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LLMs Learn to Play Games Strategically

Large language models (LLMs) are becoming increasingly sophisticated, but they still struggle to play games strategically. A new paper from researchers at Rutgers University and other institutions investigated the rationality of LLMs in game-theoretic scenarios and proposes ways to improve their performance.

The researchers found that LLMs often deviate from rational strategies, particularly in complex games. For example, in the classic “Prisoner’s Dilemma,” LLMs often chose to defect, even when cooperation would have been the best strategy. The researchers attribute this behavior to a lack of robustness to noise and uncertainty in LLMs.

To address this, the researchers developed game-theory-inspired workflows that guide the reasoning and decision-making processes of LLMs. These workflows incorporate principles like dominant strategy search, backward induction, and Bayesian belief updating. The researchers tested these workflows on a variety of games, including the Prisoner’s Dilemma, the Battle of the Sexes, and the Escalation Game. In every case, the workflows significantly improved the LLMs’ performance, enabling them to achieve near-optimal outcomes and make more rational choices.

The researchers also explored the impact of negotiation on LLM behavior. They found that while negotiation can sometimes improve performance, it can also lead to suboptimal outcomes. For instance, LLMs often exhibit a tendency to trust the statements of other players without sufficient justification, which can lead them to make decisions that are not in their best interest.

The researchers suggest that prompt engineering can be used to mitigate the negative impact of negotiation on LLM rationality. By carefully crafting prompts, researchers can encourage LLMs to be more skeptical and analytical during negotiations and to make their own decisions without relying on the other player’s statements.

This study offers valuable insights into the limitations of LLMs in strategic decision-making contexts. The researchers’ work provides a promising roadmap for developing more robust and strategically sound AI agents capable of navigating complex interactive environments.

The paper’s findings highlight the importance of integrating game-theoretic principles into the design of AI systems. By incorporating these principles, researchers can develop more sophisticated and reliable AI agents capable of making optimal decisions in a wide range of strategic settings. This research has the potential to revolutionize the development of AI-powered systems, leading to new breakthroughs in areas like automated negotiation, economic modeling, and collaborative problem-solving.