The nonlinear feedback dynamics of asymmetric political polarization

Analogies for modeling belief dynamics

Belief dynamics has an important role in shaping our responses to natural and societal phenomena, ranging from climate change and pandemics to immigration and conflicts. Researchers often base their models of belief dynamics on analogies to other systems and processes, such as epidemics or ferromagnetism. Similar to other analogies, analogies for belief dynamics can help scientists notice and study properties of belief systems that they would not have noticed otherwise (conceptual mileage). However, forgetting the origins of an analogy may lead to some less appropriate inferences about belief dynamics (conceptual baggage). Here, we review various analogies for modeling belief dynamics, discuss their mileage and baggage, and offer recommendations for using analogies in model development.

Unraveling polarization: insights into individual and collective dynamics

Polarization poses a critical threat to the stability of nations around the world, as it impacts climate change, populism, democracy, and global health. This perspective examines the conceptual understanding, measurement challenges, and potential interventions for polarization. Our analysis highlights the distinction and interactions between the individual and collective levels of polarization, conceptually, methodologically, and in terms of interventions. We conclude by pointing out future directions for understanding polarization and highlighting the interrelations between polarization and other social phenomena.

Polarization and the Psychology of Collectives

Achieving global sustainability in the face of climate change, pandemics, and other global systemic threats will require collective intelligence and collective action beyond what we are currently experiencing. Increasing polarization within nations and populist trends that undercut international cooperation make the problem even harder. Allegiance within groups is often strengthened because of conflict among groups, leading to a form of polarization termed "affective." Hope for addressing these global problems will require recognition of the commonality in threats facing all groups collective intelligence that integrates relevant inputs from all sources but fights misinformation and coordinated, cooperative collective action. Elinor Ostrom's notion of polycentric governance, involving centers of decision-making from the local to the global in a complex interacting framework, may provide a possible pathway to achieve these goals.

Modelling opinion dynamics under the impact of influencer and media strategies

Digital communication has made the public discourse considerably more complex, and new actors and strategies have emerged as a result of this seismic shift. Aside from the often-studied interactions among individuals during opinion formation, which have been facilitated on a large scale by social media platforms, the changing role of traditional media and the emerging role of "influencers" are not well understood, and the implications of their engagement strategies arising from the incentive structure of the attention economy even less so. Here we propose a novel framework for opinion dynamics that can accommodate various versions of opinion dynamics as well as account for different roles, namely that of individuals, media and influencers, who change their own opinion positions on different time scales. Numerical simulations of instances of this framework show the importance of their relative influence in creating qualitatively different opinion formation dynamics: with influencers, fragmented but short-lived clusters emerge, which are then counteracted by more stable media positions. The framework allows for mean-field approximations by partial differential equations, which reproduce those dynamics and allow for efficient large-scale simulations when the number of individuals is large. Based on the mean-field approximations, we can study how strategies of influencers to gain more followers can influence the overall opinion distribution. We show that moving towards extreme positions can be a beneficial strategy for influencers to gain followers. Finally, our framework allows us to demonstrate that optimal control strategies allow other influencers or media to counteract such attempts and prevent further fragmentation of the opinion landscape. Our modelling framework contributes to a more flexible modelling approach in opinion dynamics and a better understanding of the different roles and strategies in the increasingly complex information ecosystem.

Opinion disparity in hypergraphs with community structure

The division of a social group into subgroups with opposing opinions, which we refer to as opinion disparity, is a prevalent phenomenon in society. This phenomenon has been modeled by including mechanisms such as opinion homophily, bounded confidence interactions, and social reinforcement mechanisms. In this paper, we study a complementary mechanism for the formation of opinion disparity based on higher-order interactions, i.e., simultaneous interactions between multiple agents. We present an extension of the planted partition model for uniform hypergraphs as a simple model of community structure, and we consider the hypergraph Susceptible-Infected-Susceptible (SIS) model on a hypergraph with two communities where the binary ideology can spread via links (pairwise interactions) and triangles (three-way interactions). We approximate this contagion process with a mean-field model and find that for strong enough community structure, the two communities can hold very different average opinions. We determine the regimes of structural and infectious parameters for which this opinion disparity can exist, and we find that the existence of these disparities is much more sensitive to the triangle community structure than to the link community structure. We show that the existence and type of opinion disparities are extremely sensitive to differences in the sizes of the two communities.

The attitudinal space framework: Embracing the multidimensionality of attitudinal diversity

Attitude polarization describes an increasing attitude difference between groups and is increasingly recognized as a multidimensional phenomenon. However, a unified framework to study polarization across multiple dimensions is lacking. We introduce the attitudinal space framework (ASF) to fully quantify attitudinal diversity. We highlight two key measures-attitudinal extremization and attitudinal dispersion-to quantify across- and within-group attitudinal patterns. First, we show that affective polarization in the US electorate is weaker than previously thought based on mean differences alone: in both Democrat and Republican partisans, attitudinal dispersion increased between 1988 and 2008. Second, we examined attitudes toward wolves in Germany. Despite attitude differences between regions with and without wolves, we did not find differences in attitudinal extremization or dispersion, suggesting only weak attitude polarization. These results illustrate how the ASF is applicable to a wide range of social systems and offers an important avenue to understanding societal transformations.

Humans strategically avoid connecting to others who agree and avert the emergence of network polarization in a coordination task

Clusters of like-minded individuals can impede consensus in group decision-making. We implemented an online color coordination task to investigate whether control over communication links creates clusters impeding group consensus. In 244 6-member networks, individuals were incentivized to reach a consensus by agreeing on a color, but had conflicting incentives for which color to choose. We varied (1) if communication links were static, changed randomly over time, or were player-controlled; (2) whether links determined who was observed or addressed; and (3) whether a majority existed or equally many individuals preferred each color. We found that individuals preferentially selected links to previously unobserved and disagreeing others, avoiding links with agreeing others. This prevented cluster formation, sped up consensus formation rather than impeding it, and increased the probability that the group agreed on the majority incentive. Overall, participants with a consensus goal avoided clusters by applying strategies that resolved uncertainty about others.

Political network composition predicts vaccination attitudes

Political polarization is growing rapidly in the United States and has been linked to politicized public health issues including vaccination. Political homogeneity among one's interpersonal relationships may predict polarization levels and partisan bias. In this study, we analyzed if political network structure predicted partisan beliefs about the COVID-19 vaccine, beliefs about vaccines in general, and COVID-19 vaccine uptake. Personal networks were measured by whom the respondent discussed "important matters" with to obtain a list of individuals who are close to the respondent. The number of associates listed who share the political identity or vaccine status with the respondent was calculated as a measure of homogeneity. We find that having more Republicans and unvaccinated individuals in one's network predicted lower vaccine confidence whereas having more Democrats and vaccinated individuals in one's network predicted higher vaccine confidence. Exploratory network analyses revealed that non-kin others are especially impactful on vaccine attitudes when those network connections are also Republican and unvaccinated.

Link updating strategies influence consensus decisions as a function of the direction of communication

Consensus decision-making in social groups strongly depends on communication links that determine to whom individuals send, and from whom they receive, information. Here, we ask how consensus decisions are affected by strategic updating of links and how this effect varies with the direction of communication. We quantified the coevolution of link and opinion dynamics in a large population with binary opinions using mean-field numerical simulations of two voter-like models of opinion dynamics: an incoming model (IM) (where individuals choose who to receive opinions from) and an outgoing model (OM) (where individuals choose who to send opinions to). We show that individuals can bias group-level outcomes in their favour by breaking disagreeing links while receiving opinions (IM) and retaining disagreeing links while sending opinions (OM). Importantly, these biases can help the population avoid stalemates and achieve consensus. However, the role of disagreement avoidance is diluted in the presence of strong preferences-highly stubborn individuals can shape decisions to favour their preferences, giving rise to non-consensus outcomes. We conclude that collectively changing communication structures can bias consensus decisions, as a function of the strength of preferences and the direction of communication.

The Polarizing Impact of Political Disinformation and Hate Speech: A Cross-country Configural Narrative

Information and communication technologies hold immense potential to enhance our lives and societal well-being. However, digital spaces have also emerged as a fertile ground for fake news campaigns and hate speech, aggravating polarization and posing a threat to societal harmony. Despite the fact that this dark side is acknowledged in the literature, the complexity of polarization as a phenomenon coupled with the socio-technical nature of fake news necessitates a novel approach to unravel its intricacies. In light of this sophistication, the current study employs complexity theory and a configurational approach to investigate the impact of diverse disinformation campaigns and hate speech in polarizing societies across 177 countries through a cross-country investigation. The results demonstrate the definitive role of disinformation and hate speech in polarizing societies. The findings also offer a balanced perspective on internet censorship and social media monitoring as necessary evils to combat the disinformation menace and control polarization, but suggest that such efforts may lend support to a milieu of hate speech that fuels polarization. Implications for theory and practice are discussed.

Wild animals suppress the spread of socially transmitted misinformation

Understanding the mechanisms by which information and misinformation spread through groups of individual actors is essential to the prediction of phenomena ranging from coordinated group behaviors to misinformation epidemics. Transmission of information through groups depends on the rules that individuals use to transform the perceived actions of others into their own behaviors. Because it is often not possible to directly infer decision-making strategies in situ, most studies of behavioral spread assume that individuals make decisions by pooling or averaging the actions or behavioral states of neighbors. However, whether individuals may instead adopt more sophisticated strategies that exploit socially transmitted information, while remaining robust to misinformation, is unknown. Here, we study the relationship between individual decision-making and misinformation spread in groups of wild coral reef fish, where misinformation occurs in the form of false alarms that can spread contagiously through groups. Using automated visual field reconstruction of wild animals, we infer the precise sequences of socially transmitted visual stimuli perceived by individuals during decision-making. Our analysis reveals a feature of decision-making essential for controlling misinformation spread: dynamic adjustments in sensitivity to socially transmitted cues. This form of dynamic gain control can be achieved by a simple and biologically widespread decision-making circuit, and it renders individual behavior robust to natural fluctuations in misinformation exposure.

Deeper Than You Think: Partisanship-Dependent Brain Responses in Early Sensory and Motor Brain Regions

Recent political polarization has illustrated how individuals with opposing political views often experience ongoing events in markedly different ways. In this study, we explored the neural mechanisms underpinning this phenomenon. We conducted fMRI scanning of 34 right- and left-wing participants (45% females) watching political videos (e.g., campaign ads and political speeches) just before the elections in Israel. As expected, we observed significant differences between left- and right-wing participants in their interpretation of the videos' content. Furthermore, neuroimaging results revealed partisanship-dependent differences in activation and synchronization in higher-order regions. Surprisingly, such differences were also revealed in early sensory, motor, and somatosensory regions. We found that the political content synchronized the responses of primary visual and auditory cortices in a partisanship-dependent manner. Moreover, right-wing (and not left-wing) individuals' sensorimotor cortex was involved in processing right-wing (and not left-wing) political content. These differences were pronounced to the extent that we could predict political orientation from the early brain-response alone. Importantly, no such differences were found with respect to neutral content. Therefore, these results uncover more fundamental neural mechanisms underlying processes of political polarization.SIGNIFICANCE STATEMENT The political sphere has become highly polarized in recent years. Would it be possible to identify the neural mechanisms underpinning such processes? In our study, left- and right-wing participants were scanned in fMRI while watching political video clips just before the elections in Israel. We found that political content was potent in synchronizing the brain responses of individuals holding similar views. This was far more pronounced in individuals holding right-wing views. Moreover, partisan-dependent differences in neural responses were identified already in early sensory, somatosensory, and motor regions, and only for political content. These results suggest that individuals' political views shape their neural responses at a very basic level.

Coordination in interpersonal systems

Coordinated group behaviour can result in conflict or social cohesion. Thus having a better understanding of coordination in social groups could help us tackle some of our most challenging social problems. Historically, the most common way to study group behaviour is to break it down into sub-processes, such as cognition and emotion, then ideally manipulate them in a social context in order to predict some behaviour such as liking versus distrusting a target person. This approach has gotten us partway to understanding many important collective behaviours, but I argue that making major changes in the world will require a more integrated approach. In this review, I introduce dynamic systems theory, with a focus on interpersonal systems, where all the processes we typically study in individuals, such as cognition and emotion, become intertwined between social partners over time. I focus on the concept of coordination, defined as a temporal correlation between interacting components of a system (or systems) arising due to coupling between them. Finally, I show how this perspective could be used to guide investigations of social problems such as polarisation.

How digital media drive affective polarization through partisan sorting

Politics has in recent decades entered an era of intense polarization. Explanations have implicated digital media, with the so-called echo chamber remaining a dominant causal hypothesis despite growing challenge by empirical evidence. This paper suggests that this mounting evidence provides not only reason to reject the echo chamber hypothesis but also the foundation for an alternative causal mechanism. To propose such a mechanism, the paper draws on the literatures on affective polarization, digital media, and opinion dynamics. From the affective polarization literature, we follow the move from seeing polarization as diverging issue positions to rooted in sorting: an alignment of differences which is effectively dividing the electorate into two increasingly homogeneous megaparties. To explain the rise in sorting, the paper draws on opinion dynamics and digital media research to present a model which essentially turns the echo chamber on its head: it is not isolation from opposing views that drives polarization but precisely the fact that digital media bring us to interact outside our local bubble. When individuals interact locally, the outcome is a stable plural patchwork of cross-cutting conflicts. By encouraging nonlocal interaction, digital media drive an alignment of conflicts along partisan lines, thus effacing the counterbalancing effects of local heterogeneity. The result is polarization, even if individual interaction leads to convergence. The model thus suggests that digital media polarize through partisan sorting, creating a maelstrom in which more and more identities, beliefs, and cultural preferences become drawn into an all-encompassing societal division.

Social asset or social liability? How partisanship moderates the relationship between social capital and Covid-19 vaccination rates across United States counties

This study investigates the interactive effect of social capital and partisanship on COVID-19 vaccination rates. Using county-level data from the United States (U.S.), we empirically find that social capital is a double-edged sword. Its effect on the vaccination rate depends on the dominant partisanship of the jurisdiction. In more liberal counties, stronger social capital is a social asset that encourages people to seek vaccination and results in a higher vaccination rate. In contrast, in more conservative counties where the Trump-voting rate reaches 73% and beyond, stronger social capital becomes a social liability for public health by reinforcing residents' hesitancy toward or rejection of vaccinations, leading to a lower vaccination rate. This study implies the need for reducing the partisanship salience and investing in bridging and linking social capital in polarized communities.

Computational analysis of 140 years of US political speeches reveals more positive but increasingly polarized framing of immigration

We classify and analyze 200,000 US congressional speeches and 5,000 presidential communications related to immigration from 1880 to the present. Despite the salience of antiimmigration rhetoric today, we find that political speech about immigration is now much more positive on average than in the past, with the shift largely taking place between World War II and the passage of the Immigration and Nationality Act in 1965. However, since the late 1970s, political parties have become increasingly polarized in their expressed attitudes toward immigration, such that Republican speeches today are as negative as the average congressional speech was in the 1920s, an era of strict immigration quotas. Using an approach based on contextual embeddings of text, we find that modern Republicans are significantly more likely to use language that is suggestive of metaphors long associated with immigration, such as "animals" and "cargo," and make greater use of frames like "crime" and "legality." The tone of speeches also differs strongly based on which nationalities are mentioned, with a striking similarity between how Mexican immigrants are framed today and how Chinese immigrants were framed during the era of Chinese exclusion in the late 19th century. Overall, despite more favorable attitudes toward immigrants and the formal elimination of race-based restrictions, nationality is still a major factor in how immigrants are spoken of in Congress.

Universality of political corruption networks

Corruption crimes demand highly coordinated actions among criminal agents to succeed. But research dedicated to corruption networks is still in its infancy and indeed little is known about the properties of these networks. Here we present a comprehensive investigation of corruption networks related to political scandals in Spain and Brazil over nearly three decades. We show that corruption networks of both countries share universal structural and dynamical properties, including similar degree distributions, clustering and assortativity coefficients, modular structure, and a growth process that is marked by the coalescence of network components due to a few recidivist criminals. We propose a simple model that not only reproduces these empirical properties but reveals also that corruption networks operate near a critical recidivism rate below which the network is entirely fragmented and above which it is overly connected. Our research thus indicates that actions focused on decreasing corruption recidivism may substantially mitigate this type of organized crime.

Inequality, identity, and partisanship: How redistribution can stem the tide of mass polarization

The form of political polarization where citizens develop strongly negative attitudes toward out-party members and policies has become increasingly prominent across many democracies. Economic hardship and social inequality, as well as intergroup and racial conflict, have been identified as important contributing factors to this phenomenon known as "affective polarization." Research shows that partisan animosities are exacerbated when these interests and identities become aligned with existing party cleavages. In this paper, we use a model of cultural evolution to study how these forces combine to generate and maintain affective political polarization. We show that economic events can drive both affective polarization and the sorting of group identities along party lines, which, in turn, can magnify the effects of underlying inequality between those groups. But, on a more optimistic note, we show that sufficiently high levels of wealth redistribution through the provision of public goods can counteract this feedback and limit the rise of polarization. We test some of our key theoretical predictions using survey data on intergroup polarization, sorting of racial groups, and affective polarization in the United States over the past 50 y.

A systems framework for remedying dysfunction in US democracy

Democracy often fails to meet its ideals, and these failures may be made worse by electoral institutions. Unwanted outcomes include elite polarization, unresponsive representatives, and the ability of a faction of voters to gain power at the expense of the majority. Various reforms have been proposed to address these problems, but their effectiveness is difficult to predict against a backdrop of complex interactions. Here we outline a path for systems-level modeling to help understand and optimize repairs to US democracy. Following the tradition of engineering and biology, models of systems include mechanisms with dynamical properties that include nonlinearities and amplification (voting rules), positive feedback mechanisms (single-party control, gerrymandering), negative feedback (checks and balances), integration over time (lifetime judicial appointments), and low dimensionality (polarization). To illustrate a systems-level approach, we analyze three emergent phenomena: low dimensionality, elite polarization, and antimajoritarianism in legislatures. In each case, long-standing rules now contribute to undesirable outcomes as a consequence of changes in the political environment. Theoretical understanding at a general level will also help evaluate whether a proposed reform's benefits will materialize and be lasting, especially as conditions change again. In this way, rigorous modeling may not only shape new lines of research but aid in the design of effective and lasting reform.

Polarization, diversity, and democratic robustness

In the Madisonian Constitution, fragmented and overlapping institutions of authority are supposed to manage democracy's innate rivalry, channeling competition to serve the public interest. This system of safeguards makes democracy more robust: capable of withstanding and, if need be, adapting to challenges posed by a changing problem environment. In this essay, I suggest why affective polarization poses a special threat to democratic robustness. While most scholars hypothesize that polarization's dangers are that it leads to bimodality and extremism, I highlight a third hypothesized effect: Polarization reduces interest and information diversity in the political system. To be effective, democracy's safeguards rely upon interest diversity, but Madison took that diversity for granted. Unique among democracy's safeguards, federalism builds in a repository for diversity; its structure enables differences between national- and state-expressed interests, even within the same party. This diversity can be democracy hindering, as the United States' history with racially discriminatory politics painfully makes clear, but it can also serve as a reservoir of interest and information dispersion that could protect democracy by restoring the possibility that cross-cutting cleavages emerge.

Link recommendation algorithms and dynamics of polarization in online social networks

The level of antagonism between political groups has risen in the past years. Supporters of a given party increasingly dislike members of the opposing group and avoid intergroup interactions, leading to homophilic social networks. While new connections offline are driven largely by human decisions, new connections on online social platforms are intermediated by link recommendation algorithms, e.g., "People you may know" or "Whom to follow" suggestions. The long-term impacts of link recommendation in polarization are unclear, particularly as exposure to opposing viewpoints has a dual effect: Connections with out-group members can lead to opinion convergence and prevent group polarization or further separate opinions. Here, we provide a complex adaptive-systems perspective on the effects of link recommendation algorithms. While several models justify polarization through rewiring based on opinion similarity, here we explain it through rewiring grounded in structural similarity-defined as similarity based on network properties. We observe that preferentially establishing links with structurally similar nodes (i.e., sharing many neighbors) results in network topologies that are amenable to opinion polarization. Hence, polarization occurs not because of a desire to shield oneself from disagreeable attitudes but, instead, due to the creation of inadvertent echo chambers. When networks are composed of nodes that react differently to out-group contacts, either converging or polarizing, we find that connecting structurally dissimilar nodes moderates opinions. Overall, our study sheds light on the impacts of social-network algorithms and unveils avenues to steer dynamics of radicalization and polarization in online social networks.

National polarization and international agreements

The network of international environmental agreements (IEAs) has been characterized as a complex adaptive system (CAS) in which the uncoordinated responses of nation states to changes in the conditions addressed by particular agreements may generate seemingly coordinated patterns of behavior at the level of the system. Unfortunately, since the rules governing national responses are ill understood, it is not currently possible to implement a CAS approach. Polarization of both political parties and the electorate has been implicated in a secular decline in national commitment to some IEAs, but the causal mechanisms are not clear. In this paper, we explore the impact of polarization on the rules underpinning national responses. We identify the degree to which responsibility for national decisions is shared across political parties and calculate the electoral cost of party positions as national obligations under an agreement change. We find that polarization typically affects the degree but not the direction of national responses. Whether national commitment to IEAs strengthens or weakens as national obligations increase depends more on the change in national obligations than on polarization per se. Where the rules governing national responses are conditioned by the current political environment, so are the dynamic consequences both for the agreement itself and for the network to which it belongs. Any CAS analysis requires an understanding of such conditioning effects on the rules governing national responses.

The emergence and perils of polarization

We provide commentaries on the papers included in the Dynamics of Political Polarization Special Feature. Baldassarri reads the contribution of the papers in light of the theoretical distinction between ideological partisanship, which is generally rooted in sociodemographic and political cleavages, and affective partisanship, which is, instead, mostly fueled by emotional attachment and repulsion, rather than ideology and material interests. The latter, she argues, is likely to lead to a runaway process and threaten the pluralistic bases of contemporary democracy. Page sees the contribution of the many distinct models in the ensemble as potentially contributing more than the parts. Individual papers identify distinct causes of polarization as well as potential solutions. Viewed collectively, the papers suggest that the multiple causes of polarization may self-reinforce, which suggests that successful interventions would require a variety of efforts. Understanding how to construct such interventions may require larger models with greater realism.

Interindividual cooperation mediated by partisanship complicates Madison's cure for "mischiefs of faction"

Political theorists have long argued that enlarging the political sphere to include a greater diversity of interests would cure the ills of factions in a pluralistic society. While the scope of politics has expanded dramatically over the past 75 y, polarization is markedly worse. Motivated by this paradox, we take a bottom-up approach to explore how partisan individual-level dynamics in a diverse (multidimensional) issue space can shape collective-level factionalization via an emergent dimensionality reduction. We extend a model of cultural evolution grounded in evolutionary game theory, in which individuals accumulate benefits through pairwise interactions and imitate (or learn) the strategies of successful others. The degree of partisanship determines the likelihood of learning from individuals of the opposite party. This approach captures the coupling between individual behavior, partisan-mediated opinion dynamics, and an interaction network that changes endogenously according to the evolving interests of individuals. We find that while expanding the diversity of interests can indeed improve both individual and collective outcomes, increasingly high partisan bias promotes a reduction in issue dimensionality via party-based assortment that leads to increasing polarization. When party bias becomes extreme, it also boosts interindividual cooperation, thereby further entrenching extreme polarization and creating a tug-of-war between individual cooperation and societal cohesion. These dangers of extreme partisanship are highest when individuals' interests and opinions are heavily shaped by peers and there is little independent exploration. Overall, our findings highlight the urgency to study polarization in a coupled, multilevel context.