Traditional models of opinion dynamics provide a simple approach to understanding human behavior in basic social scenarios. However, when it comes to issues such as polarization and extremism, we require a more nuanced understanding of human biases and cognitive tendencies. In this paper, we propose an approach to modeling opinion dynamics by integrating mental models and assumptions of individuals agents using Bayesian-inspired methods. By exploring the relationship between human rationality and Bayesian theory, we demonstrate the efficacy of these methods in describing how opinions evolve. Our analysis leverages the Continuous Opinions and Discrete Actions (CODA) model, applying Bayesian-inspired rules to account for key human behaviors such as confirmation bias, motivated reasoning, and our reluctance to change opinions. Through this, we obtain update rules that offer deeper insights into the dynamics of extreme opinions. Our work sheds light on the role of human biases in shaping opinion dynamics and highlights the potential of Bayesian-inspired modeling to provide more accurate predictions of real-world scenarios. Keywords: Opinion dynamics, Bayesian methods, Cognition, CODA, Agent-based models

翻译：传统的观点动态模型为理解基本社会场景中的人类行为提供了简单方法。然而，在应对两极化和极端主义等问题时，我们需要对人类偏见和认知倾向有更细致的理解。本文提出一种通过整合个体主体的心理模型与假设来构建观点动态模型的方法，并借助贝叶斯启发式方法展开研究。通过探究人类理性与贝叶斯理论之间的关系，我们论证了这些方法在描述观点演化过程中的有效性。我们的分析依托持续观点与离散行动（CODA）模型，应用贝叶斯启发式规则来刻画确认偏差、动机性推理以及人们不愿改变观点等关键人类行为。由此获得的更新规则为极端观点的动态演化提供了更深入的洞见。本研究揭示了人类偏见在塑造观点动态中的作用，并凸显了贝叶斯启发式建模在更准确预测现实情境方面的潜力。关键词：观点动态，贝叶斯方法，认知，CODA，基于主体的模型