some more references and theory links

paper/src/bib/references.bib

@@ -102,3 +102,30 @@
    title = {Distributionally Robust Optimization},
    year = {2025}
 }
+@article{Yokoo2004,
+   abstract = {We examine the effect of false-name bids on combinatorial auction protocols. False-name bids are bids submitted by a single bidder using multiple identifiers such as multiple e-mail addresses. The obtained results are summarized as follows: (1) the Vickrey-Clarke-Groves (VCG) mechanism, which is strategy-proof and Pareto efficient when there exists no false-name bid, is not false-name-proof; (2) there exists no false-name-proof combinatorial auction protocol that satisfies Pareto efficiency; (3) one sufficient condition where the VCG mechanism is false-name-proof is identified, i.e., the concavity of a surplus function over bidders. © 2003 Elsevier Inc. All rights reserved.},
+   author = {Makoto Yokoo and Yuko Sakurai and Shigeo Matsubara},
+   doi = {10.1016/S0899-8256(03)00045-9},
+   issn = {08998256},
+   issue = {1},
+   journal = {Games and Economic Behavior},
+   keywords = {Auction,Mechanism design,Strategy-proof},
+   pages = {174-188},
+   publisher = {Academic Press Inc.},
+   title = {The effect of false-name bids in combinatorial auctions: New fraud in internet auctions},
+   volume = {46},
+   year = {2004}
+}
+
+@inproceedings{Feldman2004,
+   abstract = {We develop a model to study the phenomenon of free-riding in peer-to-peer (P2P) systems. At the heart of our model is a user of a certain type, an intrinsic and private parameter that reflects the user's willingness to contribute resources to the system. A user decides whether to contribute or free-ride based on how the current contribution cost in the system compares to her type. When the societal generosity (i.e., the average type) is low, intervention is required in order to sustain the system. We present the effect of mechanisms that exclude low type users or, more realistic, penalize free-riders with degraded service. We also consider dynamic scenarios with arrivals and departures of users, and with whitewashers: users who leave the system and rejoin with new identities to avoid reputational penalties. We find that when penalty is imposed on all newcomers in order to avoid whitewashing, system performance degrades significantly only when the turnover rate among users is high.},
+   author = {Michal Feldman and Christos Papadimitriou and John Chuang and Ion Stoica},
+   doi = {10.1145/1016527.1016539},
+   isbn = {158113942X},
+   booktitle = {Proceedings of the ACM SIGCOMM 2004 Workshops},
+   keywords = {Cheap pseudonyms,Cooperation,Equilibrium,Exclusion,Free-riding,Identity cost,Incentives,Peer-to-peer,Whitewashing},
+   pages = {228-235},
+   publisher = {Association for Computing Machinery},
+   title = {Free-riding and whitewashing in peer-to-peer systems},
+   year = {2004}
+}

paper/src/chapters/01-intro.tex

@@ -18,7 +18,7 @@ The current innovation boom in generative artificial intelligence and its applic
 
 The key stakeholders affected by the threat of increasing agent-driven traffic include online businesses and platform operators (especially in bot-heavy sectors like retail, travel, and financial services), their security, fraud, and engineering teams, end users whose accounts and data are exposed and whose experience degrades, regulators and legal stakeholders responding to breaches and fraud, and the attackers or bot operators driving the automation \cite{Imperva2025}.
 
-The industry has already seen legal action in cases like Amazon against Perplexity \cite{AmazonvsPerplexity}, stemming from the difficulty of identifying traffic from hybrid systems like the Commet browser. This paper explores such systems to better understand what the interaction data looks like and what it means for dynamic pricing and recommendation systems downstream. This observed impact indicates a need for prevention of secondary negative effects on the ``legacy'' systems which power modern revenue sources for many companies. Dynamic pricing algorithms rely on directly translating demand features $q$ to new price assignments $\hat{p}$ across a catalogue of products of size $N$.
+The industry has already seen legal action in cases like Amazon against Perplexity \cite{AmazonvsPerplexity}, stemming from the difficulty of identifying traffic from hybrid systems like the Commet browser. This paper explores such systems to better understand what the interaction data looks like and what it means for dynamic pricing and recommendation systems downstream. This observed impact indicates a need for prevention of secondary negative effects on the ``legacy'' systems which power modern revenue sources for many companies. Dynamic pricing algorithms rely on directly translating demand features $q$ to new price assignments $\hat{p}$ across a catalogue of products of size $N$. This opens opportunities to design a \textit{tabula rasa} of digital market mechanisms that will shape the future of commerce in the age of artificial intelligence.
 
 \subsection{Solution Space Overview}
 Dynamic pricing systems, as presented in \cite{Mueller2019}, often deal with sparse low-rank data of demand signals which, combined with contamination from agents, creates complex interactions that impact pricing. To further complicate the problem, certain commercial settings such as the one presented in \cite{Amjad2017} must address the true demand of products under censored observations. This provides a formulation for handling demand in our case with multiple kinds of commercial mediators: $\hat{q} \gets q_A + q_H$ where $q_A$ represents the distribution of demand generated by agentic mediators and $q_H$ represents that of true human demand, these are two distinct populations with divergent objective functions.
@@ -54,8 +54,4 @@ Extract final result $r$ from terminal state\;
 \end{algorithm}
 
 
-The previously described goal of separability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$. We propose a robust optimization objective defined in our methodology, transforming the pricing problem into a form of Distributionally Robust Optimization \cite{Kuhn2025} where the learner must guard against adversarial contamination in observed demand distributors.
-
-% A Distributionally Robust Optimization (DRO) problem is fundamentally about making decisions that perform well not just for a single estimated probability distribution, but for any distribution within a plausible set (called the "Ambiguity Set").
-% In standard optimization, you assume you know the distribution of your data (e.g., "Demand is Gaussian with mean μ") and you optimize for the average case. In DRO, you admit you don't know the exact distribution—perhaps the mean shifts, or the tail is heavier. You optimize for the worst-case distribution within your uncertainty set.
-% he observed demand q^​t​ is a mixture of two distributions: The parameter αt​ (the percentage of traffic that is non-human) is unknown and non-stationary. It defines the distribution of the data you observe.
+The previously described goal of separability allows us to formulate a task which entails taking raw interaction data for either actor and creating a composite demand estimate $\hat{q}$. We propose a robust optimization objective defined in our methodology, transforming the pricing problem into a form of Distributionally Robust Optimization \cite{Kuhn2025} where the learner must guard against adversarial contamination in observed demand distributors. In this setting we must learn to make decision that perform under the assumption of not having a single estimated probability distribution but under an ambiguity set of any distribution, of which we have limited information. In our case as stated is a mixture of distributions with a parameter which is unknown and non-stationary.

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

@@ -1,6 +1,6 @@
 \section{Literature Review}
 
-To better understand all wedges of the work, we must start by exploring the nature of agents and agentic computer use and web automation, complementing that with economic reasoning and strategic interaction. The final surface to cover, leads us to data-driven dynamic pricing under uncertainty. 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. The introduction of these mediating actor entities into economic systems, is further creating a threat of false-name bidding, which prior research has explored in a trading context. Dynamic pricing assumes demand proxies are behaviorally meaningful, while bot detection aims at security and access control. The missing bridge is a principled framework for separating non-human reconnaissance from genuine human demand expression and integrating that separation into pricing heuristics without degrading legitimate user experience (in our research tracked by the user-experience index). This gap, is what our contribution aims to address - particularly for the aforementioned stakeholder groups.
+To better understand all wedges of the work, we must start by exploring the nature of agents and agentic computer use and web automation, complementing that with economic reasoning and strategic interaction. The final surface to cover, leads us to data-driven dynamic pricing under uncertainty. 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. The introduction of these mediating actor entities into economic systems, is further creating a threat of false-name bidding \cite{Yokoo2004}, which prior research has explored in a trading context. Other research on pseudonyms in dynamic systems, demonstrate whitewashing in AI agents which can ignore defensive mechanisms by re-entry with different identities \cite{Feldman2004}. Dynamic pricing assumes demand proxies are behaviorally meaningful, while bot detection aims at security and access control. The missing bridge is a principled framework for separating non-human reconnaissance from genuine human demand expression and integrating that separation into pricing heuristics without degrading legitimate user experience (in our research tracked by the user-experience index). This gap, is what our contribution aims to address, particularly for the aforementioned stakeholder groups.
 
 \subsection{Agent Taxonomy and Definitions}
 
@@ -15,20 +15,17 @@ We model an agent session as producing some events with lower in-session convers
 
 Existing behavioral 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 appropriate for our case, particularly because these assumptions of rationality have been argued to be a very adequate 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 $A$.
 
-This economic framing also helps separate two related but distinct phenomena of agents as buyers (changing market demand composition), and agents as information gatherers (changing the observed interactions used by pricing/recommendation systems). The thesis focuses on the second, where information acquisition strategically precedes purchase execution.
+This economic framing also helps separate two related but distinct phenomena of agents as buyers (changing market demand composition), and agents as information gatherers (changing the observed interactions used by pricing/recommendation systems). The thesis focuses on the second, where information acquisition strategically precedes purchase execution. We do not however dismiss the proposed expectation that existing economic systems serving humans, will not be populated by AIs across multiple channels and with various possibly misaligned goals as stated by \cite{Parkes2015}.
 
 
 \subsection{Problem Evidence and Market Impact}
 
-% For dynamic pricing systems that map observed demand features into updated prices, contamination is not only a security issue but a statistical one: automated reconnaissance can distort session-level funnels (view-to-cart, look-to-book), inflate demand proxies, and bias elasticity estimates. The practical consequence is mispricing—either supra-competitive outcomes driven by inflated demand signals, or defensive price suppression that harms margin and legitimate customer experience.
+The statistical issue of contamination in dynamic pricing systems that observe demand features as a means to update prices has been documented in various previous contexts. The airline industry (which has accounted for 24\% of observed disruptions) has seen malicious activity with a measureable impact on skewing key performance indicators by behavior visible in the look-to-book metrics. Excessive reconnaissance traffic inflates search volume without corresponding completed bookings, thereby skewing demand forecasts and disrupting dynamic pricing models. Demand proxies have also been observed to cause significant threat to inventory management by creating artificial scarcity that distorts the demand-supply relationships in the enterprise model. Censored demand as shown in \cite{Amjad2017} can also be observed in low-bias demand under-estimation caused by a distortion effect coming from non-human traffic data. \cite{Imperva2025}
 
-The statistical issue of contamination in dynamic pricing systems that observe demand features as a means to update prices has been documented in various previous contexts. The airline industry (which has accounted for 24\% of observed disruptions) has seen malicious activity with a measureable impact on skewing key performance indicators by behavior such as look-to-book. Excessive reconnaissance traffic inflates search volume without corresponding completed bookings, thereby skewing demand forecasts and disrupting dynamic pricing models. Demand proxies have also been observed to cause significant threat to inventory management by creating artificial scarcity that distorts the demand-supply relationships in the enterprise model. Censored demand as shown in \cite{Amjad2017} can also be observed in low-bias demand under-estimation caused by a distortion effect coming from non-human traffic data. \cite{Imperva2025}
-
-% • Defensive Price Suppression (Harm to Margin): When algorithms operate on heavily contaminated or noisy data, the risk associated with inaccurate price setting increases. To mitigate the unknown risk introduced by bad data, some systems may default to defensive price suppression to ensure sales continuity, thereby unnecessarily harming margins and resulting in lost revenue. Furthermore, systems that are poorly constrained can learn undesirable behaviors like price gouging in pursuit of short-term rewards if not properly monitored.
-\cite{Mullapudi}
+When dynamic pricing algorithms operate on highly contaminated or noisy data, the risk grows significantly in creating inaccurate price inferences. The emergent mitigation driven by un-informed reward and regret signals might lead to price suppression for sales continuity which results in harming margins and resulting in a revenue loss. System that poorly fit undesired behavior might result in price gouging, which calls for strong guardrails while preserving targeted business strategy. \cite{Mullapudi}
 
 
-Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
+%Documented instances of agent-driven market disruptions - Quantitative evidence of pricing manipulation - Case studies from affected industries
 
 \subsection{Theoretical Foundations: Economic Parallels}