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Evidence record 4813 · automatically gathered

Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State

Outcome metrics can certify the wrong behavior. We study this failure in a two-hotel revenue-management simulator where Hotel A trains an agent against a fixed rule-based revenue-management competitor, Hotel B. A standard learning agent can obtain near-reference revenue per available room (RevPAR) while failing to learn market-like yield management: it sells too aggressively, undercuts, or collapses to modal price buckets. We diagnose this as a Goodhart-style failure under partial observability.

Record details

Published: 7 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare · Agents & autonomy
Retrieved: 14 July 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Same publisher byline · Shared topic: Safety & alignment

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Shared topic: Safety & alignment · Shared topic: Healthcare

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Shared topic: Safety & alignment · Shared topic: Healthcare

ProtoMedAgent: Multimodal Clinical Interpretability via Privacy-Aware Agentic Workflows

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Same publisher byline · Shared topic: Agents & autonomy

When Outcome Looks Right But Discipline Fails: Trace-Based Evaluation Under Hidden Competitor State

arXiv · 18 May 2026

Shared topic: Safety & alignment · Shared topic: Healthcare

AI Safety Training Can be Clinically Harmful

arXiv · 25 April 2026

How to cite this record

ethics.ai (7 May 2026), “Market-Alignment Risk in Pricing Agents: Trace Diagnostics and Trace-Prior RL under Hidden Competitor State,” evidence record 4813, https://ethics.ai/record/4813 (originally published by arXiv).

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