arXiv AI
When Disclosure Is Compulsory: Evaluating Ride-Hailing Rebalancing Policies with Self-Interested LLM Driver Agents
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arXiv AI
Sep 25
A General Framework for Budgeted Threshold Incentives on Request
The paper introduces a request-driven framework for designing budgeted threshold incentives on on-demand delivery platforms. It decomposes the process into four stages—conditional prediction, population reduction, trajectory integration, and budget allocation—using seven interchangeable modules that share conditional trajectory laws. The framework includes a response-correction step that reweights abundant no-offer data to match short pilot moments, and the authors prove that the end-to-end value loss is bounded by the sum of stage errors, with empirical results showing significant speedups and reduced regret compared to traditional trials.
arXiv AI
Sep 2
When Guardrails Look Effective: Construct Validity Failures in LLM Agent Commerce Evaluation
The paper investigates how marketplace guardrails affect welfare in language‑model agent simulations of hotel transactions. It finds that initial reports of large welfare gains disappear when controlling for offer schemas and buyer choice, and that guardrails mainly redistribute rather than increase welfare unless sellers are explicitly forced to produce inefficient bundles. The authors propose a construct‑validity framework to flag invalid or inconclusive policy claims before they are reported.
arXiv Machine Learning
Oct 15
Rent-Seeking Language Models: Reward Models Set the Prices, Frequent Audits Curb the Rent
arXiv:2610.19832v1 Announce Type: new Abstract: Reward model overoptimization in RLHF is usually described as reward hacking. We recast it as rent-seeking: the policy, a firm, earns reward from a reg...
arXiv AI
Sep 2
The Constitutional Coverage Trilemma in AI Governance
The paper investigates how well the implicit value rankings encoded by frontier AI systems—termed constitutional institutions—meet human demand. By auditing 23 large language model archetypes and surveying 1,649 U.S. participants, the authors find that user demand spans all five values (safety, helpfulness, honesty, autonomy, equity) but the supply is narrow, covering only about 2% of the demand space, with no model prioritizing helpfulness or autonomy. They propose a sparse two‑vertex menu that substantially reduces regret compared to the full set of models and formalize these observations as a budgeted‑pluralism trilemma. whyItMatters":"The study reveals a significant mismatch between the values users prioritize and the values encoded by current AI models, highlighting the need for more diverse and aligned constitutional designs."
arXiv Machine Learning
Oct 15
Price of Anarchy of First-Price Auctions in Agentic Markets
arXiv:2610.20587v1 Announce Type: cross Abstract: An agentic market is a decentralized serving-time market where AI agents bid to claim each user prompt. An agent's bid is based on its expected value...
arXiv AI
Aug 26
The Shadow Price of Intelligence: Quality Degradation in LLM Inference as a Supply Chain Problem
The paper argues that large language model (LLM) providers, constrained by compute, often degrade service during congestion by routing queries to smaller models, cutting reasoning effort, or truncating context. It shows that this practice misrepresents costs because degraded answers can fail, leading to retries that inflate traffic or churn that erodes lifetime value. By modeling inference allocation with newsvendor, retry, and queueing frameworks, the authors derive a ‘shadow price of intelligence’ that quantifies the marginal value of each query, revealing that throttling under congestion acts as a demand lever rather than a cost lever.