Opinion dynamics models are based on the implicit assumption that people can observe the opinions of others directly, and update their own opinions based on the observation. This assumption significantly reduces the complexity of the process of learning opinions, but seems to be rather unrealistic. Instead, we argue that the opinion itself is unobservable, and that people attempt to infer the opinions of others by observing and interpreting their actions. Building on the notion of Bayesian learning, we introduce an action-opinion inference model (AOI model); this model describes and predicts opinion dynamics where actions are governed by underlying opinions, and each agent changes her opinion according to her inference of others’ opinions from their actions. We study different action-opinion relations in the framework of the AOI model, and show how opinion dynamics are determined by the relations between opinions and actions. We also show that the well-known voter model can be formulated as being a special case of the AOI model when adopting a bijective action-opinion relation. Furthermore, we show that a so-called inclusive opinion, which is congruent with more than one action (in contrast with an exclusive opinion which is only congruent with one action), plays a special role in the dynamic process of opinion spreading. Specifically, the system containing an inclusive opinion always ends up with a full consensus of an exclusive opinion that is incompatible with the inclusive opinion, or with a mixed state of other opinions, including the inclusive opinion itself. A mathematical solution is given for some simple action-opinion relations to help better understand and interpret the simulation results. Finally, the AOI model is compared with the constrained voter model and the language competition model; several avenues for further research are discussed at the end of the paper.

中文翻译：

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通过观察动作学习观点：使用动作观点推理模型对观点动力学进行仿真

意见动态模型基于人们可以直接观察他人的意见并基于观察结果更新自己的意见的隐含假设。这种假设大大降低了学习意见的过程的复杂性，但似乎是不现实的。相反，我们认为观点本身是不可观察的，人们试图通过观察和解释自己的行为来推断他人的观点。在贝叶斯学习概念的基础上，我们引入了行动-观点推理模型（AOI模型）。该模型描述并预测了意见动态，其中行为由基本意见控制，每个行为人根据自己从他人行为中得出的其他意见的推论改变其意见。我们在AOI模型的框架内研究了不同的行动与意见之间的关系，并展示如何通过观点与行动之间的关系来决定观点动态。我们还表明，当采用双射动作-观点关系时，可以将众所周知的投票者模型表示为AOI模型的特例。此外，我们表明与一个以上行动一致的所谓包容性意见（与仅与一个行动一致的排他性意见相反）在意见传播的动态过程中起着特殊的作用。具体而言，包含包容性意见的系统始终以与包容性意见不相容的排他性意见的完全共识或其他意见（包括包容性意见本身）的混合状态结束。给出了一些简单的动作-观点关系的数学解决方案，以帮助更好地理解和解释模拟结果。最后，将AOI模型与受限选民模型和语言竞争模型进行了比较。本文最后讨论了一些进一步研究的途径。