Summary of Game-theoretic Llm: Agent Workflow For Negotiation Games, by Wenyue Hua et al.
Game-theoretic LLM: Agent Workflow for Negotiation Games
by Wenyue Hua, Ollie Liu, Lingyao Li, Alfonso Amayuelas, Julie Chen, Lucas Jiang, Mingyu Jin, Lizhou Fan, Fei Sun, William Wang, Xintong Wang, Yongfeng Zhang
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Computer Science and Game Theory (cs.GT); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the decision-making abilities of large language models (LLMs) in strategic settings, using game theory as a framework. It assesses several top-performing LLMs across various complete- and incomplete-information games, discovering that these models often stray from rational strategies, particularly when faced with more complex payoff matrices or deeper sequential trees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how good large language models are at making decisions in strategic situations. It tests some of the best LLMs against each other in different types of games, both ones where everyone has all the information and ones where people don’t have everything they need to know. The results show that these AI models often make choices that aren’t the most rational, especially when dealing with really complicated situations. |
