Polarized opinions are everywhere. From opposite attitudes towards Hawaiian pizza to the partisan divide in theUnited States, we have experienced enough opinion polarization in recent years. Sadly, it is usually a sign of follow-up criticism when people start to talk about "opinion polarization". The term, which should neutrally describe a widespread social phenomenon, has been proven to be associated with different dismaying outcomes, ranging from hostility to civil wars. Given its harmful consequence, few would doubt the urgent need for a solution to this long-lasting issue, and such a solution requires a deep understanding of opinion polarization in real-life situations. The urgent need has motivated remarkable research efforts in the past few decades. Especially in the domain of computational sociology, a considerable amount of opinion dynamics models have been proposed to explain opinion polarization from microscopic mechanisms that govern interactions between individuals. A common feature of these models, which probably results from their roots in statistic physics, is that opinions are observable and can be directly affected by other opinions just like a "spin" in the famous Ising model. However, opinions in real life are of fundamental difference from "spin" in the sense that it is by nature private and unobservable, whose expression, transmission, and inference largely depend on observable behaviors: even if people are allowed to verbally exchange opinions, how these opinions are translated into words and how these words are inferred by both parties still play a critical role in the dynamics of opinions. Thereby, we could put forward a thesis (which we did, literally) that there is a fundamental discrepancy between opinion polarization in the literature and opinion polarization in real-life situations that would deteriorate our trust in these models, let alone the solutions generated accordingly. @en

The growing polarization of our societies and economies has been extensively studied in various disciplines and is subject to public controversy. Yet, measuring polarization is hampered by the discrepancy between how polarization is conceptualized and measured. For instance, the notion of group, especially groups that are identified based on similarities between individuals, is key to conceptualizing polarization but is usually neglected when measuring polarization. To address the issue, this paper presents a new polarization measurement based on a grouping method called “Equal Size Binary Grouping” (ESBG) for both uni- and multi-dimensional discrete data, which satisfies a range of desired properties. Inspired by techniques of clustering, ESBG divides the population into two groups of equal sizes based on similarities between individuals, while overcoming certain theoretical and practical problems afflicting other grouping methods, such as discontinuity and contradiction of reasoning. Our new polarization measurement and the grouping method are illustrated by applying them to a two-dimensional synthetic data set. By means of a so-called “squeezing-and-moving” framework, we show that our measurement is closely related to bipolarization and could help stimulate further empirical research.@en

In the field of opinion dynamics, the hiding of opinions is routinely modeled as staying silent. However, staying silent is not always feasible. In situations where opinions are indirectly expressed by one’s observable actions, people may however try to hide their opinions via a more complex and intelligent strategy called obfuscation, which minimizes the information disclosed to others. This study proposes a formal opinion dynamics model to study the hitherto unexplored effect of obfuscation on public opinion formation based on the recently developed Action-Opinion Inference Model. For illustration purposes, we use our model to simulate two cases with different levels of complexity, highlighting that the effect of obfuscation largely depends on the subtle relations between actions and opinions.

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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.

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