Inference in Opinion Dynamics Under Social Pressure
Ali Jadbabaiea, Anuran Makurb, Elchanan Mosselc, Rabih Salhabd
- aDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- bDepartment of Computer Science and the Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA
- cDepartment of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
- dAmazon, New York, NY, USA
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Abstract
We introduce a new opinion dynamics model where a group of agents holds two kinds of opinions: inherent and declared. Each agent’s inherent opinion is fixed and unobservable by the other agents. At each time step, agents broadcast their declared opinions on a social network, which are governed by the agents’ inherent opinions and social pressure. In particular, we assume that agents may declare opinions that are not aligned with their inherent opinions to conform with their neighbors. This raises the natural question: Can we estimate the agents’ inherent opinions from observations of declared opinions? For example, agents’ inherent opinions may represent their true political alliances (Democrat or Republican), while their declared opinions may model the political inclinations of tweets on social media. In this context, we may seek to predict the election results by observing voters’ tweets, which do not necessarily reflect their political support due to social pressure. We analyze this question in the special case where the underlying social network is a complete graph. We prove that, as long as the population does not include large majorities, estimation of aggregate and individual inherent opinions is possible. On the other hand, large majorities force minorities to lie over time, which makes asymptotic estimation impossible.
TL;DRIt is proved that, as long as the population does not include large majorities, estimation of aggregate and individual inherent opinions is possible; on the other hand, large majorities force minorities to lie over time, which makes asymptotic estimation impossible. (Semantic Scholar)