ThinkTank-ME: A Multi-Expert Framework for Middle East Event Forecasting

TL;DR

ThinkTank-ME introduces a multi-expert framework for Middle East event forecasting, leveraging collaborative analysis to better capture regional geopolitical complexities, outperforming single-model approaches.

Contribution

The paper presents a novel multi-expert framework and a dedicated benchmark for Middle East event forecasting, enhancing prediction accuracy in complex geopolitical contexts.

Findings

Multi-expert collaboration improves forecasting accuracy.

The framework outperforms single-model baselines.

Benchmark facilitates rigorous evaluation of regional event prediction.

Abstract

Event forecasting is inherently influenced by multifaceted considerations, including international relations, regional historical dynamics, and cultural contexts. However, existing LLM-based approaches employ single-model architectures that generate predictions along a singular explicit trajectory, constraining their ability to capture diverse geopolitical nuances across complex regional contexts. To address this limitation, we introduce ThinkTank-ME, a novel Think Tank framework for Middle East event forecasting that emulates collaborative expert analysis in real-world strategic decision-making. To facilitate expert specialization and rigorous evaluation, we construct POLECAT-FOR-ME, a Middle East-focused event forecasting benchmark. Experimental results demonstrate the superiority of multi-expert collaboration in handling complex temporal geopolitical forecasting tasks. The code is available at https://github.com/LuminosityX/ThinkTank-ME.