Opinion Polarization in Human Communities Can Emerge as a Natural Consequence of Beliefs Being Interrelated

Abstract

The emergence of opinion polarization within human communities—the phenomenon that individuals within a society tend to develop conflicting attitudes related to the greatest diversity of topics—has been a focus of interest for decades, both from theoretical and modelling points of view. Regarding modelling attempts, an entire scientific field—opinion dynamics—has emerged in order to study this and related phenomena. Within this framework, agents’ opinions are usually represented by a scalar value which undergoes modification due to interaction with other agents. Under certain conditions, these models are able to reproduce polarization—a state increasingly familiar to our everyday experience. In the present paper, an alternative explanation is suggested along with its corresponding model. More specifically, we demonstrate that by incorporating the following two well-known human characteristics into the representation of agents: (1) in the human brain beliefs are interconnected, and (2) people strive to maintain a coherent belief system; polarization immediately occurs under exposure to news and information. Furthermore, the model accounts for the proliferation of fake news, and shows how opinion polarization is related to various cognitive biases.

Agent-Based Modeling of Consensus Group Formation with Complex Webs of Beliefs

“…Therefore, there is no sharp exclusion of interactions, but instead, bias for similarity (homophily). In this respect, the WoBs introduced here parallel some other recent attempts to extend the consensus and opinion formation models [7,13].…”

“…Second, the WoBs and their update rules do not take into account the coherence of elements in the WoBs, nor requirements of coherence when elements are added or deleted. In some recent ABM approaches, the structure and coherence of agents' opinions (or beliefs) are taken into account, allowing more realistic description of complex opinion and belief systems [7,44,45]. The inclusion of coherence of belief elements as part of ABM is, however, not unproblematic; diverse opinions exist regarding how to implement that notion as part of the idea of network-like knowledge.…”

“…The event k is selected if a random number 0 < r < 1 falls in the slot Φ k−1 < r < Φ k . In case (1), the probabilities p k are given by P ξγ in Equation ( 1), while in case (2), one has only two probabilities p k = P ± defined by Equation (7). The roulette-wheel method is thus entirely event-based, where the occurrence of events is predicted on the basis of cumulative distribution.…”

“…The computational modeling of opinion group formation [5][6][7], the formation of collaborative groups and collective decision making [2,8,9], as well as disciplinary fragmentation and progress in science [10], has shed light on the social dynamics behind group formation, segregation, and consolidation, often revealing the unexpectedly simple but self-reinforcing interactions behind such complex phenomena. Consequently, consensus group formation and opinion adoption, and their change and evolution, have been modeled using a variety of idealized models, many of them founded on one or another theoretical view about social influence and interaction or social learning, some of them being computational renderings of empirical findings.…”

“…The role of noise and its non-trivial effects in deterministically thresholded bounded confidence models suggests that to model the bounded regions of interactions due to similarity bias, genuinely stochastic and probabilistic rules to decide whether or not the interactions happen are sometimes preferable [13,14]. Finally, important and interesting recent generalizations of bounded confidence models are models where opinions or beliefs are multidimensional [15][16][17], or in one way or another, more complex structures of beliefs and related opinion elements [2,7,9,18]. Such models allow more realistic modeling of complex opinions, give rise to richer dynamics than onedimensional bounded confidence models, and, moreover, the emerging clusters of opinions are in these cases more diverse than those found in one-dimensional models.…”