Modeling Warfare Conflicts at the Operational Level as a Game under a Social-Learning DeGroot Network

Abstract

This study investigates the integration of the DeGroot model and game theory to enhance strategic decision-making in military contexts. Traditional models such as the Prisoners' Dilemma and Chicken Game are critiqued for their dependence on assumptions of rationality and complete information. Recent advancements, including Prospect Theory and bounded rationality, are explored for their ability to address real-world complexities, such as misinformation and cognitive biases. The DeGroot model is introduced as an effective tool for iterative belief updates, reflecting the dynamic nature of military operations. The paper proposes a multi-level dynamic game framework incorporating the DeGroot update method to account for evolving payoffs and strategic interactions over time. This integrated framework is exemplified through historical instances such as the Iraq War, where misinformation about weapons of mass destruction led to flawed strategic decisions, and the Vietnam War, where biases distorted perceived payoffs. The model underscores the significance of real-time information processing and the influence of biases and misinformation on decision-making. Future research directions include the integration of machine learning algorithms to enhance data processing, the exploration of specific cognitive biases in decision-making, the application of the model to non-military conflicts, empirical validation with real-time data, extensions to complex multi-agent systems, and the incorporation of humanitarian decision-making. The proposed framework provides a comprehensive approach to understanding and predicting military strategies, with potential applications extending beyond traditional warfare to other strategic domains such as cyber security and economic conflicts.