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As the supply chain shortage continues, many truck drivers across the country find themselves disturbed with both their employers' administrative decisions in response to pressures from the federal government and their working conditions. In the 2019 case, New Prime v. Oliveira, the U.S. Supreme Court declined to compel arbitration of wage and hour class action lawsuits brought by an interstate trucker and independent contractor. The Court unanimously ruled that the exemption language in the Federal Arbitration Act ("FAA"), which includes "seamen, railroad employees, or any other class of workers engaged in foreign or interstate commerce," also includes owner-operators (those who own their own trucking business). The Court ruled that the phrase "contracts of employment" was to be interpreted as "agreement to perform," and found that independent contractors are to be covered by the FAA, similar to employees. As a result of this groundbreaking case, employers are not able to bind their truck driving employees or independent contractors to arbitration agreements, constituting the first opportunity for equivalent treatment among the two groups. This post was originally published on the Cardozo Journal of Conflict Resolution website on March 7, 2022. The original post can be accessed via the Archived Link button above.

Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme—and less accurate—when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains.

Vital scientific communications are frequently misinterpreted by the lay public as a result of motivated reasoning, where people misconstrue data to fit their political and psychological biases. In the case of climate change, some people have been found to systematically misinterpret climate data in ways that conflict with the intended message of climate scientists. While prior studies have attempted to reduce motivated reasoning through bipartisan communication networks, these networks have also been found to exacerbate bias. Popular theories hold that bipartisan networks amplify bias by exposing people to opposing beliefs. These theories are in tension with collective intelligence research, which shows that exchanging beliefs in social networks can facilitate social learning, thereby improving individual and group judgments. However, prior experiments in collective intelligence have relied almost exclusively on neutral questions that do not engage motivated reasoning. Using Amazon's Mechanical Turk, we conducted an online experiment to test how bipartisan social networks can influence subjects' interpretation of climate communications from NASA. Here, we show that exposure to opposing beliefs in structured bipartisan social networks substantially improved the accuracy of judgments among both conservatives and liberals, eliminating belief polarization. However, we also find that social learning can be reduced, and belief polarization maintained, as a result of partisan priming. We find that increasing the salience of partisanship during communication, both through exposure to the logos of political parties and through exposure to the political identities of network peers, can significantly reduce social learning.