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<p>This paper advances international relations (IR) theory by introducing a dynamic model that captures the evolving nature of state preferences through continuous learning and adaptation, moving beyond traditional models’ static and binary constraints. The main research question explores how states’ strategies and preferences evolve through repeated interactions and what implications this has for understanding international cooperation and conflict. We employ a novel integration of Bayesian Q-learning into an endogenous signaling framework, allowing for a more flexible representation of state behavior. The model is applied to analyze the economic interactions between China and the United States from 1990 to 2023, utilizing empirical data to construct adaptability and malleability indices that quantify these changes over time. The model reveals significant shifts in China’s approach to intellectual property rights, currency policies, and market access, influenced by continuous strategic adaptation and learning. Empirical findings highlight China's increasing malleability in response to U.S. signaling correlates with increased cooperation levels, while the U.S. shows a more complex adaptability pattern. The research confirms that dynamic learning models provide a deeper understanding of the fluid nature of international relations, challenging the conventional static approach. By demonstrating how preferences evolve through a learning mechanism based on socialization and strategic interactions, this paper contributes to a more nuanced comprehension of global diplomatic dynamics and offers strategic insights for policymakers.</p>

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Engineering 101: How to Construct a Realist Bridge? Start with Endogenous Foundations

  • 期刊论文

  • 发表日期：

    2025年4月3日

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  • Dwayne Woods

摘要

This paper advances international relations (IR) theory by introducing a dynamic model that captures the evolving nature of state preferences through continuous learning and adaptation, moving beyond traditional models’ static and binary constraints. The main research question explores how states’ strategies and preferences evolve through repeated interactions and what implications this has for understanding international cooperation and conflict. We employ a novel integration of Bayesian Q-learning into an endogenous signaling framework, allowing for a more flexible representation of state behavior. The model is applied to analyze the economic interactions between China and the United States from 1990 to 2023, utilizing empirical data to construct adaptability and malleability indices that quantify these changes over time. The model reveals significant shifts in China’s approach to intellectual property rights, currency policies, and market access, influenced by continuous strategic adaptation and learning. Empirical findings highlight China's increasing malleability in response to U.S. signaling correlates with increased cooperation levels, while the U.S. shows a more complex adaptability pattern. The research confirms that dynamic learning models provide a deeper understanding of the fluid nature of international relations, challenging the conventional static approach. By demonstrating how preferences evolve through a learning mechanism based on socialization and strategic interactions, this paper contributes to a more nuanced comprehension of global diplomatic dynamics and offers strategic insights for policymakers.