Information Architecture and Emergent Deceptive Selling in LLM Multi-Agent Markets

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Information architecture may shape harmful conduct in dynamic multi-agent systems, yet its relationship to objectively measured misrepresentation remains unclear. We examine seller-only communication and market-wide public history in a repeated hidden-quality market populated by 12 GPT-4o-mini seller agents and 12 buyer agents over 20 rounds. The submitted five-replicate pilot and a fresh primary-wording replication batch with twenty replicates per condition compared five conditions. Deceptive selling is operationalized solely as observable quality overstatement, defined as listing an item above its source-quality tier; strategic intent is not inferred. The primary outcome is the fraction of unseeded sellers making at least one false quality claim. With intervention sellers held fixed, the fresh primary-wording replication batch showed mean forum-associated prevalence differences of 0.850 under private history and 0.483 under market-wide public history. These contrasts apply only to intervention-present markets because the design does not include a forum condition without intervention sellers. Two seller-listing paraphrases produced materially different private-history contrasts, indicating prompt sensitivity rather than wording invariance. Across the submitted pilot, seller conduct, realized buyer harm, and market activity diverged. These descriptive results motivate multi-agent safety evaluations that jointly examine information architecture, harmful conduct, and system utility. They do not identify message versus prompt mechanisms, buyer- versus seller-side public-history mechanisms, intent, or seed-to-peer transmission.