Coalition negotiations are a cornerstone of parliamentary democracies, characterised by complex interactions and strategic communications among political parties. Despite its significance, the modelling of these negotiations has remained unexplored with the domain of Natural Language Processing (NLP), mostly due to lack of proper data. In this paper, we introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents. We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries. This dataset addresses the challenge of the current scope limitations in political negotiation modelling by providing a diverse, real-world basis for simulation. Additionally, we propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes. We evaluate the performance of state-of-the-art large language models (LLMs) as agents in handling coalition negotiations, offering insights into their capabilities and paving the way for future advancements in political modelling.

煤炭联盟谈判是议会民主制的基石，主要包括政党之间的复杂互动和战略沟通。本文将煤炭联盟谈判作为一种新颖的自然语言处理（NLP）任务，并模拟其为基于大型语言模型的代理之间的协商。我们引入了一个多语种数据集POLCA，包括欧洲政党的宣言和这些国家多次选举中的联盟协议。该数据集通过提供多样化的现实基础来解决当前政治谈判建模范围的挑战。此外，我们提出了一个层次化的马尔可夫决策过程，旨在模拟政党之间的联盟谈判过程并预测结果。我们评估了最先进的大型语言模型（LLMs）在处理联盟谈判中的性能，并为政治建模的未来发展提供了见解。

基于LLM的代理模型解析政治联盟谈判