Consider a group of users (agents) in a social network among whom some process of opinion formation takes place. In general, these users will take into account the opinions of others subject to their own biases. Indeed users in social networks may shape their beliefs by attributing more value to the opinions of influential figures. This common cognitive bias is known as authority bias [1]. Furthermore, social networks often target their users with information that they may already agree with to keep engagement. It is known that users tend to give more value to opinions that confirm their own preexisting beliefs [2] in another common cognitive bias known as confirmation bias. As a result, users can become radical and isolated in their own ideological circle, causing dangerous splits in society [3] in a phenomenon known as polarization [2].

There is a growing interest in studying opinion formation, consensus and polarization in the context of social networks by adapting measures and models from economics and statistics such as Degroot learning and Esteban and Ray’s polarization. Roughly speaking in these models the agents update their beliefs about the proposition of interest taking into account the beliefs of their neighbors in an underlying weighted influence graph. Agents update their beliefs giving more value to the opinion of agents with higher influence (authority bias) and to the opinion of agents with similar views (confirmation bias). Esteban and Ray’s measure captures the intuition that polarization is accentuated by both intra-group homogeneity and inter-group heterogeneity.

In this talk I will motivate and introduce some of these models and measures, present our contributions to this subject and discuss some ongoing and future work. In particular, our more insightful result establishes that, under some natural assumptions, if polarization does not eventually vanish then either there is a disconnected subgroup of agents, or some agent influences others more than she is influenced.

[1] Ramos,V.J.: Analyzing the Role of Cognitive Biases in the Decision Making Process. IGI Global (2019) [2] Aronson, E., Wilson, T., Akert, R.: Social Psychology. Upper Saddle River, NJ : Prentice Hall, 7 edn. (2010) [3] Bozdag, E.: Bias in algorithmic filtering and personalization. Ethics and Information Technology (2013).