Who Should Know What? Empirical Information Design for Multi-Agent LLM Economies

Multi-agent LLM systems are shaped not only by their component models but also by an information policy that determines what each agent observes and at what level of detail. Can giving AI agents more information lead to worse outcomes? To investigate this question, we introduce empirical information design, an experimental framework that compares disclosure policies on the same markets while holding models and interaction rules fixed. We study frontier LLMs in multi-resource trade, single-good bargaining, and double auctions, where agents interact while pursuing their individual gains. Using ground-truth utilities, we distinguish between the opportunities agents identify and the outcomes they achieve. In trade, full disclosure of exact valuations reduces total gains for GPT-5.1 and Gemini 3.7 Flash agents, but increases gains for Claude Sonnet 5 and Grok 4.3. Relative to coarse disclosure of preference rankings, GPT-5.1's total gains fall from 97.2% to 88.4% of the maximum available, while Sonnet's rise from 72.1% to 85.6%. In bargaining, exact information produces less balanced agreements for GPT-5.1, while Sonnet generally divides gains more equally. In auctions, full disclosure can change price demands enough to prevent beneficial trades, lowering GPT-5.1's total gains from 98.9% to 91.0% of the maximum. These results suggest that model selection and information design should be coupled, as models differ in how their responses to the same information support the system's objective.