Search papers, labs, and topics across Lattice.
The authors introduce "Cooperate to Compete" (C2C), a multi-agent environment where players with asymmetric objectives engage in private negotiations to form alliances and compete to achieve their secret objective. They compare human and LM-based agent negotiation strategies within C2C, finding that humans favor simpler deals and are less reliable partners, but more aggressive negotiators. By prompting LM-based agents to mimic human negotiation aggressiveness, the authors improved agent win rates from 22.2% to 32.7%, demonstrating the potential for targeted prompting to enhance strategic coordination.
Humans are softies: AI agents can learn to win more by being more aggressive in negotiations, outperforming human players in a mixed-motive game.
Language Model (LM)-based agents remain largely untested in mixed-motive settings where agents must leverage short-term cooperation for long-term competitive goals (e.g., multi-party politics). We introduce Cooperate to Compete (C2C), a multi-agent environment where players can engage in private negotiations while competing to be the first to achieve their secret objective. Players have asymmetric objectives and negotiations are non-binding, allowing alliances to form and break as players'short-term interests align and diverge. We run AI only games and conduct a user study pitting human players against AI opponents. We identify significant differences between human and AI negotiation behaviors, finding that humans favor lower-complexity deals and are significantly less reliable partners compared to LM-based agents. We also find that humans are more aggressive negotiators, accepting deals without a counteroffer only 56.3% of the time compared to 67.6% for LM-based agents. Through targeted prompting inspired by these findings, we modify agents'negotiation behavior and improve win rates from 22.2% to 32.7%. We run over 1,100 games with over 16,000 private conversations totaling 15.2 million tokens and over 150,000 player actions. Our results establish C2C as a testbed for studying and building LM-based agents that can navigate the sophisticated coordination required for real-world deployments. The game, code, and dataset may be found at https://negotiationgame.io/c2c.
BAIR