COVID-19 vaccine sceptics are persuaded by pro-vaccine expert consensus messaging

To further understand how to combat COVID-19 vaccination hesitancy, we examined the effects of pro-vaccine expert consensus messaging on lay attitudes of vaccine safety and intention to vaccinate. We surveyed N = 729 individuals from four countries. Regardless of its content, consensus messaging had an overall small positive effect. Most critically, the direction of the effect varied depending on the baseline attitudes of participants: consensus information improved the attitude of vaccine sceptics and uncertain individuals, while having no effect on vaccine supporters. We also analysed whether the persuasiveness of expert consensus would increase after puncturing an illusion of explanatory depth in individuals. This further manipulation had no direct effect, nor interacted with the type of expert consensus. We conclude that highlighting expert consensus may be a way to increase support toward COVID-19 vaccination in those already hesitant or sceptical with little risk of side-effects.

A Reconstruction Method for Cross-Cut Shredded Documents Based on the Extreme Learning Machine Algorithm

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm's design is to exploit all available information and computationally suggest matches to increase the algorithm's ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios.

Credible Peer-to-Peer Trading with Double-Layer Energy Blockchain Network in Distributed Electricity Markets

Blockchain-based peer-to-peer (P2P) energy trading is one of the most viable solutions to incentivize prosumers in distributed electricity markets. However, P2P energy trading through an open-end blockchain network is not conducive to mutual credit and the privacy protection of stakeholders. Therefore, improving the credibility of P2P energy trading is an urgent problem for distributed electricity markets. In this paper, a novel double-layer energy blockchain network is proposed that stores private trading data separately from publicly available information. This blockchain network is based on optimized cross-chain interoperability technology and fully considers the special attributes of energy trading. Firstly, an optimized ring mapping encryption algorithm is designed to resist malicious nodes. Secondly, a consensus verification subgroup is built according to contract performance, consensus participation and trading enthusiasm. This subgroup verifies the consensus information through the credit-threshold digital signature. Thirdly, an energy trading model is embedded in the blockchain network, featuring dynamic bidding and credit incentives. Finally, the Erenhot distributed electricity market in China is utilized for example analysis, which demonstrates the proposed method could improve the credibility of P2P trading and realize effective supervision.

Distributed moving horizon state estimation for sensor networks with low computation capabilities

This paper focuses on distributed state estimation for sensor network observing a discrete-time linear system. The provided solution is based on a Distributed Moving Horizon Estimation (DMHE) algorithm considering a pre-estimating Luenberger observer in the formulation of the local problem solved by each sensor. This leads to reduce the computation load, while preserving the accuracy of the estimation. Moreover, observability properties of local sensors are used for tuning the weights related to consensus information fusion built on a rank-based condition, in order to improve the convergence of the estimation error. Results obtained by Monte Carlo simulations are provided to compare the performance with existing approaches, in terms of accuracy of the estimations and computation time.

Using a News Article to Convey Climate Science Consensus Information

The current study investigates how people respond to a climate science consensus statement embedded within a news article. Participants ( N = 1,048) were randomly assigned to read a news article about climate change, read the same article with a scientific consensus message included, read a simple consensus statement, or a control condition. Participants in consensus conditions had increased perceptions of scientific agreement compared with those who did not receive consensus information. Moreover, the article was similarly effective as an overt consensus statement. However, neither consensus statement affected other climate change attitudes, suggesting the effect may be limited to consensus perceptions.

A Pandemic of Misbelief: How Beliefs Promote or Undermine COVID-19 Mitigation

Sustained and coordinated social action is needed to combat the spread of the novel coronavirus disease 2019 (COVID-19). Health practitioners and governments around the world have issued recommendations and mandates designed to reduce the transmission of COVID-19 by influencing the social behaviors of the general public. Why and when are some people unwilling to take action to protect themselves and others from the effects of this public health crisis? We find that belief in COVID-19 consensus information (by the self or perceptions of scientists’ beliefs), are consequential predictors of COVID-19 mitigation behaviors. Importantly, support for COVID-19 conspiracy theories predicted decreased, whereas perceived understanding of COVID-19 predicted increased, belief in COVID-19 consensus information. We also implemented an Illusion of Explanatory depth paradigm, an approach to examining knowledge overestimation shown to reduce confidence in one’s understanding of complex phenomena. By requiring participants to elaborate upon COVID-19 conspiracies, we experimentally increased understanding of these theories, which led, in turn, to ironic increases in support for the conspiracy theories and undermined perceived understanding of COVID-19 information for a notable portion of our participants. Together, our results suggest that attention given to COVID-19 conspiracies may be misguided; describing or explaining the existence of COVID-19 conspiracies may ironically increase support for these accounts and undermine knowledge about and willingness to engage in COVID-19 mitigation.

Subset-binding: A novel algorithm to detect paired itemsets from heterogeneous data including biological datasets

The integration of heterogeneous data to infer latent relationships across them and find the factors in the relationship is a challenging task. In this regard, various machine learning techniques have provided novel insights through data integration. However, concerns remain regarding their application to biological datasets because the latent consensus information across all views is often limited to partial components that do not have a significant impact on the mutual agreement across views. Advocating the idea of “subset-binding,” which focuses on finding inter-related attributes in heterogeneous data according to their co-occurrence, this study developed a novel algorithm to perform subset-binding by extending fuzzy association rule mining techniques. Our method could detect genes related to liver toxicity caused by acetaminophen in a data-driven manner; the results are consistent with those reported in the literature. This technology paves the way for a wide range of applications, including biomarker detection and patient stratification.

Status

Moral judgments are often regarded as universally true, whereas judgments of taste are taken to be only true relative to some group or individual. How could such meta-evaluative assessments be acquired? This chapter argues that people use consensus information to arrive at such assessments, and that it is rational to do so. Statistical inference mandates a trade-off between the extent to which a hypothesis fits the data, and the extent to which the hypothesis is flexible in its ability to fit a wider range of data. If almost everyone agrees in their judgments, this provides some reason to endorse a universalist hypothesis, according to which there is a single fact that the majority is tracking. So if almost everyone thinks that a certain action is wrong, the high consensus provides some evidence that it’s a universal truth that this action is wrong. The inference that it’s a universal truth that an action is wrong can also ground the judgment that the action is wrong in a way that is independent of authority. Thus, this might also provide an explanation for the acquisition of the moral/conventional distinction.

Heuristic and systematic processing differentially influence the effects of scientific consensus messaging on perceived scientific consensus

This study investigated the impact of scientific consensus messaging on perceived scientific consensus in terms of heuristic and systematic processing. Japanese undergraduates (N = 226) received a message indicating relatively moderate and high levels of scientific consensus on the safety of foods grown with pesticides and genetically modified (GM) foods. Participants in the presentation-style evaluation condition evaluated the style and manner of providing the message and thereby were encouraged to heuristically process information about scientific consensus in the message. Participants in the content evaluation condition evaluated the message content and therefore could process the information systematically. After evaluating the message, participants’ perceptions of scientific consensus improved. The levels of posteriorly perceived scientific consensus were higher for the presentation-style evaluation condition than for the content evaluation condition. Participants’ initial beliefs about the GM-food safety predicted their posterior perceptions of scientific consensus for the content evaluation condition but not for the presentation-style evaluation condition. These results suggest that the heuristic and systematic processing of scientific consensus information differentially influence the impact of scientific consensus messaging.