COFFEE: consensus single cell-type specific inference for gene regulatory networks.

The inference of gene regulatory networks (GRNs) is crucial to understanding the regulatory mechanisms that govern biological processes. GRNs may be represented as edges in a graph, and hence, it have been inferred computationally for scRNA-seq data. A wisdom of crowds approach to integrate edges from several GRNs to create one composite GRN has demonstrated improved performance when compared with individual algorithm implementations on bulk RNA-seq and microarray data. In an effort to extend this approach to scRNA-seq data, we present COFFEE (COnsensus single cell-type speciFic inFerence for gEnE regulatory networks), a Borda voting-based consensus algorithm that integrates information from 10 established GRN inference methods. We conclude that COFFEE has improved performance across synthetic, curated, and experimental datasets when compared with baseline methods. Additionally, we show that a modified version of COFFEE can be leveraged to improve performance on newer cell-type specific GRN inference methods. Overall, our results demonstrate that consensus-based methods with pertinent modifications continue to be valuable for GRN inference at the single cell level. While COFFEE is benchmarked on 10 algorithms, it is a flexible strategy that can incorporate any set of GRN inference algorithms according to user preference. A Python implementation of COFFEE may be found on GitHub: https://github.com/lodimk2/coffee.

The Effects of the 2020 BLM Protests on Racial Bias in the United States.

The 2020 Black Lives Matter (BLM) protests in response to the murder of George Floyd highlighted the lingering structural inequalities faced by Black people in the United States. In the present research, we investigated whether these protests led to reduced implicit and explicit racial bias among White U.S. Americans. Combining data from Project Implicit, Armed Conflict Location Event Data Project (ACLED), Google Trends, and the American Community survey, we observed rapid drops in implicit and explicit measures of racial bias after the onset of the protests. However, both types of racial bias slowly increased again over time as (attention to) BLM faded. We use directed acyclic graphs to show under which assumptions causal inferences are warranted. We discuss our results in light of situational models of bias, their implications for protest movements, and raise questions about when and how social norms play a role in large-scale attitude change.

Beyond borders: Exploring the mental, emotional, and spiritual significance of Hajj.

For Muslims all across the world, the desire to participate in the religious rites of the Hajj (pilgrimage to Mecca) which stands as one of the five pillars of Islam is a heartfelt longing. It stands for the pinnacle of devotion and spiritual gratification, luring followers to the most sacred city in Islam for a life-changing journey of faith, comradery, and submission to Allah. Muslims hold Mecca in the highest regard; it is a source of endless inspiration and devotion throughout their lives, as seen by their desire to undertake the Hajj and Umrah. The pilgrimage encompasses a series of synchronized rituals and acts of worship, each holding its unique spiritual meaning, and serve as a powerful testament to the universal nature of Islamic teachings. These rituals have a significant impact on Muslims' mental and spiritual well-being. Hajj elicits a spectrum of feelings; creates unity, humility, and thankfulness; and encourages self-reflection as well as personal development. It also instills a sense of spiritual fulfillment. Hajj transcends personal boundaries, strengthening a pilgrim's sense of connection to the larger Muslim community that rejuvenates their hearts and souls toward the teaching of Islam. Hence, it is imperative to explore in depth this transformative journey, illuminating the mental, emotional, and spiritual dimensions that bind Muslims across the globe.

Collective Intelligence-Based Participatory COVID-19 Surveillance in Accra, Ghana: Pilot Mixed Methods Study.

BACKGROUND: Infectious disease surveillance is difficult in many low- and middle-income countries. Information market (IM)-based participatory surveillance is a crowdsourcing method that encourages individuals to actively report health symptoms and observed trends by trading web-based virtual "stocks" with payoffs tied to a future event. OBJECTIVE: This study aims to assess the feasibility and acceptability of a tailored IM surveillance system to monitor population-level COVID-19 outcomes in Accra, Ghana. METHODS: We designed and evaluated a prediction markets IM system from October to December 2021 using a mixed methods study approach. Health care workers and community volunteers aged ≥18 years living in Accra participated in the pilot trading. Participants received 10,000 virtual credits to trade on 12 questions on COVID-19-related outcomes. Payoffs were tied to the cost estimation of new and cumulative cases in the region (Greater Accra) and nationwide (Ghana) at specified future time points. Questions included the number of new COVID-19 cases, the number of people likely to get the COVID-19 vaccination, and the total number of COVID-19 cases in Ghana by the end of the year. Phone credits were awarded based on the tally of virtual credits left and the participant's percentile ranking. Data collected included age, occupation, and trading frequency. In-depth interviews explored the reasons and factors associated with participants' user journey experience, barriers to system use, and willingness to use IM systems in the future. Trading frequency was assessed using trend analysis, and ordinary least squares regression analysis was conducted to determine the factors associated with trading at least once. RESULTS: Of the 105 eligible participants invited, 21 (84%) traded at least once on the platform. Questions estimating the national-level number of COVID-19 cases received 13 to 19 trades, and obtaining COVID-19-related information mainly from television and radio was associated with less likelihood of trading (marginal effect: -0.184). Individuals aged <30 years traded 7.5 times more and earned GH ¢134.1 (US $11.7) more in rewards than those aged >30 years (marginal effect: 0.0135). Implementing the IM surveillance was feasible; all 21 participants who traded found using IM for COVID-19 surveillance acceptable. Active trading by friends with communal discussion and a strong onboarding process facilitated participation. The lack of bidirectional communication on social media and technical difficulties were key barriers. CONCLUSIONS: Using an IM system for disease surveillance is feasible and acceptable in Ghana. This approach shows promise as a cost-effective source of information on disease trends in low- and middle-income countries where surveillance is underdeveloped, but further studies are needed to optimize its use.

CHAI: consensus clustering through similarity matrix integration for cell-type identification.

Several methods have been developed to computationally predict cell-types for single cell RNA sequencing (scRNAseq) data. As methods are developed, a common problem for investigators has been identifying the best method they should apply to their specific use-case. To address this challenge, we present CHAI (consensus Clustering tHrough similArIty matrix integratIon for single cell-type identification), a wisdom of crowds approach for scRNAseq clustering. CHAI presents two competing methods which aggregate the clustering results from seven state-of-the-art clustering methods: CHAI-AvgSim and CHAI-SNF. CHAI-AvgSim and CHAI-SNF demonstrate superior performance across several benchmarking datasets. Furthermore, both CHAI methods outperform the most recent consensus clustering method, SAME-clustering. We demonstrate CHAI's practical use case by identifying a leader tumor cell cluster enriched with CDH3. CHAI provides a platform for multiomic integration, and we demonstrate CHAI-SNF to have improved performance when including spatial transcriptomics data. CHAI overcomes previous limitations by incorporating the most recent and top performing scRNAseq clustering algorithms into the aggregation framework. It is also an intuitive and easily customizable R package where users may add their own clustering methods to the pipeline, or down-select just the ones they want to use for the clustering aggregation. This ensures that as more advanced clustering algorithms are developed, CHAI will remain useful to the community as a generalized framework. CHAI is available as an open source R package on GitHub: https://github.com/lodimk2/chai.

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.

Promoting Erroneous Divergent Opinions Increases the Wisdom of Crowds.

The aggregation of many lay judgments generates surprisingly accurate estimates. This phenomenon, called the "wisdom of crowds," has been demonstrated in domains such as medical decision-making and financial forecasting. Previous research identified two factors driving this effect: the accuracy of individual assessments and the diversity of opinions. Most available strategies to enhance the wisdom of crowds have focused on improving individual accuracy while neglecting the potential of increasing opinion diversity. Here, we study a complementary approach to reduce collective error by promoting erroneous divergent opinions. This strategy proposes to anchor half of the crowd to a small value and the other half to a large value before eliciting and averaging all estimates. Consistent with our mathematical modeling, four experiments (N = 1,362 adults) demonstrated that this method is effective for estimation and forecasting tasks. Beyond the practical implications, these findings offer new theoretical insights into the epistemic value of collective decision-making.

Conventional and frugal methods of estimating COVID-19-related excess deaths and undercount factors.

Across the world, the officially reported number of COVID-19 deaths is likely an undercount. Establishing true mortality is key to improving data transparency and strengthening public health systems to tackle future disease outbreaks. In this study, we estimated excess deaths during the COVID-19 pandemic in the Pune region of India. Excess deaths are defined as the number of additional deaths relative to those expected from pre-COVID-19-pandemic trends. We integrated data from: (a) epidemiological modeling using pre-pandemic all-cause mortality data, (b) discrepancies between media-reported death compensation claims and official reported mortality, and (c) the "wisdom of crowds" public surveying. Our results point to an estimated 14,770 excess deaths [95% CI 9820-22,790] in Pune from March 2020 to December 2021, of which 9093 were officially counted as COVID-19 deaths. We further calculated the undercount factor-the ratio of excess deaths to officially reported COVID-19 deaths. Our results point to an estimated undercount factor of 1.6 [95% CI 1.1-2.5]. Besides providing similar conclusions about excess deaths estimates across different methods, our study demonstrates the utility of frugal methods such as the analysis of death compensation claims and the wisdom of crowds in estimating excess mortality.

Collective Intelligence Increases Diagnostic Accuracy in a General Practice Setting.

BACKGROUND: General practitioners (GPs) work in an ill-defined environment where diagnostic errors are prevalent. Previous research indicates that aggregating independent diagnoses can improve diagnostic accuracy in a range of settings. We examined whether aggregating independent diagnoses can also improve diagnostic accuracy for GP decision making. In addition, we investigated the potential benefit of such an approach in combination with a decision support system (DSS). METHODS: We simulated virtual groups using data sets from 2 previously published studies. In study 1, 260 GPs independently diagnosed 9 patient cases in a vignette-based study. In study 2, 30 GPs independently diagnosed 12 patient actors in a patient-facing study. In both data sets, GPs provided diagnoses in a control condition and/or DSS condition(s). Each GP's diagnosis, confidence rating, and years of experience were entered into a computer simulation. Virtual groups of varying sizes (range: 3-9) were created, and different collective intelligence rules (plurality, confidence, and seniority) were applied to determine each group's final diagnosis. Diagnostic accuracy was used as the performance measure. RESULTS: Aggregating independent diagnoses by weighing them equally (i.e., the plurality rule) substantially outperformed average individual accuracy, and this effect increased with increasing group size. Selecting diagnoses based on confidence only led to marginal improvements, while selecting based on seniority reduced accuracy. Combining the plurality rule with a DSS further boosted performance. DISCUSSION: Combining independent diagnoses may substantially improve a GP's diagnostic accuracy and subsequent patient outcomes. This approach did, however, not improve accuracy in all patient cases. Therefore, future work should focus on uncovering the conditions under which collective intelligence is most beneficial in general practice. HIGHLIGHTS: We examined whether aggregating independent diagnoses of GPs can improve diagnostic accuracy.Using data sets of 2 previously published studies, we composed virtual groups of GPs and combined their independent diagnoses using 3 collective intelligence rules (plurality, confidence, and seniority).Aggregating independent diagnoses by weighing them equally substantially outperformed average individual GP accuracy, and this effect increased with increasing group size.Combining independent diagnoses may substantially improve GP's diagnostic accuracy and subsequent patient outcomes.

The nature of anchor-biased estimates and its application to the wisdom of crowds.

We propose a method to achieve better wisdom of crowds by utilizing anchoring effects. In this method, people are first asked to make a comparative judgment such as "Is the number of new COVID-19 infections one month later more or less than 10 (or 200,000)?" As in this example, two sufficiently different anchors (e.g., "10" or "200,000") are set in the comparative judgment. After this comparative judgment, people are asked to make their own estimates. These estimates are then aggregated. We hypothesized that the aggregated estimates using this method would be more accurate than those without anchor presentation. To examine the effectiveness of the proposed method, we conducted three studies: a computer simulation and two behavioral experiments (numerical estimation of perceptual stimuli and estimation of new COVID-19 infections by physicians). Through computer simulations, we could identify situations in which the proposed method is effective. Although the proposed method is not always effective (e.g., when a group can make fairly accurate estimations), on average, the proposed method is more likely to achieve better wisdom of crowds. In particular, when a group cannot make accurate estimations (i.e., shows biases such as overestimation or underestimation), the proposed method can achieve better wisdom of crowds. The results of the behavioral experiments were consistent with the computer simulation findings. The proposed method achieved better wisdom of crowds. We discuss new insights into anchoring effects and methods for inducing diverse opinions from group members.

Subjective Confidence as a Monitor of the Replicability of the Response.

Confidence is commonly assumed to monitor the accuracy of responses. However, intriguing results, examined in the light of philosophical discussions of epistemic justification, suggest that confidence actually monitors the reliability of choices rather than (directly) their accuracy. The focus on reliability is consistent with the view that the construction of truth has much in common with the construction of reality: extracting reliable properties that afford prediction. People are assumed to make a binary choice by sampling cues from a "collective wisdomware," and their confidence is based on the consistency of these cues, in line with the self-consistency model. Here, however, I propose that internal consistency is taken to index the reliability of choices themselves-the likelihood that they will be repeated. The results of 10 studies using binary decisions from different domains indicated that confidence in a choice predicts its replicability both within individuals and across individuals. This was so for domains for which choices have a truth value and for those for which they do not. For the former domains, differences in replicability mediated the prediction of accuracy whether confidence was diagnostic or counterdiagnostic of accuracy. Metatheoretical, methodological, and practical implications are discussed.

Young adult peer crowds, e-cigarette advertising exposure, and e-cigarette use: Test of a mediation model.

BACKGROUND: Young people often make lifestyle choices or engage in behaviors, including tobacco product use, based on the norms of peer crowds they affiliate with. Peer crowds are defined as reputation-based peer groups centered around lifestyle norms (e.g., Hipster, Surfer, Hip Hop). This study examined the effects of peer crowd affiliation on e-cigarette use via increased exposure to e-cigarette advertising and increased social network e-cigarette use. METHOD: Data were collected from 1398 ethnically diverse young adults (Mean age = 22.3; SD = 3.2; 62% women) in six-month intervals over one year. Path analyses were used to test a mediation model in which advertising exposure and social network e-cigarette use at six-month follow-up were specified to mediate the effects of baseline peer crowd affiliation on current e-cigarette use at one-year follow-up. RESULTS: Affiliations with Popular-Social and Alternative peer crowds at baseline were associated with higher e-cigarette advertising exposure at six-month follow-up. Affiliation with Popular-Social peer crowd at baseline was associated with increased social network e-cigarette use at six-month follow-up. Affiliation with Popular-Social peer crowds at baseline was found to have a statistically significant indirect effect on increased e-cigarette use at one-year follow-up via increased e-cigarette advertising exposure at six-month follow-up. CONCLUSIONS: Better understanding Popular-Social peer crowds may be highly relevant for development of tailored media and other interventions for e-cigarette use prevention among young adults.

The role of identity-related beliefs in the appraisal and management of crowding: Insights from the Hajj.

In crowds, to the degree one identifies with other crowd members one likely experiences a sense of common purpose, social connection and mutual support. Such is the psychological significance of these correlates of a shared identity that even others' close physical proximity can be pleasurable. However, such pleasure in others' proximity cannot be assumed: physical crowding can bring practical challenges and so potentially disturb the positive experience of crowd membership. In the research reported here, we explore crowd members' reports of such challenges and the ways in which these were interpreted and managed through reference to the beliefs and values associated with crowd members' shared identity. Our data arise from semi-structured interviews (N = 33) with British Muslims after participating in the Hajj pilgrimage in Saudi Arabia. Exploring these data sheds light on the ways in which identity-related beliefs and values can contribute to the maintenance of order and harmony even in situations where crowding could undermine the positive experience of others' proximity. Accordingly, our analysis advances our understanding of the self-organization and self-policing of crowds.

Crowds Can Effectively Identify Misinformation at Scale.

Identifying successful approaches for reducing the belief and spread of online misinformation is of great importance. Social media companies currently rely largely on professional fact-checking as their primary mechanism for identifying falsehoods. However, professional fact-checking has notable limitations regarding coverage and speed. In this article, we summarize research suggesting that the "wisdom of crowds" can be harnessed successfully to help identify misinformation at scale. Despite potential concerns about the abilities of laypeople to assess information quality, recent evidence demonstrates that aggregating judgments of groups of laypeople, or crowds, can effectively identify low-quality news sources and inaccurate news posts: Crowd ratings are strongly correlated with fact-checker ratings across a variety of studies using different designs, stimulus sets, and subject pools. We connect these experimental findings with recent attempts to deploy crowdsourced fact-checking in the field, and we close with recommendations and future directions for translating crowdsourced ratings into effective interventions.

The Wisdom of the Crowd is not a Forgone Conclusion. Effects of Self-Selection on (Collaborative) Knowledge Construction.

Web 2.0 has elevated the possibilities of collaboration to unprecedented levels. Therein lies great potential, as the aptly coined phenomenon "Wisdom of the Crowd" implies. When it comes to controversial topics, however, there is no safety in numbers alone. On the contrary, collaboration among only like-minded people may even exacerbate biases (e.g., Echo Chambers). Yet, it is human nature to seek out like-minded others. Consequently, the process of self-selection is crucial if the heterogeneity of opinions serves as a safeguard against undesirable effects of group processes (e.g., attitude polarization). Accordingly, online environments that invite more heterogeneous (vs. homogeneous) users should produce less biased content. We tested this hypothesis in a field study, comparing articles on the same 20 controversial topics from the online encyclopedias Conservapedia and RationalWiki with Wikipedia (and Britannica serving as a gold standard) and exploring the opinions of discussants in the three online encyclopedias. As expected, articles from Conservapedia and RationalWiki were significantly less balanced than articles from Wikipedia and Britannica. We replicated this finding in a lab study with 257 participants who self-selected to one of three online wikis (Vegan Love, Nutrition, Meat & Fish) and individually as well as collaboratively wrote an encyclopedia-like article about "Diets." As expected, Wikis with a specific focus (Vegan Love, Meat & Fish) predominantly attracted authors with a positive attitude toward this focus and, as a consequence, resulted in more biased content than in the Nutrition Wiki. Overall, our results suggest that crowds alone do not guarantee wisdom-self-selection is a crucial process that needs to be taken into account.

The Strengths and Weaknesses of Crowds to Address Global Problems.

Global climate change, the COVID-19 pandemic, and the spread of misinformation on social media are just a handful of highly consequential problems affecting society. We argue that the rough contours of many societal problems can be framed within a "wisdom of crowds" perspective. Such a framing allows researchers to recast complex problems within a simple conceptual framework and leverage known results on crowd wisdom. To this end, we present a simple "toy" model of the strengths and weaknesses of crowd wisdom that easily maps to many societal problems. Our model treats the judgments of individuals as random draws from a distribution intended to represent a heterogeneous population. We use a weighted mean of these individuals to represent the crowd's collective judgment. Using this setup, we show that subgroups have the potential to produce substantively different judgments and we investigate their effect on a crowd's ability to generate accurate judgments about societal problems. We argue that future work on societal problems can benefit from more sophisticated, domain-specific theory and models based on the wisdom of crowds.

Harnessing the power of collective intelligence in dentistry: a pilot study in Victoria, Australia.

BACKGROUND: In many dental settings, diagnosis and treatment planning is the responsibility of a single clinician, and this process is inevitably influenced by the clinician's own heuristics and biases. Our aim was to test whether collective intelligence increases the accuracy of individual diagnoses and treatment plans, and whether such systems have potential to improve patient outcomes in a dental setting. METHODS: This pilot project was carried out to assess the feasibility of the protocol and appropriateness of the study design. We used a questionnaire survey and pre-post study design in which dental practitioners were involved in the diagnosis and treatment planning of two simulated cases. Participants were provided the opportunity to amend their original diagnosis/treatment decisions after viewing a consensus report made to simulate a collaborative setting. RESULTS: Around half (55%, n = 17) of the respondents worked in group private practices, however most practitioners (74%, n = 23) did not collaborate when planning treatment. Overall, the average practitioners' self-confidence score in managing different dental disciplines was 7.22 (s.d. 2.20) on a 1-10 scale. Practitioners tended to change their mind after viewing the consensus response, particularly for the complex case compared to the simple case (61.5% vs 38.5%, respectively). Practitioners' confidence ratings were also significantly higher (p < 0.05) after viewing the consensus for complex case. CONCLUSION: Our pilot study shows that collective intelligence in the form of peers' opinion can lead to modifications in diagnosis and treatment planning by dentists. Our results lay the foundations for larger scale investigations on whether peer collaboration can improve diagnostic accuracy, treatment planning and, ultimately, oral health outcomes.

Compromising improves forecasting.

Predicting the future can bring enormous advantages. Across the ages, reliance on supernatural foreseeing was substituted by the opinion of expert forecasters, and now by collective intelligence approaches which draw on many non-expert forecasters. Yet all of these approaches continue to see individual forecasts as the key unit on which accuracy is determined. Here, we hypothesize that compromise forecasts, defined as the average prediction in a group, represent a better way to harness collective predictive intelligence. We test this by analysing 5 years of data from the Good Judgement Project and comparing the accuracy of individual versus compromise forecasts. Furthermore, given that an accurate forecast is only useful if timely, we analyze how the accuracy changes through time as the events approach. We found that compromise forecasts are more accurate, and that this advantage persists through time, though accuracy varies. Contrary to what was expected (i.e. a monotonous increase in forecasting accuracy as time passes), forecasting error for individuals and for team compromise starts its decline around two months prior to the event. Overall, we offer a method of aggregating forecasts to improve accuracy, which can be straightforwardly applied in noisy real-world settings.

Collaboration to improve cross-race face identification: Wisdom of the multi-racial crowd?

Face identification is particularly prone to error when individuals identify people of a race other than their own - a phenomenon known as the other-race effect (ORE). Here, we show that collaborative "wisdom-of-crowds" decision-making substantially improves face identification accuracy for own- and other-race faces over individuals working alone. In two online experiments, East Asian and White individuals recognized own- and other-race faces as individuals and as part of a collaborative dyad. Collaboration never proved more beneficial in a social setting than when individual identification decisions were combined computationally. The reliable benefit of non-social collaboration may stem from its ability to avoid the potential negative outcomes of group diversity such as conflict. Consistent with this benefit, the racial diversity of collaborators did not influence either general or race-specific face identification accuracy. Our findings suggest that collaboration between two individuals is a promising strategy for improving cross-race face identification that may translate effectively into forensic and eyewitness settings.

Peer Crowds and Tobacco Product Use in Hawai'i: A Qualitative Study.

BACKGROUND: Young adults often derive self-identity from affiliation with peer crowds, which may be defined as reputation-based peer groups centered around characterizable lifestyle norms. Little is known about peer crowds prevalent among Asian American, Native Hawaiian, and other Pacific Islander (AANHPI) populations and the peer crowds' normative tobacco and other substance use behavior. To address this gap in knowledge, this study conducted focus groups with young adult community college students. METHODS: Focus group discussions were conducted with a convenience sample of 42 young adults (Mean age = 21.5, SD = 2.7) recruited across community colleges on O'ahu, Hawai'i. The participants represented 60% women, 55% NHPI, and 29% Asian American. RESULTS: Results indicated the presence of a wide range of peer crowds in the population, which may be classified into the following seven categories prevalent in the literature: Regular, Academic, Alternative, Athlete, Geek, High Risk, and Popular. Several peer crowds within the Alternative, Athlete, Geek, High Risk, and Popular categories appeared to represent subcultures relevant for NHPI young adults. High-risk peer crowds were reported to be vulnerable to different types of substance use. Tobacco product use, particularly e-cigarette use or vaping, was noted to be characteristically present among Popular crowds and certain Athlete crowds. CONCLUSION: Tobacco and other substance use prevention interventions, such as mass media campaigns, may benefit from targeting high-risk peer crowds, especially those relevant for NHPI young adults, who are at high risk for tobacco and other substance use. E-cigarette use prevention interventions may benefit from paying close attention to vulnerable Popular and Athlete groups.