Preventing extreme polarization of political attitudes

Democrats and Republicans choose solar panels in very similar ways

Introduction

Appealing to individuals' social identity is a powerful form of social influence, capable of changing the way people process information, the information they think about, and how they evaluate other individuals. The purpose of this study is to explore the idea that Democrat and Republican environmental norms may impact the attributes and strategies partisans use when choosing whether to have solar panels on a house.

Methods

An online study with N = 363 participants was conducted to examine these possible effects through multi-attribute decision making, applying predefined decision process models to participant behavior to test which attribute-based models best describe participants' decision making. A choice task was combined with an experimental manipulation of political affiliation salience to examine whether the norms of political groups would have influence on decision behavior.

Results

Results of the study show remarkable similarities between political parties in their strategies for choosing solar panels. Members of both political parties appeared to use similar strategies and similar attributes for the formation of their decisions.

Discussion

Recommendations are made that science communicators and policy makers avoid polarizing language so as not to create unnecessary polarization where ideological gaps may not currently exist.

Timing decisions as the next frontier for collective intelligence

The past decade has witnessed a growing interest in collective decision making, particularly the idea that groups can make more accurate decisions compared with individuals. However, nearly all research to date has focused on spatial decisions (e.g., food patches). Here, we highlight the equally important, but severely understudied, realm of temporal collective decision making (i.e., decisions about when to perform an action). We illustrate differences between temporal and spatial decisions, including the irreversibility of time, cost asymmetries, the speed-accuracy tradeoff, and game theoretic dynamics. Given these fundamental differences, temporal collective decision making likely requires different mechanisms to generate collective intelligence. Research focused on temporal decisions should lead to an expanded understanding of the adaptiveness and constraints of living in groups.

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.

The effect of non-verbal mimicry on evaluations in interactions with cognitively (dis)similar individuals

Non-verbal mimicry (i.e., being posturally similar by copying another person's body language) has been shown to increase evaluations of the mimicker. Concurrently, extensive research in social psychology has demonstrated a negative effect on interpersonal evaluations when one perceives others as cognitively dissimilar, often resulting in interpersonal conflicts. Across two experiments (Experiment 1: N = 159, Experiment 2: N = 144), we tested our hypotheses that mimicry, compared with no mimicry, will make mimickers come across as more likable and competent regardless of whether they were perceived as cognitively dissimilar or not (Experiment 1) and regardless of the extent to which they were perceived as cognitively dissimilar (Experiment 2). Broadly, we found support for our hypotheses, and via mediation sensitivity analyses, we found that the effect of mimicry, at least for likability, was mediated by participants' perceived personal similarity to the mimicker. Non-verbal mimicry may thus be one way of alleviating interpersonal conflicts via increasing perceptions of personal similarity regardless of initial cognitive dissimilarity.

Policy education in a research-focused doctoral nursing program: Power as knowing participation in change

Nurses have moral obligations incurred by membership in the profession to participate knowingly in health policy advocacy. Many barriers have historically hindered nurses from realizing their potential to advance health policy. The contemporary political context sets additional challenges to policy work due to polarization and conflict. Nursing education can help nurses recognize their role in advancing health through political advocacy in a manner that is consistent with disciplinary knowledge and ethical responsibilities. In this paper, the authors describe an exemplar of Elizabeth Barrett's "Power as Knowing Participation in Change" theory as a disciplinary lens within a doctoral nursing health policy course. Barrett (radically) emphasizes "power as freedom" instead of "power as control." This approach is congruent with nursing disciplinary values and enhances awareness of personal freedom and building collaborative relationships in the policy process. The theory was used in concert with other traditional policy content and frameworks from nursing and other disciplines. We discuss the role of nursing ethics viewed as professional responsibility for policy action, an overview of Barrett's theory, and the design of the course. Four student reflections on how the course influenced their thinking about policy advocacy are included. While not specific to policymaking, Barrett's theory provides a disciplinary grounding to increase students' awareness of freedom and choices in political advocacy participation. Our experience suggests that Barrett's work can be fruitful for enhancing nurses' awareness of choices to participate in change across settings.

Success-driven opinion formation determines social tensions

Polarization is common in politics and public opinion. It is believed to be shaped by media as well as ideologies, and often incited by misinformation. However, little is known about the microscopic dynamics behind polarization and the resulting social tensions. By coupling opinion formation with the strategy selection in different social dilemmas, we reveal how success at an individual level transforms to global consensus or lack thereof. When defection carries with it the fear of punishment in the absence of greed, as in the stag-hunt game, opinion fragmentation is the smallest. Conversely, if defection promises a higher payoff and also evokes greed, like in the prisoner's dilemma and snowdrift game, consensus is more difficult to attain. Our research thus challenges the top-down narrative of social tensions, showing they might originate from fundamental principles at individual level, like the desire to prevail in pairwise evolutionary comparisons.

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.

The roots of polarization in the individual reward system

Polarization raises concerns for democracy and society, which have expanded in the internet era where (mis)information has become ubiquitous, its transmission faster than ever, and the freedom and means of opinion expressions are expanding. The origin of polarization however remains unclear, with multiple social and emotional factors and individual reasoning biases likely to explain its current forms. In the present work, we adopt a principled approach and show that polarization tendencies can take root in biased reward processing of new information in favour of choice confirmatory evidence. Through agent-based simulations, we show that confirmation bias in individual learning is an independent mechanism and could be sufficient for creating polarization at group level independently of any additional assumptions about the opinions themselves, a priori beliefs about them, information transmission mechanisms or the structure of social relationship between individuals. This generative process can interact with polarization mechanisms described elsewhere, but constitutes an entrenched biological tendency that helps explain the extraordinary resilience of polarization against mitigating efforts such as dramatic informational change in the environment.

On the robustness of democratic electoral processes to computational propaganda

There is growing evidence of systematic attempts to influence democratic elections by controlled and digitally organized dissemination of fake news. This raises the question of the intrinsic robustness of democratic electoral processes against external influences. Particularly interesting is to identify the social characteristics of a voter population that renders it more resilient against opinion manipulation. Equally important is to determine which of the existing democratic electoral systems is more robust to external influences. Here we construct a mathematical electoral model to address these two questions. We find that, not unexpectedly, biased electorates with clear-cut elections are overall quite resilient against opinion manipulations, because inverting the election outcome requires to change the opinion of many voters. More interesting are unbiased or weakly biased electorates with close elections. We find that such populations are more resilient against opinion manipulations (i) if they are less polarized and (ii) when voters interact more with each other, regardless of their opinion differences, and that (iii) electoral systems based on proportional representation are generally the most robust. Our model qualitatively captures the volatility of the US House of Representatives elections. We take this as a solid validation of our approach.

Affinity, value homophily, and opinion dynamics: The coevolution between affinity and opinion

We propose two analytical relationships between affinity and opinion change. The first one focuses on value homophily, while the second one incorporates affinity in opinion dynamics. Three analytical test models are derived based on these relationships: the value homophily model, the temporal evolution of opinion summation, and the evolution of opinion difference between two individuals. We test these models using data from a previous experiment, and the results demonstrate their validity.

Reputations for treatment of outgroup members can prevent the emergence of political segregation in cooperative networks

Reputation systems promote cooperation and tie formation in social networks. But how reputations affect cooperation and the evolution of networks is less clear when societies are characterized by fundamental, identity-based, social divisions like those centered on politics in the contemporary U.S. Using a large web-based experiment with participants (N = 1073) embedded in networks where each tie represents the opportunity to play a dyadic iterated prisoners' dilemma, we investigate how cooperation and network segregation varies with whether and how reputation systems track behavior toward members of the opposing political party (outgroup members). As predicted, when participants know others' political affiliation, early cooperation patterns show ingroup favoritism. As a result, networks become segregated based on politics. However, such ingroup favoritism and network-level political segregation is reduced in conditions in which participants know how others behave towards participants from both their own party and participants from the other party. These findings have implications for our understanding of reputation systems in polarized contexts.

Mathematical modeling of disinformation and effectiveness of mitigation policies

Disinformation is spread to manipulate public opinion for malicious purposes. Mathematical modeling was used to examine and optimize several strategies for combating disinformation-content moderation, education, and counter-campaigns. We implemented these strategies in a modified binary agreement model and investigated their impacts on properties of the tipping point. Social interactions were described by weighted, directed, and heterogeneous networks. Real social network data was examined as well. We find that content moderation achieved by removing randomly selected agents who spread disinformation is comparable to that achieved by removing highly influential agents; removing disinformation anywhere in a network could be an effective way to counter disinformation. An education strategy that increases public skepticism was more effective than one that targets already biased agents. Successful counter-campaign strategies required a substantial population of agents to influence other agents to oppose disinformation. These results can be used to inform choices of effective strategies for combating disinformation.

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.

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study

World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with "neighbors" (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics.

Exploring the entropic nature of political polarization through its formulation as a isolated thermodynamic system

Political polarization has become an alarming trend observed in various countries. In the effort to produce more consistent simulations of the process, insights from the foundations of physics are adopted. The work presented here looks at a simple model of political polarization amongst agents which influence their immediate locality and how a entropy trace of the political discourse can be produced. From this model an isolated system representation can be formulated in respect to the changes in the entropy values across all variables of the system over simulation time. It is shown that a constant entropy value for the system can be calculated so that as the agents coalesce their opinions, the entropy trace in regards to political engagements decreases as the entropy value across non-political engagements increase. This relies upon an intrinsic constraint upon agents imposing a fixed number of activities per time point. As a result the simulation respects the second law of thermodynamics and provides insight into political polarization as a basin of entropy within an isolated system without making assumptions about external activities.

Ideological polarization during a pandemic: Tracking the alignment of attitudes toward COVID containment policies and left-right self-identification

Research on opinion polarization has focused on growing divides in positions toward political issues between the more politically and ideologically engaged parts of the population. However, it is fundamentally difficult to track the alignment process between ideological group identity and issue positions because classically controversial political issues are already strongly associated with ideological or partisan identity. This study uses the COVID pandemic as an unique opportunity to investigate polarizing trends in the population. Pandemic management policies were not a politicized issue before COVID, but became strongly contested after governments all across the world initiated policies to contain the pandemic. We use data from the Austrian Corona Panel Project (ACPP) to track trajectories in attitudes toward current COVID measures over the course of more than a year of the pandemic. We differentiate individuals by their ideological self-identity as measured by left-right self-placement. Results suggest that all ideological groups viewed the containment measures as similarly appropriate in the very beginning. However, already in the first weeks, individuals who identify as right-wing increasingly viewed the policies as too extreme, whereas centrists and left-wing identifiers viewed them as appropriate. Opinion differences between left-wing and right-wing identifiers solidified over the course of the pandemic, while centrists fluctuated between left and right self-identifiers. However, at the end of our observation period, there are signs of convergence between all groups. We discuss these findings from the perspective of theoretical models of opinion polarization and suggest that polarization dynamics are likely to stop when the political context (salience of certain issues and concrete material threats) changes.

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.

Statistical Mechanics of Political Polarization

Rapidly increasing political polarization threatens democracies around the world. Scholars from several disciplines are assessing and modeling polarization antecedents, processes, and consequences. Social systems are complex and networked. Their constant shifting hinders attempts to trace causes of observed trends, predict their consequences, or mitigate them. We propose an equivalent-neighbor model of polarization dynamics. Using statistical physics techniques, we generate anticipatory scenarios and examine whether leadership and/or external events alleviate or exacerbate polarization. We consider three highly polarized USA groups: Democrats, Republicans, and Independents. We assume that in each group, each individual has a political stance s ranging between left and right. We quantify the noise in this system as a "social temperature" T. Using energy E, we describe individuals' interactions in time within their own group and with individuals of the other groups. It depends on the stance s as well as on three intra-group and six inter-group coupling parameters. We compute the probability distributions of stances at any time using the Boltzmann probability weight exp(-E/T). We generate average group-stance scenarios in time and explore whether concerted interventions or unexpected shocks can alter them. The results inform on the perils of continuing the current polarization trends, as well as on possibilities of changing course.

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.

Empirical social triad statistics can be explained with dyadic homophylic interactions

Social stability is often associated with triangular interactions between people. Various possible social triangles appear in peculiar ratios. The triangles “The friend of my friend is my friend” and “The enemy of my friend is my enemy” are strongly overrepresented, which plays an important role for social balance. A standard explanation for these characteristic triangle fractions is that people consider triadic information before forming social relations. This assumption often contradicts everyday experience. We propose an explanation of the observed overrepresentations without individuals having to consider triangles. A society where individuals minimize their social stress self-organizes toward the empirically observed triangular structures. We demonstrate this with data from a society of computer game players, where triangle formation can be directly observed.

The remarkable robustness of many social systems has been associated with a peculiar triangular structure in the underlying social networks. Triples of people that have three positive relations (e.g., friendship) between each other are strongly overrepresented. Triples with two negative relations (e.g., enmity) and one positive relation are also overrepresented, and triples with one or three negative relations are drastically suppressed. For almost a century, the mechanism behind these very specific (“balanced”) triad statistics remained elusive. Here, we propose a simple realistic adaptive network model, where agents tend to minimize social tension that arises from dyadic interactions. Both opinions of agents and their signed links (positive or negative relations) are updated in the dynamics. The key aspect of the model resides in the fact that agents only need information about their local neighbors in the network and do not require (often unrealistic) higher-order network information for their relation and opinion updates. We demonstrate the quality of the model on detailed temporal relation data of a society of thousands of players of a massive multiplayer online game where we can observe triangle formation directly. It not only successfully predicts the distribution of triangle types but also explains empirical group size distributions, which are essential for social cohesion. We discuss the details of the phase diagrams behind the model and their parameter dependence, and we comment on to what extent the results might apply universally in societies.

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.

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.

Segregation and clustering of preferences erode socially beneficial coordination

Polarization on various issues has increased in many Western democracies over the last decades, leading to divergent beliefs, preferences, and behaviors within societies. We develop a model to investigate the effects of polarization on the likelihood that a society will coordinate on a welfare-improving action in a context in which collective benefits are acquired only if enough individuals take that action. We examine the impacts of different manifestations of polarization: heterogeneity of preferences, segregation of the social network, and the interaction between the two. In this context, heterogeneity captures differential perceived benefits from coordinating, which can lead to different intentions and sensitivity regarding the intentions of others. Segregation of the social network can create a bottleneck in information flows about others' preferences, as individuals may base their decisions only on their close neighbors. Additionally, heterogeneous preferences can be evenly distributed in the population or clustered in the local network, respectively reflecting or systematically departing from the views of the broader society. The model predicts that heterogeneity of preferences alone is innocuous and it can even be beneficial, while segregation can hamper coordination, mainly when local networks distort the distribution of valuations. We base these results on a multimethod approach including an online group experiment with 750 individuals. We randomize the range of valuations associated with different choice options and the information respondents have about others. The experimental results reinforce the idea that, even in a situation in which all could stand to gain from coordination, polarization can impede social progress.