Natural Language Mechanisms via Self-Resolution with Foundation Models

Understand

Practical mechanisms often limit agent reports to constrained formats like trades or orderings, potentially limiting the information agents can express.

• We propose a novel class of mechanisms that elicit agent reports in natural language and leverage the world-modeling capabilities of large language models (LLMs) to select outcomes and assign payoffs.
• We identify sufficient conditions for these mechanisms to be incentive-compatible and efficient as the LLM being a good enough world model and a strong inter-agent information over-determination condition.
• We show situations where these LM-based mechanisms can successfully aggregate information in signal structures on which prediction markets fail.