From 0fd145901149abbd90e56a550da0af1868b7e73c Mon Sep 17 00:00:00 2001 From: Daniel Rosel Date: Thu, 9 Apr 2026 22:03:53 +0200 Subject: [PATCH] fixing summary top --- paper/src/summary.tex | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paper/src/summary.tex b/paper/src/summary.tex index 17151eb..f400853 100644 --- a/paper/src/summary.tex +++ b/paper/src/summary.tex @@ -21,7 +21,7 @@ \vspace{0.75em} -Large language model (LLM) agents are spreading in e-commerce; one consequence is intermediaries that can separate information gathering from transaction execution. +Large language model (LLM) agents are spreading in e-commerce, one consequence is intermediaries that can separate information gathering from transaction execution. This thesis studies dynamic pricing when agents reconnoitre in isolated sessions and thereby weaken the \emph{Cost of Information} (COI), the premium platforms typically extract once demand signals are expressed. The key technical risk is not ``agents buying things'' per se, but agents shaping the behavioral and demand signals that downstream pricing systems consume and depend on \parencite{xia_evaluation-driven_2025}. Dynamic pricing assumes demand proxies are behaviorally meaningful, while bot detection aims at security and access control.