Modelling Political Coalition Negotiations Using LLM-based Agents
Farhad Moghimifar Yuan-Fang Li Robert Thomson Gholamreza Haffari
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.