Opinion dynamics: models, extensions and external effects (1605.06326v1)

Published 20 May 2016 in physics.soc-ph and cs.SI

Abstract: Recently, social phenomena have received a lot of attention not only from social scientists, but also from physicists, mathematicians and computer scientists, in the emerging interdisciplinary field of complex system science. Opinion dynamics is one of the processes studied, since opinions are the drivers of human behaviour, and play a crucial role in many global challenges that our complex world and societies are facing: global financial crises, global pandemics, growth of cities, urbanisation and migration patterns, and last but not least important, climate change and environmental sustainability and protection. Opinion formation is a complex process affected by the interplay of different elements, including the individual predisposition, the influence of positive and negative peer interaction (social networks playing a crucial role in this respect), the information each individual is exposed to, and many others. Several models inspired from those in use in physics have been developed to encompass many of these elements, and to allow for the identification of the mechanisms involved in the opinion formation process and the understanding of their role, with the practical aim of simulating opinion formation and spreading under various conditions. These modelling schemes range from binary simple models such as the voter model, to multi-dimensional continuous approaches. Here, we provide a review of recent methods, focusing on models employing both peer interaction and external information, and emphasising the role that less studied mechanisms, such as disagreement, has in driving the opinion dynamics. [...]

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Authors (4)

Vittorio Loreto (54 papers)
Vito D. P. Servedio (22 papers)
Francesca Tria (17 papers)
Alina Sîrbu (18 papers)

Citations (193)

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Summary

Opinion Dynamics: Models, Extensions, and External Effects

The paper "Opinion dynamics: models, extensions and external effects" provides a comprehensive review of contemporary modeling approaches to understanding opinion dynamics within complex social systems. Authored by Alina Sârbu, Vittorio Loreto, Vito D.P. Servedio, and Francesca Tria, the paper addresses the multifaceted nature of opinion formation and dissemination, considering both internal peer interactions and external influences such as mass media.

The significance of this work lies in its thorough exploration of various modeling frameworks that capture the essence of opinion dynamics. These models range from traditional binary approaches, such as the Ising and voter models, to advanced multidimensional continuous frameworks, including the Deffuant-Weisbuch and Hegselmann-Krause models. Each model differs in how it represents opinions—either as discrete states or continuous variables—reflecting the versatility required to simulate real-world social phenomena.

Several key findings emerge from this review. First, the inclusion of less conventional interactions, such as disagreement and contrarian behavior, serves to enrich the dynamics and outcomes predicted by traditional models. Such interactions foster coexistence among diverse opinions, challenging the common trajectory towards consensus often expected in social systems. Noise and variability in agent behavior further add complexity, introducing phenomena like spontaneous opinion shifts and fluctuating cluster dynamics, particularly in continuous opinion models.

The incorporation of external information sources, primarily represented by mass media, presents another dimension to opinion dynamics modeling. Although numerous studies in the paper focus on discrete models such as Axelrod or Deffuant, findings indicate that strong or extreme external influences can inadvertently lead to opinion fragmentation, rather than fostering uniform consensus. Media competition and interaction frequency play crucial roles in shaping collective behavior, suggesting that nuanced approaches to media influence are vital in simulating real-world scenarios.

Despite the robust theoretical insights presented, the paper identifies a notable gap in empirical validation of these models against real-world data. Although qualitative comparisons suggest alignment between model outputs and observed social behaviors, quantitative analyses remain scarce. This points to an urgent need for future research to engage more rigorously with emerging data streams, facilitated by advances in technology and human computation, for more empirical grounding of opinion dynamics theories.

In essence, the paper lays a strong foundation for understanding opinion dynamics through various modeling perspectives, acknowledging the inherent complexity and interconnectedness of social systems. The exploration of external effects opens avenues for further investigation, particularly pertaining to the role of media and algorithmic feedbacks in contemporary society. Looking ahead, bridging theoretical models with real-world data stands as the next pivotal step in advancing the field and enhancing predictive capabilities in sociological research.

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