Modeling economic sanctions as a stochastic game: insights from Russia, Libya, and Iran

This study introduces a continuous-time stochastic game model to analyze the strategic dynamics of economic sanctions within the global monetary system, focusing on the interplay between a sanctioning player (e.g. Western countries) and a sanctioned player (e.g. Russia). Employing a four-node framework – provocation, sanctioning, reinforcement, and continuation – the model captures the recursive, uncertain interactions exemplified by Russia’s 2022 SWIFT exclusion, Libya’s 2003 strategic retreat, and the 1979 Iranian hostage crisis. By integrating game theory with stochastic differential equations, it formalizes the cost-benefit calculus of sanctions, revealing emergent patterns of sustained conflict or de-escalation driven by economic pressures, geopolitical resilience, and stochastic shocks. Monte Carlo simulations quantify how variations in sanction sensitivity, costs, and benefits shape strategic outcomes, validated against empirical data (e.g. Russia’s 20–45 billion USD/year trade losses). The model’s contributions are threefold: it advances theoretical rigor by embedding stochastic processes in game-theoretic analysis, provides practical insights for predicting sanction efficacy and unintended consequences (e.g. de-dollarization, trade realignments), and offers policymakers a versatile tool to navigate global financial volatility. This framework redefines sanctions as dynamic processes, ensuring lasting relevance for understanding economic statecraft amid deepening global interdependence.