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arXiv · 2407.07845

Natural Language Mechanisms via Self-Resolution with Foundation Models

Nicolas Della Penna
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Abstract

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.

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Nicolas Della Penna. 2024-07-10. Natural Language Mechanisms via Self-Resolution with Foundation Models. https://arxiv.org/abs/2407.07845

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