On modeling the correlates of conspiracy thinking.

While a robust literature on the psychology of conspiracy theories has identified dozens of characteristics correlated with conspiracy theory beliefs, much less attention has been paid to understanding the generalized predisposition towards interpreting events and circumstances as the product of supposed conspiracies. Using a unique national survey of 2015 U.S.

The psychological and political correlates of conspiracy theory beliefs.

Understanding the individual-level characteristics associated with conspiracy theory beliefs is vital to addressing and combatting those beliefs. While researchers have identified numerous psychological and political characteristics associated with conspiracy theory beliefs, the generalizability of those findings is uncertain because they are typically drawn from studies of only a few conspiracy theories. Here, we employ a national survey of 2021 U.

Have beliefs in conspiracy theories increased over time?

The public is convinced that beliefs in conspiracy theories are increasing, and many scholars, journalists, and policymakers agree. Given the associations between conspiracy theories and many non-normative tendencies, lawmakers have called for policies to address these increases. However, little evidence has been provided to demonstrate that beliefs in conspiracy theories have, in fact, increased over time.

The Relationship Between Social Media Use and Beliefs in Conspiracy Theories and Misinformation.

Unlabelled: Numerous studies find associations between social media use and beliefs in conspiracy theories and misinformation. While such findings are often interpreted as evidence that social media causally promotes conspiracy beliefs, we theorize that this relationship is conditional on other individual-level predispositions. Across two studies, we examine the relationship between beliefs in conspiracy theories and media use, finding that individuals who get their news from social media and use social media frequently express more beliefs in some types of conspiracy theories and misinformation.

Efficient Computation of Conditionals in the Dempster-Shafer Belief Theoretic Framework.

The Dempster-Shafer (DS) belief theory constitutes a powerful framework for modeling and reasoning with a wide variety of uncertainties due to its greater expressiveness and flexibility. As in the Bayesian probability theory, the DS theoretic (DST) conditional plays a pivotal role in DST strategies for evidence updating and fusion. However, a major limitation in employing the DST framework in practical implementations is the absence of an efficient and feasible computational framework to overcome the prohibitive computational burden DST operations entail.

Toward Efficient Computation of the Dempster-Shafer Belief Theoretic Conditionals.

Dempster-Shafer (DS) belief theory provides a convenient framework for the development of powerful data fusion engines by allowing for a convenient representation of a wide variety of data imperfections. The recent work on the DS theoretic (DST) conditional approach, which is based on the Fagin-Halpern (FH) DST conditionals, appears to demonstrate the suitability of DS theory for incorporating both soft (generated by human-based sensors) and hard (generated by physics-based sources) evidence into the fusion process. However, the computation of the FH conditionals imposes a significant computational burden.

Transmission rate allocation in multisensor target tracking over a shared network.

In a multisensor target tracking application running on a shared network, at what bit rates should the sensors send their measurements to the tracking fusion center? Clearly, the sensors cannot use arbitrary rates in a shared network, and a standard network rate control algorithm may not provide rates amenable to effective target tracking. For Kalman filter-based multisensor target tracking, we derive a utility function that captures the tracking quality of service as a function of the sensor bit rates. We incorporate this utility function into a network rate resource allocation framework, deriving a distributed rate control algorithm for a shared network that is suitable for current best effort packet networks, such as the Internet.

Effects of molecular siting and adsorbent heterogeneity on the ideality of adsorption equilibria.

The ideal adsorbed solution (IAS) theory is the benchmark for the prediction of mixed-gas adsorption equilibria from pure-component isotherms. In this work, we use atomistic grand canonical Monte Carlo simulations to test the effects of molecular siting and adsorbent energetic heterogeneity on the applicability of the IAS theory. Pure-component isotherms generated by atomistic simulation are used to predict binary isobaric isotherms using the IAS theory.