Expressive Survey Responding: A Closer Look at the Evidence and Its Implications for American Democracy

Concerns about public opinion-based threats to American democracy are often tied to evidence of partisan bias in factual perceptions. However, influential work on expressive survey responding suggests that many apparent instances of such bias result from respondents insincerely reporting politically congenial views in order to gain expressive psychological benefits. Importantly, these findings have been interpreted as “good news for democracy” because partisans who knowingly report incorrect beliefs in surveys can act on their correct beliefs in the real world. We presently synthesize evidence and commentary on this matter, drawing two conclusions. First, evidence for insincere expressive responding on divisive political matters is limited and ambiguous. Second, when experimental manipulations in surveys reduce reports of politically congenial factual beliefs, this is often because such reported beliefs serve as flexible and interchangeable ways of justifying the largely stable allegiances that guide political behavior. So when circumstances render it costly to endorse a partisan belief, assessments of that belief become less diagnostic of the political predispositions that matter most, not more diagnostic of sincere views that will override partisan commitments. The expressive value of acting on political commitments should be viewed as a central feature of the American political context rather than a methodological artifact of surveys.

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.

Calculated Discrimination: Exposing Racial Gerrymandering Using Computational Methods

In Rucho v. Common Cause, the Supreme Court held that challenges to partisan gerrymanders presented a nonjusticiable political question. This decision threatened to discard decades of work by political scientists and other experts, who had developed a myriad of techniques designed to help the courts objectively and unambiguously identify excessively partisan district maps. Simulated redistricting promised to be one of the most effective of these techniques. Simulated redistricting algorithms are computer programs capable of generating thousands of election-district maps, each of which conforms to a set of permissible criteria determined by the relevant state legislature. By measuring the partisan lean of both the automatically generated maps and the map put forth by the state legislature, a court could determine how much of this partisan bias was attributable to the deliberate actions of the legislature, rather than the natural distribution of the state’s population.Rucho ended partisan gerrymandering challenges brought under the U.S. Constitution—but it need not close the book on simulated redistricting. Although originally developed to combat partisan gerrymanders, simulated redistricting algorithms can be repurposed to help courts identify intentional racial gerrymanders. Instead of measuring the partisan bias of automatically generated maps, these programs can gauge improper racial considerations evident in the legislature’s plan and demonstrate the discriminatory intent that produced such an outcome. As long as the redistricting process remains in the hands of state legislatures, there is a threat that constitutionally impermissible considerations will be employed when drawing district plans. Simulated redistricting provides a powerful tool with which courts can detect a hidden unconstitutional motive in the redistricting process.

Interpersonal factors and mental well-being are associated with accuracy in judging the veracity of political news

Little is known about whether individual differences in interpersonal behaviors and mental health (broadly defined) are associated with vulnerability to political misinformation. Three days before the 2020 U.S. presidential election, 477 participants guessed the veracity of true and false political statements. Various interpersonal factors (e.g. high prosociality and a need to belong) and mental health risk factors (e.g. high depressive symptoms and low eudaimonic well-being) were highly associated with the tendency to believe that most headlines were true. However, low prosociality, low negative affect, and high eduaimonic well-being were associated with assessing news with a partisan bias. To reduce the chances of overfitting, out-of-sample validation was used to understand the combination of factors which best predicted accuracy in judging political statements. Including measures of a.) interpersonal behaviors, b.) state affect, and c.) eudaimonic well-being in a model, explained more than 50% of the variance for both true and false statements.

Directional motives and different priors are observationally equivalent

Many experimental and observational studies use the way that subjects respond to information as evidence that partisan bias or directional motives influence (or do not influence) political beliefs. For a natural and tractable formulation belief formation with both accuracy and directional motives, this is not possible. Any subject influenced by directional motives has a "Fully Bayesian Equivalent" with identical beliefs upon observing any signal. As a result, comparing how individuals or groups with different partisanship or priors respond to information has no diagnostic value in detecting motivated reasoning, even in a multivariate or dynamic setting. Conversely, providing a ``Bayesian rationalization'' consistent with a pattern of updating is not meaningful evidence for a lack of directional motives. These results have theoretical implications for the convergence of beliefs among those with directional motives and practical implications for empirical studies that aim to detect directional motives.

A Signal Detection Approach to Understanding the Identification of Fake News

Researchers across many disciplines seek to understand how misinformation spreads with a view toward limiting its impact. One important question in this research is how people determine whether a given piece of news is real or fake. In the current article, we discuss the value of signal detection theory (SDT) in disentangling two distinct aspects in the identification of fake news: (a) ability to accurately distinguish between real news and fake news and (b) response biases to judge news as real or fake regardless of news veracity. The value of SDT for understanding the determinants of fake-news beliefs is illustrated with reanalyses of existing data sets, providing more nuanced insights into how partisan bias, cognitive reflection, and prior exposure influence the identification of fake news. Implications of SDT for the use of source-related information in the identification of fake news, interventions to improve people’s skills in detecting fake news, and the debunking of misinformation are discussed.

The Trump Era Legacy of Partisanism

What did Trump’s four years do to our mass politics? Partisanism—a blind, often unyielding loyalty to one’s own party—has come to define much of our political discourse, very much to the detriment of the American polity. Both the literature and the data on party evaluations confirm that people are behaving in ways that display not just consistent polarization but a deeper level of partisan bias, despite their lack of ideological consistency. Political science should respond to these developments with increased focus on the negative aspects of partisanship that can lead to this form of partisanism so dangerously exhibited in the Capitol riot, among other events, as well as a thoughtful classroom critique of these habits.