Project Swap: What happens when agents trade for us?

This post details an experiment where AI agents (Claude models) participated in a decentralized book-trading market. Key technical contributions include: 1. Evaluating agent understanding of user preferences derived from short natural language conversations (achieving 61% agreement on book pair rankings). 2. Studying agent negotiation strategies in a simulated market, comparing 'ruthless' vs. 'prosocial' agents. 3. Analyzing the impact of different underlying LLM models (Haiku, Sonnet, Opus, Fable) on market efficiency and negotiation outcomes, finding that stronger models led to more efficient markets. 4. Comparing decentralized agent-based markets to centralized benchmarks (utilitarian optimum, Top Trading Cycles). The experiment highlights challenges in agent understanding and market design for AI agents operating in real-world scenarios.