
@article{arnoud_v_den_boer_dynamic_2015,
	title = {Dynamic pricing and learning: {Historical} origins, current research, and new directions},
	volume = {20},
	url = {https://www.sciencedirect.com/science/article/pii/S1876735415000021},
	doi = {10.1016/j.sorms.2015.03.001},
	number = {1},
	journal = {Surveys in Operations Research and Management Science},
	author = {{Arnoud V. den Boer}},
	month = jun,
	year = {2015},
	pages = {1--18},
	file = {PDF:/home/velocitatem/Zotero/storage/NUAGDYER/memo2025.pdf:application/pdf},
}

@article{iliou_detection_2021,
	title = {Detection of {Advanced} {Web} {Bots} by {Combining} {Web} {Logs} with {Mouse} {Behavioural} {Biometrics}},
	volume = {2},
	url = {https://dl.acm.org/doi/10.1145/3447815},
	doi = {10.1145/3447815},
	number = {3},
	journal = {Digital Threats: Research and Practice},
	author = {Iliou, Christos and Kostoulas, Theodoros and Tsikrika, Theodora and Katos, Vasilis and Vrochidis, Stefanos and Kompatsiaris, Ioannis},
	year = {2021},
	pages = {1--26},
	file = {PDF:/home/velocitatem/Zotero/storage/Q7J5EBEJ/3447815.pdf:application/pdf},
}

@phdthesis{salassa_politecnico_2024,
	title = {Politecnico di {Torino} {Algorithmic} {Pricing} in the digital age "{Ethical} considerations on its economic and social implications, and an analysis of possible solutions to overcome its critical issues" {Tutor}: {Candidate}},
	abstract = {Algorithmic pricing is an emerging business practice that uses computational algorithms to determine
the prices of products and services based on a number of dynamic factors. The aim of this thesis is to
draw attention to the existence of these business practices, and the ethical and social implications that
derive from them, and then focus on what could be effective solutions to increase the well-being of
the community.
In Chapter 2 of the thesis, a general introduction to the topic will be made, starting from its history
and its evolution over the years; Chapter 3 will examine the different types of pricing algorithms.
Subsequently, in Chapter 4 we will analyze the sectors in which they are most applicable, and the
relative advantages and disadvantages they bring with them, with a critical analysis of the trade-offs
generated. The effect of algorithmic pricing on competition will be studied, considering how the
ability of algorithms to adapt quickly to market conditions can foster anti-competitive practices, such
as price discrimination. Later, in Chapter 5, we will look at the issue of price transparency and how
the opacity of algorithms can make it difficult for consumers to understand the pricing process and
assess whether they are receiving fair treatment.
To address these ethical issues, several possible solutions will be brought to light, described in
Chapter 6, which will focus on the role of the government, as a regulatory, of the end consumer, who
must be encouraged to educate and inform himself about the use of these practices, and of the
company, as responsible for making its customers aware and acting in compliance with government
laws, for fair and non-discriminatory use.},
	urldate = {2025-11-12},
	school = {Politecnico di Torino},
	author = {Salassa, Fabio and Pautassi, Paolo},
	month = apr,
	year = {2024},
	file = {PDF:/home/velocitatem/Zotero/storage/L95WYQ8B/m-api-06aad998-d926-0d59-5593-82fdce5a678b.pdf:application/pdf},
}

@inproceedings{mueller_low-rank_2019,
	title = {Low-{Rank} {Bandit} {Methods} for {High}-{Dimensional} {Dynamic} {Pricing}},
	booktitle = {Advances in {Neural} {Information} {Processing} {Systems} 32 ({NeurIPS} 2019)},
	author = {Mueller, Jonas W and Syrgkanis, Vasilis and Taddy, Matt},
	year = {2019},
	pages = {15442--15452},
	file = {PDF:/home/velocitatem/Zotero/storage/IZD3C5SR/m-api-26f6207c-cc89-4aed-29b6-34629f18fe9b.pdf:application/pdf},
}

@article{shahidi_coasean_2025,
	title = {The {Coasean} {Singularity}? {Demand}, {Supply}, and {Market} {Design} with {AI} {Agents}},
	abstract = {AI agents—autonomous systems that perceive, reason, and act on behalf of human principals—are poised to transform digital markets by dramatically reducing transaction costs. This chapter evaluates the economic implications of this transition, adopting a consumeroriented view of agents as market participants that can search, negotiate, and transact directly. From the demand side, agent adoption reflects derived demand: users trade off decision quality against effort reduction, with outcomes mediated by agent capability and task context. On the supply side, firms will design, integrate, and monetize agents, with outcomes hinging on whether agents operate within or across platforms. At the market level, agents create efficiency gains from lower search, communication, and contracting costs, but also introduce frictions such as congestion and price obfuscation. By lowering the costs of preference elicitation, contract enforcement, and identity verification, agents expand the feasible set of market designs but also raise novel regulatory challenges. While the net welfare effects remain an empirical question, the rapid onset of AI-mediated transactions presents a unique opportunity for economic research to inform real-world policy and market design.},
	language = {en},
	author = {Shahidi, Peyman and Rusak, Gili and Manning, Benjamin S and Fradkin, Andrey and Horton, John J},
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/TQCAPJDP/Shahidi et al. - The Coasean Singularity Demand, Supply, and Market Design with AI Agents.pdf:application/pdf},
}

@misc{byrnes_intro_2025,
	title = {Intro to {Brain}-{Like}-{AGI} {Safety}},
	url = {https://osf.io/fe36n_v1},
	doi = {10.31219/osf.io/fe36n_v1},
	abstract = {Suppose we someday build an Artificial General Intelligence (AGI) algorithm using similar principles of learning and cognition as the human brain. How would we use such an algorithm safely? I argue that this is an open technical problem, and my goal is to bring readers with no prior knowledge all the way up to the front-line of unsolved problems. Chapter 1 has background and motivation; Chapters 2-7 are on neuroscience, arguing for a picture of the brain that combines large-scale learning algorithms (e.g. in the cortex) and specific evolved reflexes (e.g. in the hypothalamus and brainstem); and Chapters 8-15 apply those neuroscience ideas to AGI safety. A major theme is the idea that the brain has something like a reinforcement learning reward function, which says that pain is bad, eating-when-hungry is good, etc. I argue that this reward function is centered around the hypothalamus and brainstem, and that all human desires—even "higher" desires for things like compassion and justice—come directly or indirectly from that innate reward function. If future programmers build brain-like AGI, they will likewise have a reward function slot in their source code, in which they can put whatever they want. If they put the wrong thing, the resulting AGI will wind up callously indifferent to human welfare. How might they avoid that? That's an open technical problem, but I will review some ideas and research directions.},
	language = {en},
	urldate = {2025-12-31},
	publisher = {Open Science Framework},
	author = {Byrnes, Steven J.},
	month = mar,
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/ZLJQ4DQ9/Byrnes - 2025 - Intro to Brain-Like-AGI Safety.pdf:application/pdf},
}

@article{shannon_mathematical_1948,
	title = {A {Mathematical} {Theory} of {Communication}},
	volume = {27},
	language = {en},
	journal = {Bell System Technical Journal},
	author = {Shannon, C E},
	month = oct,
	year = {1948},
	file = {PDF:/home/velocitatem/Zotero/storage/FJRFRWK2/Shannon - A Mathematical Theory of Communication.pdf:application/pdf},
}

@misc{noauthor_order_stats_nodate,
	title = {order\_stats},
	file = {PDF:/home/velocitatem/Zotero/storage/D3QRGY9Z/order_stats.pdf:application/pdf},
}

@article{devine_nonlinear_2017,
	title = {Nonlinear {Pricing} with {Costly} {Information} {Acquisition}},
	abstract = {This paper examines a nonlinear pricing model where the ﬁrm can choose to acquire costly information prior to oﬀering contract menus to consumers; such as paying a consultant or investing in machine learning technologies. Information provides the ﬁrm with a signal about consumers types, whose accuracy increases as the ﬁrm acquires larger amounts of information. We show that the ﬁrm chooses to acquire information, only if it can purchase a suﬃcient amount that could alter its initial prior beliefs. Relative to standard settings where ﬁrms cannot acquire information, we identify how information acquisition changes optimal contract oﬀers, equilibrium proﬁts, information rents, and welfare. A better-informed ﬁrm increases its expected proﬁts, but it can also increase expected utility when the cost of information is intermediate. Our results recommend balanced online privacy laws.},
	language = {en},
	author = {Devine, Brett R and Munoz-Garcia, Felix},
	month = nov,
	year = {2017},
	file = {PDF:/home/velocitatem/Zotero/storage/GQ28KVBF/Devine and Munoz-Garcia - Nonlinear Pricing with Costly Information Acquisition.pdf:application/pdf},
}

@misc{wang_learning_2025,
	title = {Learning {Optimal} {Distributionally} {Robust} {Stochastic} {Control} in {Continuous} {State} {Spaces}},
	url = {http://arxiv.org/abs/2406.11281},
	doi = {10.48550/arXiv.2406.11281},
	abstract = {We study data-driven learning of robust stochastic control for infinite-horizon systems with potentially continuous state and action spaces. In many managerial settings–supply chains, finance, manufacturing, services, and dynamic games–the state-transition mechanism is determined by system design, while available data capture the distributional properties of the stochastic inputs from the environment. For modeling and computational tractability, a decision maker often adopts a Markov control model with i.i.d. environment inputs, which can render learned policies fragile to internal dependence or external perturbations. We introduce a distributionally robust stochastic control paradigm that promotes policy reliability by introducing adaptive adversarial perturbations to the environment input, while preserving the modeling, statistical, and computational tractability of the Markovian formulation. From a modeling perspective, we examine two adversary models–current-action-aware and current-action-unaware–leading to distinct dynamic behaviors and robust optimal policies. From a statistical learning perspective, we characterize optimal finite-sample minimax rates for uniform learning of the robust value function across a continuum of states under ambiguity sets defined by the fk-divergence and Wasserstein distance. To efficiently compute the optimal robust policies, we further propose algorithms inspired by deep reinforcement learning methodologies. Finally, we demonstrate the applicability of the framework to real managerial problems.},
	language = {en},
	urldate = {2025-12-29},
	publisher = {arXiv},
	author = {Wang, Shengbo and Meng, Jason and Si, Nian and Blanchet, Jose and Zhou, Zhengyuan},
	month = nov,
	year = {2025},
	note = {arXiv:2406.11281 [stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	file = {PDF:/home/velocitatem/Zotero/storage/RQ8XDSSG/Wang et al. - 2025 - Learning Optimal Distributionally Robust Stochastic Control in Continuous State Spaces.pdf:application/pdf},
}

@misc{ie_recsim_2019,
	title = {{RecSim}: {A} {Configurable} {Simulation} {Platform} for {Recommender} {Systems}},
	shorttitle = {{RecSim}},
	url = {http://arxiv.org/abs/1909.04847},
	doi = {10.48550/arXiv.1909.04847},
	abstract = {We propose RecSim, a configurable platform for authoring simulation environments for recommender systems (RSs) that naturally supports sequential interaction with users. RecSim allows the creation of new environments that reflect particular aspects of user behavior and item structure at a level of abstraction well-suited to pushing the limits of current reinforcement learning (RL) and RS techniques in sequential interactive recommendation problems. Environments can be easily configured that vary assumptions about: user preferences and item familiarity; user latent state and its dynamics; and choice models and other user response behavior. We outline how RecSim offers value to RL and RS researchers and practitioners, and how it can serve as a vehicle for academic-industrial collaboration.},
	urldate = {2025-12-29},
	publisher = {arXiv},
	author = {Ie, Eugene and Hsu, Chih-wei and Mladenov, Martin and Jain, Vihan and Narvekar, Sanmit and Wang, Jing and Wu, Rui and Boutilier, Craig},
	month = sep,
	year = {2019},
	note = {arXiv:1909.04847 [cs]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Human-Computer Interaction, Computer Science - Information Retrieval},
	file = {Preprint PDF:/home/velocitatem/Zotero/storage/CJJI2VQF/Ie et al. - 2019 - RecSim A Configurable Simulation Platform for Recommender Systems.pdf:application/pdf;Snapshot:/home/velocitatem/Zotero/storage/8XJKJTHE/1909.html:text/html},
}

@misc{kuhn_wasserstein_2024,
	title = {Wasserstein {Distributionally} {Robust} {Optimization}: {Theory} and {Applications} in {Machine} {Learning}},
	shorttitle = {Wasserstein {Distributionally} {Robust} {Optimization}},
	url = {http://arxiv.org/abs/1908.08729},
	doi = {10.48550/arXiv.1908.08729},
	abstract = {Many decision problems in science, engineering and economics are affected by uncertain parameters whose distribution is only indirectly observable through samples. The goal of data-driven decision-making is to learn a decision from finitely many training samples that will perform well on unseen test samples. This learning task is difficult even if all training and test samples are drawn from the same distribution—especially if the dimension of the uncertainty is large relative to the training sample size. Wasserstein distributionally robust optimization seeks data-driven decisions that perform well under the most adverse distribution within a certain Wasserstein distance from a nominal distribution constructed from the training samples. In this tutorial we will argue that this approach has many conceptual and computational benefits. Most prominently, the optimal decisions can often be computed by solving tractable convex optimization problems, and they enjoy rigorous out-of-sample and asymptotic consistency guarantees. We will also show that Wasserstein distributionally robust optimization has interesting ramifications for statistical learning and motivates new approaches for fundamental learning tasks such as classification, regression, maximum likelihood estimation or minimum mean square error estimation, among others.},
	language = {en},
	urldate = {2025-12-27},
	publisher = {arXiv},
	author = {Kuhn, Daniel and Esfahani, Peyman Mohajerin and Nguyen, Viet Anh and Shafieezadeh-Abadeh, Soroosh},
	month = nov,
	year = {2024},
	note = {arXiv:1908.08729 [stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Mathematics - Optimization and Control},
	file = {PDF:/home/velocitatem/Zotero/storage/FAWJEK6J/Kuhn et al. - 2024 - Wasserstein Distributionally Robust Optimization Theory and Applications in Machine Learning.pdf:application/pdf},
}

@misc{arunachaleswaran_learning_2025,
	title = {Learning to {Play} {Against} {Unknown} {Opponents}},
	url = {http://arxiv.org/abs/2412.18297},
	doi = {10.48550/arXiv.2412.18297},
	abstract = {We consider the problem of a learning agent who has to repeatedly play a general sum game against a strategic opponent who acts to maximize their own payoﬀ by optimally responding against the learner’s algorithm. The learning agent knows their own payoﬀ function, but is uncertain about the payoﬀ of their opponent (knowing only that it is drawn from some distribution D). What learning algorithm should the agent run in order to maximize their own total utility, either in expectation or in the worst-case over D? When the learning algorithm is constrained to be a no-regret algorithm, we demonstrate how to eﬃciently construct an optimal learning algorithm (asymptotically achieving the optimal utility) in polynomial time for both the in-expectation and worst-case problems, independent of any other assumptions. When the learning algorithm is not constrained to no-regret, we show how to construct an ε-optimal learning algorithm (obtaining average utility within ε of the optimal utility) for both the in-expectation and worst-case problems in time polynomial in the size of the input and 1/ε, when either the size of the game or the support of D is constant. Finally, for the special case of the maximin objective, where the learner wishes to maximize their minimum payoﬀ over all possible optimizer types, we construct a learner algorithm that runs in polynomial time in each step and guarantees convergence to the optimal learner payoﬀ. All of these results make use of recently developed machinery that converts the analysis of learning algorithms to the study of the class of corresponding geometric objects known as menus.},
	language = {en},
	urldate = {2025-12-27},
	publisher = {arXiv},
	author = {Arunachaleswaran, Eshwar Ram and Collina, Natalie and Schneider, Jon},
	month = feb,
	year = {2025},
	note = {arXiv:2412.18297 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Computer Science and Game Theory},
	file = {PDF:/home/velocitatem/Zotero/storage/M6V9LLCS/Arunachaleswaran et al. - 2025 - Learning to Play Against Unknown Opponents.pdf:application/pdf},
}

@misc{li_distributionally_2025,
	title = {Distributionally {Robust} {Optimization} with {Adversarial} {Data} {Contamination}},
	url = {http://arxiv.org/abs/2507.10718},
	doi = {10.48550/arXiv.2507.10718},
	abstract = {Distributionally Robust Optimization (DRO) provides a framework for decision-making under distributional uncertainty, yet its effectiveness can be compromised by outliers in the training data. This paper introduces a principled approach to simultaneously address both challenges. We focus on optimizing Wasserstein-1 DRO objectives for generalized linear models with convex Lipschitz loss functions, where an \$ε\$-fraction of the training data is adversarially corrupted. Our primary contribution lies in a novel modeling framework that integrates robustness against training data contamination with robustness against distributional shifts, alongside an efficient algorithm inspired by robust statistics to solve the resulting optimization problem. We prove that our method achieves an estimation error of \$O({\textbackslash}sqrtε)\$ for the true DRO objective value using only the contaminated data under the bounded covariance assumption. This work establishes the first rigorous guarantees, supported by efficient computation, for learning under the dual challenges of data contamination and distributional shifts.},
	language = {en},
	urldate = {2025-12-27},
	publisher = {arXiv},
	author = {Li, Shuyao and Diakonikolas, Ilias and Diakonikolas, Jelena},
	month = nov,
	year = {2025},
	note = {arXiv:2507.10718 [cs]},
	keywords = {Computer Science - Machine Learning, Mathematics - Optimization and Control, Computer Science - Data Structures and Algorithms},
	file = {PDF:/home/velocitatem/Zotero/storage/H6AXDTLX/Li et al. - 2025 - Distributionally Robust Optimization with Adversarial Data Contamination.pdf:application/pdf},
}

@misc{karten_llm_2025,
	title = {{LLM} {Economist}: {Large} {Population} {Models} and {Mechanism} {Design} in {Multi}-{Agent} {Generative} {Simulacra}},
	shorttitle = {{LLM} {Economist}},
	url = {http://arxiv.org/abs/2507.15815},
	doi = {10.48550/arXiv.2507.15815},
	abstract = {We present the LLM Economist, a novel framework that uses agent-based modeling to design and assess economic policies in strategic environments with hierarchical decision-making. At the lower level, bounded rational worker agents—instantiated as persona-conditioned prompts sampled from U.S. Census-calibrated income and demographic statistics—choose labor supply to maximize text-based utility functions learned in-context. At the upper level, a planner agent employs in-context reinforcement learning to propose piecewise-linear marginal tax schedules anchored to the current U.S. federal brackets. This construction endows economic simulacra with three capabilities requisite for credible fiscal experimentation: (i) optimization of heterogeneous utilities, (ii) principled generation of large, demographically realistic agent populations, and (iii) mechanism design—the ultimate nudging problem—expressed entirely in natural language. Experiments with populations of up to one hundred interacting agents show that the planner converges near Stackelberg equilibria that improve aggregate social welfare relative to Saez solutions, while a periodic, persona-level voting procedure furthers these gains under decentralized governance. These results demonstrate that large language model-based agents can jointly model, simulate, and govern complex economic systems, providing a tractable test bed for policy evaluation at the societal scale to help build better civilizations.},
	language = {en},
	urldate = {2025-12-27},
	publisher = {arXiv},
	author = {Karten, Seth and Li, Wenzhe and Ding, Zihan and Kleiner, Samuel and Bai, Yu and Jin, Chi},
	month = jul,
	year = {2025},
	note = {arXiv:2507.15815 [cs]},
	keywords = {Computer Science - Machine Learning, Computer Science - Multiagent Systems},
	file = {PDF:/home/velocitatem/Zotero/storage/U7A5Q78V/Karten et al. - 2025 - LLM Economist Large Population Models and Mechanism Design in Multi-Agent Generative Simulacra.pdf:application/pdf},
}

@techreport{mullapudi_reinforcement_2025,
	title = {A {Reinforcement} {Learning} {Approach} to {Dynamic} {Pricing}},
	abstract = {Dynamic pricing represents a critical strategic challenge in modern e-commerce, where firms must navigate fluctuating demand, inventory constraints, and aggressive competitor actions. Traditional static and heuristic-based pricing models often fail to capture the complex, non-linear dynamics of competitive digital markets, leading to suboptimal profitability. This paper proposes a model-free reinforcement learning (RL) framework to address this challenge. Specifically, we design, implement, and evaluate a Q-learning agent capable of learning an optimal, state-dependent pricing policy. The agent is trained and evaluated within a simulated market environment constructed from the publicly available "Retail Price Optimization" dataset from Kaggle, which provides a rich feature set including historical sales, product characteristics, seasonality, and, crucially, competitor pricing data. The problem is formulated as a Markov Decision Process (MDP), where the agent's state incorporates its price position relative to competitors, competitor price trends, and seasonal factors. The agent's performance is benchmarked against three baseline strategies: static pricing, a reactive "follow-the-leader" heuristic, and random pricing. The results demonstrate that the Q-learning agent achieves a substantial increase in total cumulative profit over the evaluation period, outperforming all baselines by learning a nuanced policy that strategically balances price adjustments in response to market conditions. This work provides a practical and reproducible blueprint for applying reinforcement learning to optimize pricing decisions in a simulated yet realistic competitive retail environment, highlighting the potential of RL to automate complex strategic decision-making.},
	author = {Mullapudi, Pavan},
	year = {2025},
	note = {Publication Title: International Journal on Science and Technology (IJSAT) IJSAT25049558
Volume: 16
Issue: 4},
	keywords = {Index Terms: Dynamic Pricing, Markov Decision Process, Price Optimization, Q-Learning, Reinforcement Learning, Retail Analytics},
	file = {PDF:/home/velocitatem/Zotero/storage/G95TBLF7/9558.pdf:application/pdf},
}

@techreport{roughgarden_cs364a_2013,
	title = {{CS364A}: {Algorithmic} {Game} {Theory} {Lecture} \#5: {Revenue}-{Maximizing} {Auctions} *},
	author = {Roughgarden, Tim},
	year = {2013},
	file = {PDF:/home/velocitatem/Zotero/storage/C39VM7N9/l5.pdf:application/pdf},
}

@techreport{kuhn_distributionally_2025,
	title = {Distributionally {Robust} {Optimization}},
	abstract = {Distributionally robust optimization (DRO) studies decision problems under uncertainty where the probability distribution governing the uncertain problem parameters is itself uncertain. A key component of any DRO model is its ambiguity set, that is, a family of probability distributions consistent with any available structural or statistical information. DRO seeks decisions that perform best under the worst distribution in the ambiguity set. This worst case criterion is supported by findings in psychology and neuroscience, which indicate that many decision-makers have a low tolerance for distributional ambiguity. DRO is rooted in statistics, operations research and control theory, and recent research has uncovered its deep connections to regularization techniques and adversarial training in machine learning. This survey presents the key findings of the field in a unified and self-contained manner.},
	author = {Kuhn, Daniel and Shafiee, Soroosh and Wiesemann, Wolfram},
	year = {2025},
	note = {arXiv: 2411.02549v3},
	file = {PDF:/home/velocitatem/Zotero/storage/IXTTMD7G/full-text.pdf:application/pdf},
}

@article{parkes_economic_2015,
	title = {Economic reasoning and artificial intelligence},
	volume = {349},
	issn = {10959203},
	doi = {10.1126/science.aaa8403},
	abstract = {The field of artificial intelligence (AI) strives to build rational agents capable of perceiving the world around them and taking actions to advance specified goals. Put another way, AI researchers aim to construct a synthetic homo economicus, the mythical perfectly rational agent of neoclassical economics.We review progress toward creating this new species of machine, machina economicus, and discuss some challenges in designing AIs that can reason effectively in economic contexts. Supposing that AI succeeds in this quest, or at least comes close enough that it is useful to think about AIs in rationalistic terms, we ask how to design the rules of interaction in multi-agent systems that come to represent an economy of AIs.Theories of normative design from economics may prove more relevant for artificial agents than human agents, with AIs that better respect idealized assumptions of rationality than people, interacting through novel rules and incentive systems quite distinct from those tailored for people.},
	number = {6245},
	journal = {Science},
	author = {Parkes, David C. and Wellman, Michael P.},
	month = jul,
	year = {2015},
	pmid = {26185245},
	note = {Publisher: American Association for the Advancement of Science},
	pages = {267--272},
	file = {PDF:/home/velocitatem/Zotero/storage/27KLNFRU/_aiEcon.pdf:application/pdf},
}

@article{yokoo_effect_2004,
	title = {The effect of false-name bids in combinatorial auctions: {New} fraud in internet auctions},
	volume = {46},
	issn = {08998256},
	doi = {10.1016/S0899-8256(03)00045-9},
	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.},
	number = {1},
	journal = {Games and Economic Behavior},
	author = {Yokoo, Makoto and Sakurai, Yuko and Matsubara, Shigeo},
	year = {2004},
	note = {Publisher: Academic Press Inc.},
	keywords = {Auction, Mechanism design, Strategy-proof},
	pages = {174--188},
	file = {PDF:/home/velocitatem/Zotero/storage/LUVQV6WT/Yokoo04.pdf:application/pdf},
}

@inproceedings{feldman_free-riding_2004,
	title = {Free-riding and whitewashing in peer-to-peer systems},
	isbn = {1-58113-942-X},
	doi = {10.1145/1016527.1016539},
	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.},
	booktitle = {Proceedings of the {ACM} {SIGCOMM} 2004 {Workshops}},
	publisher = {Association for Computing Machinery},
	author = {Feldman, Michal and Papadimitriou, Christos and Chuang, John and Stoica, Ion},
	year = {2004},
	keywords = {Cheap pseudonyms, Cooperation, Equilibrium, Exclusion, Free-riding, Identity cost, Incentives, Peer-to-peer, Whitewashing},
	pages = {228--235},
	file = {PDF:/home/velocitatem/Zotero/storage/K32WH6SB/1016527.1016539.pdf:application/pdf},
}

@article{calvano_artificial_2018,
	title = {Artificial {Intelligence}, {Algorithmic} {Pricing} and {Collusion}},
	url = {https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3304991},
	doi = {10.2139/ssrn.3304991},
	journal = {SSRN Electronic Journal},
	author = {Calvano, Emilio and Calzolari, Giacomo and Denicolo, Vincenzo and Pastorello, Sergio},
	year = {2018},
	file = {PDF:/home/velocitatem/Zotero/storage/WYTSSZBR/ssrn-3304991.pdf:application/pdf},
}

@techreport{varian_economic_1995,
	title = {Economic {Mechanism} {Design} for {Computerized} {Agents}},
	abstract = {The eeld of economic mechanism design has been an active area of research in economics for at least 20 years. This eld uses the tools of economics and game theory to design {\textbackslash}rules of interaction" for economic transactions that will, in principle , yield some desired outcome. In this paper I provide an overview of this subject for an audience interested in applications to electronic commerce and discuss some special problems that arise in this context.},
	author = {Varian, Hal R},
	year = {1995},
	file = {PDF:/home/velocitatem/Zotero/storage/S8635QX6/varian95a.pdf:application/pdf},
}

@book{russell_artificial_2021,
	title = {Artificial {Intelligence} {A} {Modern} {Approach} {Fourth} {Edition} {Global} {Edition}},
	isbn = {978-1-292-40117-1},
	author = {Russell, Stuart and Norvig, Peter},
	year = {2021},
	file = {PDF:/home/velocitatem/Zotero/storage/6B8W8S27/efdd4d1d4c2087fe1cbe03d9ced67f34.pdf:application/pdf},
}

@techreport{wellman_price_2004,
	title = {Price {Prediction} in a {Trading} {Agent} {Competition} {Yevgeniy} {Vorobeychik}},
	abstract = {The 2002 Trading Agent Competition (TAC) presented a challenging market game in the domain of travel shopping. One of the pivotal issues in this domain is uncertainty about hotel prices, which have a significant influence on the relative cost of alternative trip schedules. Thus, virtually all participants employ some method for predicting hotel prices. We survey approaches employed in the tournament, finding that agents apply an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on data provided by entrants on their agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. The results show that taking into account game-specific information about flight prices is a major distinguishing factor. Machine learning methods effectively induce the relationship between flight and hotel prices from game data, and a purely analytical approach based on competitive equilibrium analysis achieves equal accuracy with no historical data. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.},
	author = {Wellman, Michael P and Reeves, Daniel M and Lochner, Kevin M and Edu, Yvorobey@umich},
	year = {2004},
	note = {Publication Title: Journal of Artificial Intelligence Research
Volume: 21},
	pages = {19--36},
	file = {PDF:/home/velocitatem/Zotero/storage/N9JNXFJW/live-1333-2265-jair.pdf:application/pdf},
}

@techreport{shoham_multiagent_2009,
	title = {Multiagent {Systems}: {Algorithmic}, {Game}-{Theoretic}, and {Logical} {Foundations}},
	url = {http://www.masfoundations.org.},
	author = {Shoham, Yoav and Leyton-Brown, Kevin},
	year = {2009},
	keywords = {algorithms, auctions, communication, competition, cooperation, distributed problem solving, game theory, learning, logic, mechanism design, social choice},
	file = {PDF:/home/velocitatem/Zotero/storage/QZVYS7V9/shoham09a.pdf:application/pdf},
}

@article{xia_evaluation-driven_2025,
	title = {Evaluation-{Driven} {Development} and {Operations} of {LLM} {Agents}: {A} {Process} {Model} and {Reference} {Architecture}},
	url = {http://arxiv.org/abs/2411.13768},
	abstract = {Large Language Models (LLMs) have enabled the emergence of LLM agents, systems capable of pursuing under-specified goals and adapting after deployment. Evaluating such agents is challenging because their behavior is open ended, probabilistic, and shaped by system-level interactions over time. Traditional evaluation methods, built around fixed benchmarks and static test suites, fail to capture emergent behaviors or support continuous adaptation across the lifecycle. To ground a more systematic approach, we conduct a multivocal literature review (MLR) synthesizing academic and industrial evaluation practices. The findings directly inform two empirically derived artifacts: a process model and a reference architecture that embed evaluation as a continuous, governing function rather than a terminal checkpoint. Together they constitute the evaluation-driven development and operations (EDDOps) approach, which unifies offline (development-time) and online (runtime) evaluation within a closed feedback loop. By making evaluation evidence drive both runtime adaptation and governed redevelopment, EDDOps supports safer, more traceable evolution of LLM agents aligned with changing objectives, user needs, and governance constraints.},
	author = {Xia, Boming and Lu, Qinghua and Zhu, Liming and Xing, Zhenchang and Zhao, Dehai and Zhang, Hao},
	month = nov,
	year = {2025},
	note = {arXiv: 2411.13768},
	file = {PDF:/home/velocitatem/Zotero/storage/H8IS64AW/2411.13768v2.pdf:application/pdf},
}

@techreport{xie_osworld_2024,
	title = {{OSWORLD}: {Benchmarking} {Multimodal} {Agents} for {Open}-{Ended} {Tasks} in {Real} {Computer} {Environments}},
	url = {https://os-world.github.io},
	abstract = {Autonomous agents that accomplish complex computer tasks with minimal human interventions have the potential to transform human-computer interaction, significantly enhancing accessibility and productivity. However, existing benchmarks either lack an interactive environment or are limited to environments specific to certain applications or domains, failing to reflect the diverse and complex nature of real-world computer use, thereby limiting the scope of tasks and agent scalability. To address this issue, we introduce OSWORLD, the first-of-its-kind scalable, real computer environment for multimodal agents, supporting task setup, execution-based evaluation, and interactive learning across various operating systems such as Ubuntu, Windows, and macOS. OSWORLD can serve as a unified, integrated computer environment for assessing open-ended computer tasks that involve arbitrary applications. Building upon OSWORLD, we create a benchmark of 369 computer tasks involving real web and desktop apps in open domains, OS file I/O, and workflows spanning multiple applications. Each task example is derived from real-world computer use cases and includes a detailed initial state setup configuration and a custom execution-based evaluation script for reliable, reproducible evaluation. Extensive evaluation of state-of-the-art LLM/VLM-based agents on OSWORLD reveals significant deficiencies in their ability to serve as computer assistants. While humans can accomplish over 72.36\% of the tasks, the best model achieves only 12.24\% success, primarily struggling with GUI grounding and operational knowledge. Comprehensive analysis using OSWORLD provides valuable insights for developing multimodal generalist agents that were not possible with previous benchmarks. Our code, environment, baseline models, and data are publicly available at https://os-world.github.io.},
	author = {Xie, Tianbao and Zhang, Danyang and Chen, Jixuan and Li, Xiaochuan and Zhao, Siheng and Cao, Ruisheng and Jing Hua, Toh and Cheng, Zhoujun and Shin, Dongchan and Lei, Fangyu and Liu, Yitao and Xu, Yiheng and Zhou, Shuyan and Savarese, Silvio and Xiong, Caiming and Zhong, Victor and Yu, Tao},
	month = may,
	year = {2024},
	note = {arXiv: 2404.07972v2},
	file = {PDF:/home/velocitatem/Zotero/storage/LLRKXIC7/full-text.pdf:application/pdf},
}

@techreport{imperva_rapid_2025,
	title = {The {Rapid} {Rise} of {Bots} and the {Unseen} {Risk} for {Business} \#{2025BADBOTREPORT}},
	author = {{Imperva}},
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/AWR9IQRD/2025-Bad-Bot-Report.pdf:application/pdf},
}

@article{perez-ricardo_exploring_2025,
	title = {Exploring booking intentions through price elasticity of demand in tourism accommodations using large-scale data analytics},
	volume = {31},
	issn = {24448834},
	doi = {10.1016/j.iedeen.2025.100271},
	abstract = {The study aims to explore tourists' booking intentions by analyzing the price elasticity of demand in tourist accommodations. This analysis should reveal how changes in price affect booking behavior across different customer segments, using online booking records. A dataset was compiled from 106 hotels in Malaga, Spain, comprising 27,910 online bookings sourced exclusively from hotel websites. To understand the price elasticity of demand, a simple log-log regression was applied, segmenting the data based on key revenue-related variables. Subsequently, a cluster segmentation was performed using the Elbow method and K-means algorithm to identify distinct market segments. The findings highlighted that Family Travelers and Short Stay Travelers segments exhibited elastic demand, indicating higher sensitivity to price fluctuations. In contrast, Early Bookers and Mid-Season Long Stayers demonstrated inelastic demand, with lower responsiveness to changes in tourist accommodation prices. The number of variables analyzed in this study, along with the cluster analysis, represent a novelty and contribute to the existing literature on market segmentation and price elasticity of demand. This integration enriches both fields of research, offering mutual benefits and deeper insights that enhance the understanding of booking intention and pricing strategies.},
	number = {1},
	urldate = {2025-11-28},
	journal = {European Research on Management and Business Economics},
	author = {Pérez-Ricardo, Elizabeth del Carmen and García-Mestanza, Josefa},
	month = jan,
	year = {2025},
	note = {Publisher: European Academy of Management and Business Economics},
	keywords = {Booking intention, Price elasticity, Tourist segmentation},
	file = {PDF:/home/velocitatem/Zotero/storage/QNXZJLRM/S2444883425000038.pdf:application/pdf},
}

@misc{ghaffary_amazon_2025,
	title = {Amazon {Sues} to {Stop} {Perplexity} {From} {Using} {AI} {Tool} to {Buy} {Stuff}},
	url = {https://www.bloomberg.com/news/articles/2025-11-04/amazon-demands-perplexity-stop-ai-agent-from-making-purchases},
	author = {Ghaffary, Shirin and Day, Matt},
	month = nov,
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/IQL6FPWE/Amazon Sues to Stop Perplexity From Using AI Tool to Buy Stuff - Bloomberg.pdf:application/pdf},
}

@techreport{besbes_dynamic_2007,
	title = {Dynamic {Pricing} {Without} {Knowing} the {Demand} {Function}: {Risk} {Bounds} and {Near}-{Optimal} {Algorithms} *},
	abstract = {We consider a single product revenue management problem where, given an initial inventory, the objective is to dynamically adjust prices over a finite sales horizon to maximize expected revenues. Realized demand is observed over time, but the underlying functional relationship between price and mean demand rate that governs these observations (otherwise known as the demand function or demand curve), is not known. We consider two instances of this problem: i.) a setting where the demand function is assumed to belong to a known parametric family with unknown parameter values; and ii.) a setting where the demand function is assumed to belong to a broad class of functions that need not admit any parametric representation. In each case we develop policies that learn the demand function "on the fly," and optimize prices based on that. The performance of these algorithms is measured in terms of the regret: the revenue loss relative to the maximal revenues that can be extracted when the demand function is known prior to the start of the selling season. We derive lower bounds on the regret that hold for any admissible pricing policy, and then show that our proposed algorithms achieve a regret that is "close" to this lower bound. The magnitude of the regret can be interpreted as the economic value of prior knowledge on the demand function; manifested as the revenue loss due to model uncertainty.},
	author = {Besbes, Omar and Zeevi, Assaf},
	month = dec,
	year = {2007},
	note = {Publication Title: Operations Research},
	keywords = {learning, asymptotic analysis, estimation, exploration-exploitation, pricing, Revenue management, value of information},
	file = {PDF:/home/velocitatem/Zotero/storage/SBAIB4V2/Dp_wo_demand_risk_ob_az_posted.pdf:application/pdf},
}

@techreport{markntel_advisors_global_2025,
	address = {Noida, Uttar Pradesh, India},
	title = {Global {AI} {Agent} {Market} {Research} {Report}: {Forecast} (2026–2032)},
	url = {https://www.marknteladvisors.com/research-library/ai-agent-market.html},
	urldate = {2025-12-12},
	institution = {MarkNtel Advisors},
	author = {{MarkNtel Advisors}},
	year = {2025},
}

@article{amjad_censored_2017,
	title = {Censored {Demand} {Estimation} in {Retail}},
	volume = {1},
	url = {https://par.nsf.gov/servlets/purl/10066022},
	doi = {10.1145/3154489},
	abstract = {In this paper, the question of interest is estimating true demand of a product at a given store location and time period in the retail environment based on a single noisy and potentially censored observation. To address this question, we introduce a \%non-parametric framework to make inference from multiple time series. Somewhat surprisingly, we establish that the algorithm introduced for the purpose of "matrix completion" can be used to solve the relevant inference problem. Specifically, using the Universal Singular Value Thresholding (USVT) algorithm [7], we show that our estimator is consistent: the average mean squared error of the estimated average demand with respect to the true average demand goes to 0 as the number of store locations and time intervals increase to \${\textbackslash}infty\$. We establish naturally appealing properties of the resulting estimator both analytically as well as through a sequence of instructive simulations. Using a real dataset in retail (Walmart), we argue for the practical relevance of our approach.},
	number = {2},
	urldate = {2025-11-12},
	journal = {Proceedings of the ACM on Measurement and Analysis of Computing Systems},
	author = {Amjad, Muhammad J. and Shah, Devavrat},
	month = dec,
	year = {2017},
	note = {Publisher: Association for Computing Machinery (ACM)},
	pages = {1--28},
	file = {PDF:/home/velocitatem/Zotero/storage/5ZYADDT4/10066022.pdf:application/pdf},
}

@misc{ganie_uncertainty_2025,
	title = {Uncertainty in {Authorship}: {Why} {Perfect} {AI} {Detection} {Is} {Mathematically} {Impossible}},
	shorttitle = {Uncertainty in {Authorship}},
	url = {http://arxiv.org/abs/2509.11915},
	doi = {10.48550/arXiv.2509.11915},
	abstract = {As large language models (LLMs) become more advanced, it is increasingly difficult to distinguish between human-written and AI-generated text. This paper draws a conceptual parallel between quantum uncertainty and the limits of authorship detection in natural language. We argue that there is a fundamental trade-off: the more confidently one tries to identify whether a text was written by a human or an AI, the more one risks disrupting the text's natural flow and authenticity. This mirrors the tension between precision and disturbance found in quantum systems. We explore how current detection methods--such as stylometry, watermarking, and neural classifiers--face inherent limitations. Enhancing detection accuracy often leads to changes in the AI's output, making other features less reliable. In effect, the very act of trying to detect AI authorship introduces uncertainty elsewhere in the text. Our analysis shows that when AI-generated text closely mimics human writing, perfect detection becomes not just technologically difficult but theoretically impossible. We address counterarguments and discuss the broader implications for authorship, ethics, and policy. Ultimately, we suggest that the challenge of AI-text detection is not just a matter of better tools--it reflects a deeper, unavoidable tension in the nature of language itself.},
	language = {en},
	urldate = {2026-01-05},
	publisher = {arXiv},
	author = {Ganie, Aadil Gani},
	month = sep,
	year = {2025},
	note = {arXiv:2509.11915 [cs]},
	keywords = {Computer Science - Computation and Language},
	file = {PDF:/home/velocitatem/Zotero/storage/3Z2XK4QC/Ganie - 2025 - Uncertainty in Authorship Why Perfect AI Detection Is Mathematically Impossible.pdf:application/pdf},
}

@article{shi_distributionally_2024,
	title = {Distributionally {Robust} {Model}-{Based} {Oﬄine} {Reinforcement} {Learning} with {Near}-{Optimal} {Sample} {Complexity}},
	abstract = {This paper concerns the central issues of model robustness and sample eﬃciency in oﬄine reinforcement learning (RL), which aims to learn to perform decision making from history data without active exploration. Due to uncertainties and variabilities of the environment, it is critical to learn a robust policy—with as few samples as possible—that performs well even when the deployed environment deviates from the nominal one used to collect the history dataset. We consider a distributionally robust formulation of oﬄine RL, focusing on tabular robust Markov decision processes with an uncertainty set speciﬁed by the Kullback-Leibler divergence in both ﬁnite-horizon and inﬁnite-horizon settings. To combat with sample scarcity, a model-based algorithm that combines distributionally robust value iteration with the principle of pessimism in the face of uncertainty is proposed, by penalizing the robust value estimates with a carefully designed data-driven penalty term. Under a mild and tailored assumption of the history dataset that measures distribution shift without requiring full coverage of the state-action space, we establish the ﬁnite-sample complexity of the proposed algorithms. We further develop an informationtheoretic lower bound, which suggests that learning RMDPs is at least as hard as the standard MDPs when the uncertainty level is suﬃcient small, and corroborates the tightness of our upper bound up to polynomial factors of the (eﬀective) horizon length for a range of uncertainty levels. To the best our knowledge, this provides the ﬁrst provably near-optimal robust oﬄine RL algorithm that learns under model uncertainty and partial coverage.},
	language = {en},
	author = {Shi, Laixi and Chi, Yuejie},
	month = jun,
	year = {2024},
	file = {PDF:/home/velocitatem/Zotero/storage/K56G4EIP/Shi and Chi - Distributionally Robust Model-Based Oﬄine Reinforcement Learning with Near-Optimal Sample Complexity.pdf:application/pdf},
}

@article{dutting_mechanism_2025,
	title = {Mechanism {Design} for {Large} {Language} {Models} ({Extended} {Abstract})},
	abstract = {We investigate auction mechanisms for AIgenerated content, focusing on applications like ad creative generation. In our model, agents’ preferences over stochastically generated content are encoded as large language models (LLMs). We propose an auction format that operates on a tokenby-token basis, and allows LLM agents to inﬂuence content creation through single dimensional bids. We formulate two desirable incentive properties and prove their equivalence to a monotonicity condition on output aggregation. This equivalence enables a second-price rule design, even absent explicit agent valuation functions. Our design is supported by demonstrations on a publicly available LLM.},
	language = {en},
	author = {Dütting, Paul and Mirrokni, Vahab and Leme, Renato Paes and Xu, Haifeng and Zuo, Song},
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/2ABDEYDN/Dütting et al. - Mechanism Design for Large Language Models (Extended Abstract).pdf:application/pdf},
}

@misc{fcmi_machine_2025,
	title = {Machine {Speed} {Markets}: {AI} {Agent} {Market} {Strategy} \& {Growth}},
	shorttitle = {Machine {Speed} {Markets}},
	url = {https://www.360strategy.co.uk/post/machine-speed-markets-ai-agents},
	abstract = {Recent research by NBER economists suggests these AI agents in particular, could drive a "Coasean singularity," a point where transaction costs fall towards zero, radically reshaping how markets function. In essence, tasks like finding information, negotiating deals, and enforcing contracts which are traditionally costly frictions in commerce, may become nearly instantaneous and costless.},
	language = {en},
	urldate = {2026-01-20},
	journal = {360 Strategy},
	author = {FCMi, CMgr, Mark Evans MBA},
	month = nov,
	year = {2025},
	file = {Snapshot:/home/velocitatem/Zotero/storage/Z22P9JJH/machine-speed-markets-ai-agents.html:text/html},
}

@article{coase_nature_1937,
	title = {The {Nature} of the {Firm}},
	volume = {4},
	issn = {1468-0335},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1468-0335.1937.tb00002.x},
	doi = {10.1111/j.1468-0335.1937.tb00002.x},
	language = {en},
	number = {16},
	urldate = {2026-01-20},
	journal = {Economica},
	author = {Coase, R. H.},
	year = {1937},
	pages = {386--405},
	file = {Full Text PDF:/home/velocitatem/Zotero/storage/TABLLPEU/Coase - 1937 - The Nature of the Firm.pdf:application/pdf;Snapshot:/home/velocitatem/Zotero/storage/Q5RFW9LJ/j.1468-0335.1937.tb00002.html:text/html},
}

@misc{fish_algorithmic_2025,
	title = {Algorithmic {Collusion} by {Large} {Language} {Models}},
	url = {http://arxiv.org/abs/2404.00806},
	doi = {10.48550/arXiv.2404.00806},
	abstract = {The rise of algorithmic pricing raises concerns of algorithmic collusion. We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). We find that LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits in oligopoly settings and that variation in seemingly innocuous phrases in LLM instructions (“prompts”) may substantially influence the degree of supracompetitive pricing. Off-path analysis using novel techniques uncovers price-war concerns as contributing to these phenomena. Our results extend to auction settings. Our findings uncover unique challenges to any future regulation of LLM-based pricing agents, and AI-based pricing agents more broadly.},
	language = {en},
	urldate = {2026-01-20},
	publisher = {arXiv},
	author = {Fish, Sara and Gonczarowski, Yannai A. and Shorrer, Ran I.},
	month = sep,
	year = {2025},
	note = {arXiv:2404.00806 [econ]},
	keywords = {Computer Science - Computer Science and Game Theory, Computer Science - Artificial Intelligence, Economics - General Economics},
	file = {PDF:/home/velocitatem/Zotero/storage/QHWVISCZ/Fish et al. - 2025 - Algorithmic Collusion by Large Language Models.pdf:application/pdf},
}

@misc{hardt_strategic_2015,
	title = {Strategic {Classification}},
	url = {http://arxiv.org/abs/1506.06980},
	doi = {10.48550/arXiv.1506.06980},
	abstract = {Machine learning relies on the assumption that unseen test instances of a classiﬁcation problem follow the same distribution as observed training data. However, this principle can break down when machine learning is used to make important decisions about the welfare (employment, education, health) of strategic individuals. Knowing information about the classiﬁer, such individuals may manipulate their attributes in order to obtain a better classiﬁcation outcome. As a result of this behavior—often referred to as gaming—the performance of the classiﬁer may deteriorate sharply. Indeed, gaming is a well-known obstacle for using machine learning methods in practice; in ﬁnancial policy-making, the problem is widely known as Goodhart’s law. In this paper, we formalize the problem, and pursue algorithms for learning classiﬁers that are robust to gaming.},
	language = {en},
	urldate = {2026-01-20},
	publisher = {arXiv},
	author = {Hardt, Moritz and Megiddo, Nimrod and Papadimitriou, Christos and Wootters, Mary},
	month = nov,
	year = {2015},
	note = {arXiv:1506.06980 [cs]},
	keywords = {Computer Science - Machine Learning},
	file = {PDF:/home/velocitatem/Zotero/storage/HNCDYGWS/Hardt et al. - 2015 - Strategic Classification.pdf:application/pdf},
}

@misc{liu_contextual_2024,
	title = {Contextual {Dynamic} {Pricing} with {Strategic} {Buyers}},
	url = {http://arxiv.org/abs/2307.04055},
	doi = {10.48550/arXiv.2307.04055},
	abstract = {Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this paper, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the buyer's true feature, but a manipulated feature according to buyers' strategic behavior. In addition, the seller does not observe the buyers' valuation of the product, but only a binary response indicating whether a sale happens or not. Recognizing these challenges, we propose a strategic dynamic pricing policy that incorporates the buyers' strategic behavior into the online learning to maximize the seller's cumulative revenue. We first prove that existing non-strategic pricing policies that neglect the buyers' strategic behavior result in a linear \$Ω(T)\$ regret with \$T\$ the total time horizon, indicating that these policies are not better than a random pricing policy. We then establish that our proposed policy achieves a sublinear regret upper bound of \$O({\textbackslash}sqrt\{T\})\$. Importantly, our policy is not a mere amalgamation of existing dynamic pricing policies and strategic behavior handling algorithms. Our policy can also accommodate the scenario when the marginal cost of manipulation is unknown in advance. To account for it, we simultaneously estimate the valuation parameter and the cost parameter in the online pricing policy, which is shown to also achieve an \$O({\textbackslash}sqrt\{T\})\$ regret bound. Extensive experiments support our theoretical developments and demonstrate the superior performance of our policy compared to other pricing policies that are unaware of the strategic behaviors.},
	language = {en},
	urldate = {2026-01-20},
	publisher = {arXiv},
	author = {Liu, Pangpang and Yang, Zhuoran and Wang, Zhaoran and Sun, Will Wei},
	month = jun,
	year = {2024},
	note = {arXiv:2307.04055 [stat]},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning, Computer Science - Computer Science and Game Theory, Computer Science - Artificial Intelligence},
	file = {PDF:/home/velocitatem/Zotero/storage/MVJNULK3/Liu et al. - 2024 - Contextual Dynamic Pricing with Strategic Buyers.pdf:application/pdf},
}

@techreport{dhir_http_2025,
	type = {Internet {Draft}},
	title = {{HTTP} {Agent} {Profile} ({HAP}): {Authenticated} and {Monetized} {Agent} {Traffic} on the {Web}},
	shorttitle = {{HTTP} {Agent} {Profile} ({HAP})},
	url = {https://datatracker.ietf.org/doc/draft-dhir-http-agent-profile},
	abstract = {Autonomous agents such as LLM-powered crawlers, browser-integrated assistants, and task-oriented bots are rapidly becoming first-class HTTP clients on the Web. Today’s infrastructure largely assumes a human behind a browser and monetizes content through advertising and coarse subscriptions. Automated agents consume content at scale without rendering pages or viewing ads, exacerbating bot-mitigation arms races and economic misalignment between content providers and AI systems. This document describes an HTTP Agent Profile (HAP) that enables: (1) cryptographic authentication of agent traffic using HTTP Message Signatures; (2) clear separation between human and agent traffic using privacy-preserving human tokens; and (3) protocol-level value exchange for agents via HTTP status code 402 ("Payment Required") and pluggable micropayment mechanisms. The profile reuses existing HTTP features and is designed for incremental deployment via reverse proxies, CDNs, and agent libraries.},
	number = {draft-dhir-http-agent-profile-00},
	urldate = {2026-01-20},
	institution = {Internet Engineering Task Force},
	author = {Dhir, Sanat},
	month = nov,
	year = {2025},
	note = {Num Pages: 13},
}

@misc{noauthor_amazoncom_2026,
	title = {Amazon.com {Services} {LLC} v. {Perplexity} {AI}, {Inc}},
	language = {en},
	month = jan,
	year = {2026},
	note = {No. 3:25-cv-09514-MMC},
	file = {PDF:/home/velocitatem/Zotero/storage/4JWZSTXJ/Posner - UNITED STATES DISTRICT COURT NORTHERN DISTRICT OF CALIFORNIA SAN FRANCISCO DIVISION.pdf:application/pdf},
}

@article{wright_2026_2025,
	title = {2026 {Artificial} {Intelligence} {Outlook}: {The} {Great} {Competition} {Wars} {Have} {Begun}},
	language = {en},
	journal = {Pitchbook},
	author = {Wright, Brian and Javaheri, Ali and Bellomo, Eric and Hernandez, Derek and Yang, Rudy and MacDonagh, John and DeGagne, Aaron and Frederick, Alex and Geurkink, Jonathan and Zabelin, Dimitri and Ulan, James},
	month = dec,
	year = {2025},
	file = {PDF:/home/velocitatem/Zotero/storage/AIY5K3TX/Wright et al. - 2025 - Institutional Research Group.pdf:application/pdf},
}

@misc{rachitsky_marc_2026,
	title = {Marc {Andreessen}: {The} real {AI} boom hasn’t even started yet},
	shorttitle = {Marc {Andreessen}},
	url = {https://www.lennysnewsletter.com/p/marc-andreessen-the-real-ai-boom},
	abstract = {On raising kids, why job loss fears are overblown, the future of PM/eng/design careers, and the macro force you should pay attention to},
	language = {en},
	urldate = {2026-02-01},
	author = {Rachitsky, Lenny},
	month = feb,
	year = {2026},
	file = {Snapshot:/home/velocitatem/Zotero/storage/DGW8PHMV/marc-andreessen-the-real-ai-boom.html:text/html},
}

@misc{noauthor_tpu_2025,
	title = {{TPU} v6e},
	url = {https://cloud.google.com/tpu/docs/v6e},
	language = {es-419-x-mtfrom-en},
	urldate = {2026-02-17},
	journal = {Google Cloud Documentation},
	month = dec,
	year = {2025},
	file = {Snapshot:/home/velocitatem/Zotero/storage/RNMB32KD/v6e.html:text/html},
}

@misc{noauthor_tpu_2025-1,
	title = {{TPU} v5e {\textbar} {Google} {Cloud} {Documentation}},
	url = {https://cloud.google.com/tpu/docs/v5e},
	language = {es-419-x-mtfrom-en},
	urldate = {2026-02-17},
	month = dec,
	year = {2025},
	file = {Snapshot:/home/velocitatem/Zotero/storage/BLLG9NZC/v5e.html:text/html},
}

@misc{noauthor_tpu_2026,
	title = {{TPU} v4 {\textbar} {Google} {Cloud} {Documentation}},
	url = {https://cloud.google.com/tpu/docs/v4},
	language = {es-419-x-mtfrom-en},
	urldate = {2026-02-17},
	month = feb,
	year = {2026},
	file = {Snapshot:/home/velocitatem/Zotero/storage/N724QGF6/v4.html:text/html},
}
