Learning Opinions by Observing Actions: Simulation of Opinion Dynamics Using an Action-Opinion Inference Model

Abstract

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 di erent 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.

Opinion dynamics of social learning with a conflicting source

“…Towards this end, it would be interesting to explore the possibilities for applying the models considered here to real data available from social media. This is a challenge for research in opinion dynamics more generally as noted in [17], but one possibility might be to draw on work that infers opinions from users' actions [25,26].…”

Hiding opinions by minimizing disclosed information: an obfuscation-based opinion dynamics model

“…In a continuous-time context, the dynamics of agent i's opinion in a sufficiently short time interval Δt is: 7 Most part of the derivation was modified from Tang and Chorus (2019).…”

Modelling the influence of peers’ attitudes on choice behaviour: Theory and empirical application on electric vehicle preferences

“…This gives a strong case to argue that (as the outcome of a long term attitude of the individual. However, in everyday life, individuals do not necessarily directly observe the attitudes and the opinions of others nor frequently exchange information about them (Tang & Chorus, 2019). This is strictly linked to the indirect influence illustrated by Ng (1980) like the subtle effects of attitudes in a social network might affect an individual making process.…”

“…Indeed, the elements of this matrix are a measure of the type of relationship between the individuals, such as social proximity or frequency of interaction (Carrasco & Miller, 2006;Carrasco et al, 2008). Social networks have widely been used as a source to explore activity-travel decisions (Carrasco et al, 2008;Frei & Axhausen, 2009;van den Berg et al, 2013) and social (Pike, 2014;Kim & Parent, 2016). However, there remain many challenges in modelling social influence in practice.…”