Opinion Dynamics and Wisdom Under Conformity

Abstract

We study a dynamic model of opinion formation in social networks. In our model, boundedly rational agents update opinions by averaging over their neighbors' expressed opinions, but may misrepresent their own opinion by conforming or counter-conforming with their neighbors. We show that an agent's social influence on the long-run group opinion is increasing in network centrality and decreasing in conformity. For efficiency of information aggregation ("wisdom"), misrepresentation of opinions need not undermine wisdom. Given the network, we provide the optimal distribution of conformity levels in the society and show which agents should be more conforming in order to increase wisdom.

A model of anonymous influence with anti-conformist agents

“…Opinion conformity has been studied widely in various fields and settings, and by using different approaches; for surveys, see e.g., Jackson (2008); Acemoglu and Ozdaglar (2011). A subset of this literature focuses on various extensions of the DeGroot model (DeGroot (1974)), see e.g., DeMarzo et al (2003); Jackson (2008); Golub and Jackson (2010); Büchel et al (2014Büchel et al ( , 2015; Grabisch et al (2017), and for a survey, e.g., Golub and Sadler (2016). So far, the analysis of the anti-conformist behavior is much less common than the study devoted to the phenomenon of conformism.…”

“…Grabisch and Rusinowska (2010a,b) address the problem of measuring negative influence in a social network but only in one-step (static) settings. Büchel et al (2015) study a dynamic model of opinion formation, where agents update their opinion by averaging over opinions of their neighbors, but might misrepresent their own opinion by conforming or counter-conforming with the neighbors. Although their model is related to DeGroot (1974), it is very different from our framework of anonymous influence with conformist and anti-conformist agents.…”

Bounded Confidence Opinion Dynamics in a Social Network of Bayesian Decision Makers

“…There have been several recent extensions of bounded confidence opinion dynamics models including models with prominent agents [23], stubborn agents [26], [27], rebel agents [28], agents with vector-valued opinions [29], agents with inertia [30], agents with heterogeneous influence [31], and agents that misrepresent their opinions to conform [32]. Moreover, there are extensions that introduce models in which only other people with whom a preexisting social connection exists have influence [33], [34], in which societies have internal hierarchies [35], in which there are interactions with a few random agents outside the bounded confidence [36], and in which interactions change gradually with confidence rather than at an abrupt bound [37].…”

Boundedly rational opinion dynamics in social networks: Does indegree matter?

“…2 This basic framework has been widely adopted and extended, explicitly assuming a communication network and modelling opinions as point estimates rather than probability distributions (e.g., Bala and Goyal, 1998, Golub and Jackson, 2010, Möbius et al, 2010, Jadbabaie et al, 2012, Buechel et al, 2014. In a prominent paper, DeMarzo et al (2003) start from a general model in which updating weights can differ among neighbors and change over time, to 1 Understanding opinion dynamics in social networks has become even more relevant with the recent advent of communication technologies, such as Facebook and Twitter, that provide explicit representations of the previously implicit network of connections.…”