Evaluating Rational Contracting in Natural Language

Abstract

The emergence of language-based AI agents promises to transform the scope of machine economic activity. Instead of just proposing bids or following hard-coded protocols, such agents can be used to negotiate and execute agreements in natural language. However, most evaluations of these abilities have focused on one-off exchanges or simple economic games, leaving open the rich space of time-extended, contingent, and incomplete contracts expressible in language; they also focus on raw profit, without measuring the qualities required for trustworthy contracting. We address this by formulating a rational framework for how agents should negotiate and perform natural language contracts in uncertain multi-step environments. Using this framework, we develop metrics and baselines for quantifying rational and cooperative play. We instantiate our framework in ContractSim, an evaluation suite where two players negotiate and execute a multi-turn supplier contract under environmental and inter-player uncertainty. We find that LLM-based agents agree on efficient contracts when environmental uncertainty is low, but under high uncertainty, often fail to negotiate satisfiable, efficient, or mutually beneficial contracts, underperforming even heuristic baselines. When executing contracts, LLM agents are also frequently uncooperative, violating contracts for extra profit even when they are easy to satisfy. These findings highlight room for improvement in the design of language agents that can negotiate and execute contracts both rationally and cooperatively.

arXiv comment10 pages, 7 figures, 2 tables (Appendix: 40 pages, 8 figures, 10 tables)