Auction Theory and Applications

Últimos artículos

• Inference Auctions

  Keegan Harris, Siddharth Prasad, Asher Trockman, Nika Haghtalab, Michael I. Jordan · 1 de octubre de 2026

  When inference demand exceeds available compute capacity, model providers must decide which requests should be served first. Users have different tolerances for delay from an LLM API, but current priority pricing schemes compress these differences into coarse fixed-price service tiers. We design an …

• Algorithmic Recourse Under Competition

  Shahin Jabbari · 1 de octubre de 2026

  Algorithmic recourse provides individuals who have received undesirable outcomes from machine learning models with suggestions for minimum-cost improvements to achieve the desired outcome. A central assumption when computing recourse is that the decision rule remains fixed throughout the recourse im…

• Risk-Aware Adaptive Evaluation: Finding High-Impact Failures Under Limited Budgets

  Priyanath Maji, Spandan Ghose Chowdhury · 1 de octubre de 2026

  Evaluating interactive agents is expensive. Agent behavior is stochastic, so reliability must be measured over repeated trials, but failures are rare and differ widely in how much they matter. Standard benchmarks spend this budget uniformly: a read-only lookup is sampled as often as an irreversible …

• Risk-Controlled Selective LLM Answering by Pricing Label-Free Checks

  Dongyub Jude Lee, Jungseob Lee, Chanjun Park, Hyeonseok Moon, Heuiseok Lim · 30 de septiembre de 2026

  Serving an answer from a large language model requires deciding when to abstain, yet a verifier's ranking accuracy alone does not determine the error rate among served answers. We introduce PriceCheck, which builds a compact family of decision rules from label-free checks such as re-solving a proble…

• Engineering Simplicity: Simple Mechanism Interfaces Steer LLM Agents

  Kehang Zhu, Anand Shah, David Parkes · 30 de septiembre de 2026

  Can interaction formats and textual scaffolds help large language model (LLM) agents make better decisions, and do better decisions come with better explanations? We study these questions in auctions and matching, multi-agent environments with explicit rules and known optimal strategies. These setti…

• Learnable Randomization as Commitment Against Adaptive Optimizers

  Zihan Deng, Chuanzhi Xu, Xiaozhen Zhong, Haoyang Li, Junjie Huang · 29 de septiembre de 2026

  A pricing page can walk the posted price up to the last amount a buyer still accepts, a recommender can hold back a better item for a barely acceptable promoted one, and a classifier can shift its boundary once applicants change their features. The system predicts the response and then picks the men…

• FARE: Deep Reinforcement Learning For Fair Exposure Constrained Uncertainty Aware Financial Content Personalization

  Arundeep Chinta, Lucas Vinh Tran, Jay Katukuri · 29 de septiembre de 2026

  Content personalization systems in financial services must ensure fair exposure across diverse offerings-a requirement driven by contractual obligations and the need to prevent "rich-get-richer" dynamics where content with high click-through rate (CTR) dominates while other relevant products receive…

• Learning to Sell: Reinforcement Learning for Strategic Large Language Model Agents in Multi-Product Markets

  Shuze Daniel Liu, Claire Chen, Jiuqi Wang, Thorsten Joachims · 29 de septiembre de 2026

  Autonomous large language model (LLM) agents operating in multi-product markets must make sequential decisions under information asymmetry and resource constraints. We develop a machine learning approach for training such agents to act effectively as sellers in a multi-item bargaining environment, w…

• CAVEAT: Towards Robust Computer-Use Agents in Incentive-Misaligned Environments

  Yuxuan Li, Will Epperson, Wesley Deng, Zezhou Huang · 24 de septiembre de 2026

  Computer-use agents (CUAs) increasingly act on behalf of users online. What happens when the environments they operate in have incentives that do not align with the user's? In online marketplaces, for example, platforms may favor some products over others, potentially steering agents away from the u…

• The Cost of Conservation: Coordination-Memory Laws for Exact-Support Generation

  Zhen Zhang, Amr Alanwar · 23 de septiembre de 2026

  Many AI systems make decisions locally, even when every realized output must obey an additive conservation law, such as selecting exactly a fixed number of items. This constraint can be statistically invisible: small subsets of a balanced fixed-budget output look increasingly independent, yet commun…

• Strategic Classification Has a Missing Lever: Audit Risk

  Raman Ebrahimi, Massimo Franceschetti · 22 de septiembre de 2026

  Strategic classification studies how a decision maker should choose a classifier when the agents being classified can adjust their features in response to it. In existing models, the classifier is the only instrument available to the decision maker, and therefore a feature that is predictive but eas…

• Multiplicative Optimism for Constant Regret in Games

  Ashkan Soleymani, Georgios Piliouras · 21 de septiembre de 2026

  We introduce Multiplicatively Optimistic Regret Matching (MORM), an uncoupled learning rule for finite general-sum games. Under simultaneous full-information self-play, every player achieves external regret $O(\sqrt n\log d)$ uniformly over all horizons, using only one-step optimism. The analysis co…

• OneBid: A Unified Auto-Bidding Foundation Model for Diverse oCPX Advertising Scenarios

  Yewen Li, Peng Jiang, Yitian Li, Pengfei Lv, Xialong Liu, Peng Jiang, Qingpeng Cai · 21 de septiembre de 2026

  Auto-bidding is central to computational advertising, where strategies must maximize advertisers' conversion value under economic constraints. It has evolved from rule-based controllers to reinforcement learning and generative methods such as Decision Transformer (DT). Yet these methods increasingly…

• Learning Principal-Agent Contracts for Equitable Smallholder Carbon Farming under Moral Hazard and Adverse Selection

  Rishi Bharadwaj, Yadati Narahari · 18 de septiembre de 2026

  Agricultural soils are a major untapped carbon sink. Carbon farming is emerging as a promising practice for tapping this potential. Smallholder farmers, who dominate agriculture across South Asia and sub-Saharan Africa, are key to scaling climate mitigation via carbon farming. It is ironic that real…

• Minimax-Optimal Online Contract Design with Unrestricted Bounded Contracts

  Rui Ai, David Simchi-Levi, Han Zhong · 18 de septiembre de 2026

  We study repeated contract design when a principal observes outcomes but not the actions that generate them. The principal may use any bounded outcome-contingent payment vector, and the agent's best response can make expected profit discontinuous in those payments. For every fixed number $m\ge2$ of …

• UniPolicy: Unified Objective-Specific Policies for Generative Search Advertising

  Kun Yao, Yuhang Zhou, Yichi Zhang, Zeliang Tong, Shengri Xue, Haitao Wang, Siyu Lu, Qianlong Xie, Xingxing Wang · 18 de septiembre de 2026

  Search advertising connects user intent with commercial content and plays a critical role in platform monetization. Recent systems typically align pretrained generative models with a single business reward, such as eCPM, or use naive reward fusion for preliminary multi-objective alignment. However, …

• Welfare-Opaque Income: Taxation under AI-Agent Delegation

  Yukun Zhang, Kemu Xu, Yishen Chen · 18 de septiembre de 2026

  We study income taxation when an AI agent implements economically relevant choices through a rule hidden from the government. Alongside unobserved productive ability, this hidden preference-to-execution mapping creates \emph{double unobservability}: the same observable tax-base response can carry di…

• Faithful yet Collusive: Why Chain-of-Thought Monitoring Cannot Detect Collusion in LLM Pricing Agents under Oligopolistic Competition

  Dohun Lee, Hyunwoo Park · 17 de septiembre de 2026

  Large language models (LLM) deployed as autonomous pricing agents may sustain supracompetitive prices through tacit coordination. We develop a causal graph divergence framework that separately measures structural faithfulness and intent faithfulness of LLM pricing agents in Bertrand competition. Acr…

• EF1-Constrained Nash Social Welfare with Identical Additive Valuations: Complexity, Guarantees, and Experiments

  Zih-Sian Yang, Yi-Hao Chen, Yu-Te Kuan, Cheng-Jui Wu, Chuang-Chieh Lin, Po-An Chen · 4 de septiembre de 2026

  We study the allocation of indivisible goods among agents with identical additive valuations, focusing on envy-freeness up to one good (EF1) and Nash social welfare (NSW). Since every maximum-NSW allocation is EF1 under additive valuations, the associated threshold problem inherits the known strong …

• Data Market Design through Deep Learning

  Sai Srivatsa Ravindranath, Yanchen Jiang, David C. Parkes · 4 de septiembre de 2026

  The data market design problem is a problem in economic theory to find a set of signaling schemes (statistical experiments) to maximize expected revenue to the information seller, where each experiment reveals some of the information known to a seller and has a corresponding price [Bergemann et al.,…

• Competitive Market Behavior of LLMs

  Pawel Struski, Jakub Swistak, Inez Okulska, Przemyslaw Biecek · 3 de septiembre de 2026

  Large language models (LLMs) are increasingly deployed as economic agents, yet there is little evidence whether LLM agents are suited for participating in market mechanisms designed for humans, and whether these mechanisms deliver desired outcomes when faced with LLM agents. We address this question…

• Keep Everyone Happy: Online Fair Division of Numerous Items with Few Copies

  Arun Verma, Indrajit Saha, Makoto Yokoo, Bryan Kian Hsiang Low · 2 de septiembre de 2026

  This paper considers a novel variant of the online fair division problem involving multiple agents in which a learner sequentially observes an indivisible item that must be irrevocably allocated to one of the agents to achieve a desired balance between fairness and efficiency. Existing algorithms as…

• Mechanism Design for Alignment and Control

  Dirk Bergemann, Andrew Koh, Stephen Morris · 2 de septiembre de 2026

  We develop a framework for mechanism design with AI agents whose alignment (preferences) and capabilities (feasible actions and information) are unknown. We want such agents to act on our behalf so mechanisms must incentivize both honesty and obedience. A one-sided imitation structure---capabilities…

• When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation

  Peiying Zhu, Sidi Chang · 2 de septiembre de 2026

  Interactive simulations increasingly evaluate policies in markets populated by language-model agents. Their outputs can look economic---prices, profits, consumer surplus, and welfare---without instantiating the behavior named in the claim. We audit this risk in a multi-turn buyer--seller testbed for…

• Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning

  Zilin Zhao, Han Yang, Tianpei Yang, Fangsheng Huang, Yanfei Cui, Kan Peng, Yi Li, Yiming Zong, Hao Zhang, Yinsong Xue · 31 de agosto de 2026

  Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and complete ads. It must balance the incentive promised in advance against the revenue realized afterward: insufficient inc…