About renegades and outgroup-haters: Modelling the link between social influence and intergroup attitudes

Polarization between groups is a major topic of contemporary societal debate as well as of research into intergroup relations. Formal modelers of opinion dynamics try to explain how intergroup polarization can arise from simple first principles of interactions within and between groups. Models have been proposed in which intergroup attitudes affect social influence in the form of homophily or xenophobia, elaborated as fixed tendencies of individuals to interact more with in-group members, be more open to influence from in-group members and perhaps even distance oneself from attitudes of outgroup members. While these models can generate polarization between groups, their underlying assumptions curiously neglect a central insight from research on intergroup attitudes. Intergroup attitudes are themselves subject to social influence in interactions with both in- and outgroup members. I extend an existing model of opinion formation with intergroup attitudes, by adding this feedback-effect. I show how this changes model predictions about the process and the conditions of polarization between groups. In particular, it is demonstrated how the model implies that intergroup polarization can become less likely if intergroup attitudes change under social influence; and how more complex patterns of intergroup relations emerge. Especially, a renegade minority (outgroup-lovers) can have a key role in avoiding mutually negative intergroup relations and even elicit attitude reversal, resulting in a majority of individuals developing a negative attitude towards their in-group and a positive one of the outgroup. Interpretations of these theoretical results and directions for future research are further discussed.Comment: 33 pages, 21 figures, Paper presented at ODCD 2017. Interdisciplinary Workshop on Opinion Dynamics and Collective Decision 2017, July 5-7, 2017 @ Jacobs University Bremen, German

Adoption as a Social Marker: Innovation Diffusion with Outgroup Aversion

Social identities are among the key factors driving behavior in complex societies. Signals of social identity are known to influence individual behaviors in the adoption of innovations. Yet the population-level consequences of identity signaling on the diffusion of innovations are largely unknown. Here we use both analytical and agent-based modeling to consider the spread of a beneficial innovation in a structured population in which there exist two groups who are averse to being mistaken for each other. We investigate the dynamics of adoption and consider the role of structural factors such as demographic skew and communication scale on population-level outcomes. We find that outgroup aversion can lead to adoption being delayed or suppressed in one group, and that population-wide underadoption is common. Comparing the two models, we find that differential adoption can arise due to structural constraints on information flow even in the absence of intrinsic between-group differences in adoption rates. Further, we find that patterns of polarization in adoption at both local and global scales depend on the details of demographic organization and the scale of communication. This research has particular relevance to widely beneficial but identity-relevant products and behaviors, such as green technologies, where overall levels of adoption determine the positive benefits that accrue to society at large.Comment: 26 pages, 10 figure

Emergence of polarized ideological opinions in multidimensional topic spaces

Opinion polarization is on the rise, causing concerns for the openness of public debates. Additionally, extreme opinions on different topics often show significant correlations. The dynamics leading to these polarized ideological opinions pose a challenge: How can such correlations emerge, without assuming them a priori in the individual preferences or in a preexisting social structure? Here we propose a simple model that qualitatively reproduces ideological opinion states found in survey data, even between rather unrelated, but sufficiently controversial, topics. Inspired by skew coordinate systems recently proposed in natural language processing models, we solidify these intuitions in a formalism of opinions unfolding in a multidimensional space where topics form a non-orthogonal basis. Opinions evolve according to the social interactions among the agents, which are ruled by homophily: two agents sharing similar opinions are more likely to interact. The model features phase transitions between a global consensus, opinion polarization, and ideological states. Interestingly, the ideological phase emerges by relaxing the assumption of an orthogonal basis of the topic space, i.e. if topics thematically overlap. Furthermore, we analytically and numerically show that these transitions are driven by the controversialness of the topics discussed, the more controversial the topics, the more likely are opinion to be correlated. Our findings shed light upon the mechanisms driving the emergence of ideology in the formation of opinions.Comment: 30 pages, 21 figure

Scientific Polarization

Contemporary societies are often "polarized", in the sense that sub-groups within these societies hold stably opposing beliefs, even when there is a fact of the matter. Extant models of polarization do not capture the idea that some beliefs are true and others false. Here we present a model, based on the network epistemology framework of Bala and Goyal ["Learning from neighbors", \textit{Rev. Econ. Stud.} \textbf{65}(3), 784-811 (1998)], in which polarization emerges even though agents gather evidence about their beliefs, and true belief yields a pay-off advantage. The key mechanism that generates polarization involves treating evidence generated by other agents as uncertain when their beliefs are relatively different from one's own.Comment: 22 pages, 5 figures, author final versio

Adherence to Misinformation on Social Media Through Socio-Cognitive and Group-Based Processes

Previous work suggests that people's preference for different kinds of information depends on more than just accuracy. This could happen because the messages contained within different pieces of information may either be well-liked or repulsive. Whereas factual information must often convey uncomfortable truths, misinformation can have little regard for veracity and leverage psychological processes which increase its attractiveness and proliferation on social media. In this review, we argue that when misinformation proliferates, this happens because the social media environment enables adherence to misinformation by reducing, rather than increasing, the psychological cost of doing so. We cover how attention may often be shifted away from accuracy and towards other goals, how social and individual cognition is affected by misinformation and the cases under which debunking it is most effective, and how the formation of online groups affects information consumption patterns, often leading to more polarization and radicalization. Throughout, we make the case that polarization and misinformation adherence are closely tied. We identify ways in which the psychological cost of adhering to misinformation can be increased when designing anti-misinformation interventions or resilient affordances, and we outline open research questions that the CSCW community can take up in further understanding this cost

The Role of Scientific Evidence in Canada\u27s West Coast Energy Conflicts

With salience, credibility, and legitimacy as organizing themes, we investigated how opposing communities engaged with scientific information for two contentious proposed energy projects in western Canada, and how their perceptions of science influenced its use in decision-making. The Trans Mountain pipeline expansion, to carry diluted bitumen from northern Alberta’s oil sands to tankers on British Columbia’s (BC) south coast, was expected to adversely impact biodiversity and contribute to climate change. The Bute Inlet hydroelectric project, a large renewable energy project planned for BC’s Central Coast, was anticipated to impact biodiversity but was largely seen as climate-friendly. Based on surveys and interviews with 68 participants who had made one or more personal or professional decisions pertaining to the projects, we discovered that values, cultural cognition, and media effects permeated all aspects of using scientific evidence—from commissioning scientific research to selecting, assessing, and weighing it with other forms of information. As a result, science was developed and used to support positions rather than to inform decisions. We discuss ways to improve the use of science in environmental assessments and other planning and development processes where engaged communities are divided by oppositional positions. We hope this research will lead to community-university partnerships that identify broadly salient, credible, and legitimate sources of information about energy and climate issues, and foster knowledge mobilization across conflict divides

Opinion dynamics: models, extensions and external effects

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. [...]Comment: 42 pages, 6 figure

Novel Multidimensional Models of Opinion Dynamics in Social Networks

Unlike many complex networks studied in the literature, social networks rarely exhibit unanimous behavior, or consensus. This requires a development of mathematical models that are sufficiently simple to be examined and capture, at the same time, the complex behavior of real social groups, where opinions and actions related to them may form clusters of different size. One such model, proposed by Friedkin and Johnsen, extends the idea of conventional consensus algorithm (also referred to as the iterative opinion pooling) to take into account the actors' prejudices, caused by some exogenous factors and leading to disagreement in the final opinions. In this paper, we offer a novel multidimensional extension, describing the evolution of the agents' opinions on several topics. Unlike the existing models, these topics are interdependent, and hence the opinions being formed on these topics are also mutually dependent. We rigorous examine stability properties of the proposed model, in particular, convergence of the agents' opinions. Although our model assumes synchronous communication among the agents, we show that the same final opinions may be reached "on average" via asynchronous gossip-based protocols.Comment: Accepted by IEEE Transaction on Automatic Control (to be published in May 2017