When Biased Agents Trade: Anchoring, Exploitation, and Market Failure in Agent-to-Agent Interactions

Abstract

Large language models have well-documented behavioral biases, but all existing evidence comes from single-agent settings. This paper asks what happens when two biased LLMs meet in a commercial transaction. Across 8,415 controlled interactions on three frontier models, agents anchor on first offers modestly more strongly than humans, yet resist information overload, largely ignore decoy products, and bid at equilibrium in auctions. Anchoring is the bias that breaks markets: a few dollars of price distortion per negotiation collapses a multi-agent marketplace from 96% to 15% efficiency as sellers price themselves out and buyers walk away. When one side knows the bias, it captures up to 78% of available surplus. Naming the bias in a warning recovers about 40% of the loss; telling the agent to reason step by step backfires, because deliberation makes the anchor more salient, not less. The three models fail in different ways, complicating any uniform regulatory response.

Keywords: agentic commerce, agent-to-agent interactions, large language models, behavioral biases, anchoring, winner’s curse, decoy effect

JEL Classification: D91, D18, D44, K24, L86