The coevolution of beliefs and networks

Abstract

In this research, we set out some of the behavioral foundations of social learning. Social psychologists have shown that people experience cognitive dissonance when two or more of their cognitions diverge, and that they actively manage the dissonance. With this in mind we develop a model of social learning in networks to understand the coevolution of beliefs and networks. We focus on beliefs concerning an objective phenomenon. Initial beliefs are based on noisy, private and unbiased information. Because private information is noisy, initial beliefs differ, creating dissonance. Social behavior is motivated by a desire to minimize this dissonance. In many circumstances this behavior adversely affects the efficiency of social learning, such that in equilibrium the mean aggregate belief is biased and there is significant variation of beliefs across the population. The parameterizations of our model that result in the most inefficient learning produce a fractionalized network structure in which there are a number of distinct groups: within any group all beliefs are identical; beliefs differ from group to group, sometimes greatly; there is no intergroup interaction.Since dissonance minimizing behavior is apparently a deeply rooted feature of humans, one that cannot be changed, we are led to ask: What policies could improve the situation? Our results suggest that policies that improve the availability of objective information and/or increase the size of networks enhance efficiency of social learning. On the other hand, anything that makes changing networks more attractive as a dissonance minimizing strategy has the opposite effect.

Consensus and clustering in opinion formation on networks

“…Proposition 2.3. The fixed point (a 0 , b 0 ) is a saddle point for 0 ≤ p < 1 5 , and a stable node for…”

“…Models of opinion formation in the socio-physics literature examine the dynamics of beliefs [1,2], language [3][4][5], culture [6], voting preference [7][8][9] and rumour propagation [10]. Specifically, these models examine how individuals within communities influence each other and reach (or drift from) consensus [11,12].…”

Conformity and Influence

“…The importance of network structure and individual behavior are further developed in such works as Golub and Jackson (2010), Acemoglu and Ozdaglar (2011), Acemoglu et al (2013), Corazzini et al (2012), Battiston and Stanca (2015), and Buechel et al (2015), where conformity and influence are the consequence of the social learning environment. Arifovic et al (2015) considers conformity as a motivator shaping beliefs and network formation in the context of social learning. In the current investigation, players actively seek conformity and influence.…”

Networks Formation to Assist Decision Making

“…Acemoglu et al (2010) injects a time element with agents pressed to act on their beliefs by a penalty for delaying adoption. Arifovic et al (2015) identifies a preference for consensus affecting the updating of beliefs.…”