Human and Algorithmic Predictions in Geopolitical Forecasting: Quantifying Uncertainty in Hard-to-Quantify Domains.

Area of Science:

• Decision Science
• Computational Social Science
• International Relations

Background:

• Statistical prediction models often outperform human judgment in various fields.
• Geopolitical forecasting presents unique challenges due to complex, qualitative data and difficulties in defining comparable historical cases.
• High-stakes geopolitical events necessitate accurate forecasting, traditionally relying on human expertise.

Purpose of the Study:

• To evaluate the role and impact of algorithms in geopolitical forecasting.
• To explore the potential for hybrid human-algorithmic approaches to enhance predictive accuracy.
• To determine the optimal integration of algorithms within human-led geopolitical analysis.

Main Methods:

• Review of existing research comparing algorithmic and human prediction accuracy.
• Analysis of the specific challenges in applying predictive algorithms to geopolitical data.
• Conceptual framework for integrating algorithms as tools for human forecasters.

Main Results:

• Algorithms demonstrate superior predictive accuracy in many domains, but geopolitical forecasting is challenging for purely algorithmic approaches.
• Algorithms are valuable for aggregating diverse information, structuring human judgment, and forming hybrid models.
• Human forecasters are essential, but their accuracy is significantly enhanced by algorithmic assistance.

Conclusions:

• Algorithms are crucial tools, not replacements, for human geopolitical forecasters.
• Hybrid models combining human expertise with algorithmic support offer the most promising path to improved geopolitical forecast accuracy.
• Continued development and integration of algorithms are vital for advancing the field of geopolitical prediction.