Stanford Paper Finds More Information Can Hurt Some LLM Trading Agents
A new Stanford paper tested 10 frontier LLM agents in trading, bargaining and double auctions, varying only the precision of disclosed private valuations. More information was not universally beneficial: exact values cut GPT-6 Sol's gains from 98% to 91% of maximum but raised Claude Sonnet 5's from 72% to 86%, while full disclosure in auctions inflated price demands and dropped Grok 4.7 from 97% to 62%. The takeaway is that multi-agent systems need model-specific rules on what each agent can see.
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Rohan Paul (@rohanpaul_ai) · 2026-11-07 22
New Stanford paper shows that more information helps some LLM agents trade better and hurts others, depending on the model.
AI agents buying and selling in an auction were told exactly how much each side valued the goods, Claude Opus 5.5’s share of possible gains fell from 94% to 64%.
Multi-agent systems need rules on what each agent sees about others, such as exact prices or only rankings. The usual assumption is that more information helps.
They ran 10 frontier LLMs in trading, bargaining, and double auctions, changing only how precisely private values were shared, and scored gains against known utilities.
In trade, exact values cut GPT-6 Sol's gains from 98% to 91% of the maximum but raised Claude Sonnet 5's from 72% to 86%. In auctions, full disclosure inflated price demands and dropped Grok 4.7 from 97% to 62%.
When agents negotiate for you, test the model and what it gets told together, because more detail makes some models greedier.
