapters/03-methodology.tex @@ -34,6 +34,17 @@ What we define in this game is the interaction between the pricing system and no +Putting it all together for formalization, we have a complete mapping of our pipeline + +\begin{equation} + \tau \to x_s \to \hat{\pi} \to \tilde{q_t} \to p_{t+1} \\ + p_{t+i}(\tau) = \hat{\pi}(x_s) \\ + % explixitly fully develop an expansion of showing the mappin from p to tau and how that carries all information and from that we can identify where to intercept with our treatments. +\end{equation} + + + + \subsection{Cost of Information Framework} @@ -139,7 +150,7 @@ R = \text{revenue} - \text{COI} - \text{UX friction index} As part of our reward engineering we want to take into account the cost of information in our reward with a weight. As seen in most other dynamic pricing systems, regret is most often use to guide the policy development, which in our case serves very well in comparing the ground truth and estimated demand. For us the regret is the revenue loss compared to the oracle which has perfect information access. \begin{equation} - \text{Regret}(\p\i) = TR(\pi_\text{oracle}) - TR(\pi) + \text{Regret}(\pi) = TR(\pi_\text{oracle}) - TR(\pi) \end{equation} % TR= total revenue % Regret is the revenue loss compared to oracle with perfect information: