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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.

Embracing local knowledge is vital to conserve and manage biodiversity, yet frameworks to do so are lacking. We need to understand which, and how many knowledge holders are needed to ensure that management recommendations arising from local knowledge are not skewed towards the most vocal individuals. Here, we apply a Wisdom of Crowds framework to a data-poor recreational catch-and-release fishery, where individuals interact with natural resources in different ways. We aimed to test whether estimates of fishing quality from diverse groups (multiple ages and years of experience), were better than estimates provided by homogenous groups and whether thresholds exist for the number of individuals needed to capture estimates. We found that diversity matters; by using random subsampling combined with saturation principles, we determine that targeting 31% of the survey sample size captured 75% of unique responses. Estimates from small diverse subsets of this size outperformed most estimates from homogenous groups; sufficiently diverse small crowds are just as effective as large crowds in estimating ecological state. We advocate for more diverse knowledge holders in local knowledge research and application. [ABSTRACT FROM AUTHOR]

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert's knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds. [ABSTRACT FROM AUTHOR]

Abstract Embracing local knowledge is vital to conserve and manage biodiversity, yet frameworks to do so are lacking. We need to understand which, and how many knowledge holders are needed to ensure that management recommendations arising from local knowledge are not skewed towards the most vocal individuals. Here, we apply a Wisdom of Crowds framework to a data-poor recreational catch-and-release fishery, where individuals interact with natural resources in different ways. We aimed to test whether estimates of fishing quality from diverse groups (multiple ages and years of experience), were better than estimates provided by homogenous groups and whether thresholds exist for the number of individuals needed to capture estimates. We found that diversity matters; by using random subsampling combined with saturation principles, we determine that targeting 31% of the survey sample size captured 75% of unique responses. Estimates from small diverse subsets of this size outperformed most estimates from homogenous groups; sufficiently diverse small crowds are just as effective as large crowds in estimating ecological state. We advocate for more diverse knowledge holders in local knowledge research and application.

Collective intelligence refers to the ability of groups to problem-solving, and decision-making. Such ability is not often exhibited when group members act individually. In addition, collective intelligence is interdisciplinary research involving biology, psychology, computer science, complex sciences, cognitive studies, etc. In recent years, the rapid development of social media has significantly changed how humans interact and communicate with each other. Accordingly, many research results have shed light on how social media fosters collective intelligence. This paper presents an approach to enhancing the collective intelligence of crowds based on providing a small expert’s knowledge related to the given problem. In addition, collective members are inspired by individuals on social networks. For such a task, graph theory is used to represent the relationship between collective members. Computational experiments have been carried out showing the positive role of the proposed method in enhancing collective intelligence. Finally, we discuss some further issues on improving the collective intelligence of crowds.

The fitness of an organism can be strongly affected by the decisions that it makes throughout its lifetime. These decisions may be spatial (deciding where to go), temporal (deciding when to perform an action), or a mixture thereof. How organisms make spatial or temporal decisions should involve different mechanisms because of fundamental differences between the two. For example, time is irreversible, while animals can traverse space more freely. Making decisions together as a group can improve the accuracy of decisions (a form of collective intelligence). However, to date, almost all existing research has been on collective spatial decisions and, as a result, it is through this spatial lens that our intuition of collective decisions has developed. Understanding how individuals in groups make timing decisions is particularly relevant in a changing climate, where both the optimal time to perform actions and the cues used to time the action are changing. Studying collective intelligence in the context of timing decisions will reveal novel mechanisms that social animals across taxa (including humans) use, allowing us to predict the future of species in a changing world and to design new bio-inspired strategies. 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. [ABSTRACT FROM AUTHOR]

In many domains, it is necessary to combine opinions or forecasts from multiple individuals. However, the average or modal judgment is often incorrect, shared information across respondents can result in correlated errors, and weighting judgments by confidence does not guarantee accuracy. We develop a Bayesian hierarchical model of crowd wisdom that incorporates predictions about others to address these aggregation challenges. The proposed model can be applied to single questions, and it can also estimate respondent expertise given multiple questions. Unlike existing Bayesian hierarchical models for aggregation, the model does not link the correct answer to consensus or privilege majority opinion. The model extends the "surprisingly popular algorithm" to enable statistical inference and in doing so, overcomes several of its limitations. We assess performance on empirical data and compare the results with other aggregation methods, including leading Bayesian hierarchical models. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported in part by the National Science Foundation [Grant MMS 2019982] and All Souls College Oxford [Visiting Fellowships in 2020 and 2022 to D. Prelec]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4955. [ABSTRACT FROM AUTHOR]

This research aims to examine whether corporate Twitter messages and tone have an effect on corporate stock return (RET) for the Saudi Stock Exchange "Tadawul". The study also investigates whether the association differs across large- and small-sized firms. We used a sample of 11,099 firm-daily observations for non-financial firms that were traded on the Saudi Stock Exchange "Tadawul" across the period 1 April 2020 to 31 December 2020. Using panel ordinary least square (OLS) and two-stage least square (2SLS), we found that corporate Twitter (currently renamed 'X') messages is positively and significantly associated with stock return (RET). The findings also suggest that the message tone increases the stock returns. Furthermore, our results show different effects of Twitter messages and tone on stock return across small- and large-sized firms. In addition, our findings show that Twitter tone is positively associated with RET when the firm is large in size. However, when the firm is small, Twitter messages has a stronger effect on RET. Our findings provide policy implications for regulators and investors. Regulators might monitor the information in accurate ways. Also, investors might start to show interest in Twitter channels to follow the firm's news. [ABSTRACT FROM AUTHOR]

'The wisdom of crowds' theory has received widespread attention and application. For scholars, the wisdom of crowds is of great significance in revealing the operating mechanism of the scientific community. However, scholar crowds are jointly affected by scientific cognition and coordination, which are different from general human crowds. 'The wisdom of crowds' theory poses significant challenges in terms of directly explaining and evaluating the wisdom generation among scholars. Considering that knowledge diffusion is an important way to generate scientific cognition and coordination, this work proposed 'the wisdom of scholar crowds' and evaluates it from the perspective of knowledge diffusion. First, scholar-paper and scholar-topic two-layer networks were constructed, achieving a holistic representation of scientific coordination and cognition in the network structure dimension. Second, the topic consistency among scholars was identified using the two-layer networks, and a knowledge diffusion evaluation model based on topic consistency was designed to evaluate the scale and threshold of the wisdom generation of scholar crowds. Finally, combined with 3,838,048 paper data, this work revealed that the cohesion and bridging of network structure contribute to the wisdom generation of scholar crowds. By comparing with the commonly used evaluation methods, this study shows that the generating difficulty of the wisdom of scholar crowds will be underestimated without topic consistency. This work provides a new perspective for expanding the 'wisdom of crowds' theory and a novel method for evaluating knowledge diffusion and the wisdom of scholar crowds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract 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.

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. [ABSTRACT FROM AUTHOR]

The explosive spread of false news on social media has severely affected many areas such as news ecosystems, politics, economics, and public trust, especially amid the COVID-19 infodemic. Machine intelligence has met with limited success in detecting and curbing false news. Human knowledge and intelligence hold great potential to complement machine-based methods. Yet they are largely underexplored in current false news detection research, especially in terms of how to efficiently utilize such information. We observe that the crowd contributes to the challenging task of assessing the veracity of news by posting responses or reporting. We propose combining these two types of scalable crowd judgments with machine intelligence to tackle the false news crisis. Specifically, we design a novel framework called CAND, which first extracts relevant human and machine judgments from data sources including news features and scalable crowd intelligence. The extracted information is then aggregated by an unsupervised Bayesian aggregation model. Evaluation based on Weibo and Twitter datasets demonstrates the effectiveness of crowd intelligence and the superior performance of the proposed framework in comparison with the benchmark methods. The results also generate many valuable insights, such as the complementary value of human and machine intelligence, the possibility of using human intelligence for early detection, and the robustness of our approach to intentional manipulation. This research significantly contributes to relevant literature on false news detection and crowd intelligence. In practice, our proposed framework serves as a feasible and effective approach for false news detection. [ABSTRACT FROM AUTHOR]

Recently, Collective Intelligence has been widely applied to solve a wide range of complex problems. The underlying mechanism behind this notion is a group of individuals as a whole can display abilities not shown by individuals. In addition, the main aim of Collective Intelligence is to maximize groups' potential. Many studies have investigated analyzing the roles of collective characteristics in fostering collective intelligence. Independence is an important principle of collective intelligence beside diversity, decentralization, and aggregation. By independence, one can have in mind the phenomenon in which collective members are freely in providing individual solutions to a given problem. This paper presents an analysis of the impact of independence on collective performance. For such a task, we propose an independence measure by taking into account individual reputations and graph theory. Then computational experiments have been conducted to determine the impact of independence on collective performance. The findings have revealed the positive role of independence in fostering collective intelligence. [ABSTRACT FROM AUTHOR]

Consulting multiple advisors tends to improve decision quality; however, limited understanding exists regarding how advisors respond to the presence of co‐advisors. Previous research has cautioned about the potential interpersonal costs of seeking advice from multiple sources. It suggests that advisors may perceive their advice as less likely to be utilized, diminishing their willingness to continue assisting the seeker. In contrast, we propose that advisors are generally unconcerned if seekers consult others, as long as they are informed before offering advice. We argue that advisors do not closely monitor or dwell on the utilization of their advice and maintain a positive attitude toward the seeker unless they infer rejection of their advice. In three studies, we show that disclosing a co‐advisor upfront completely eliminates any negative interpersonal effects by rendering inferences about advice rejection implausible. Advisors respond as if they were the sole advisor irrespective of the presence of multiple co‐advisors and regardless of whether they are consulted first of second. [ABSTRACT FROM AUTHOR]

Digitally enabled means for judgment aggregation have renewed interest in "wisdom of the crowd" effects and kick-started collective intelligence design as an emerging field in the cognitive and computational sciences. A keenly debated question here is whether social influence helps or hinders collective accuracy on estimation tasks, with recent results on the role of network structure hinting at a reconciliation of seemingly contradictory past results. Yet, despite a growing body of literature linking social network structure and collective accuracy, strategies for exploiting network structure to harness crowd wisdom are underexplored. We introduce one such strategy: rewiring algorithms that dynamically manipulate the structure of communicating social networks. Through agent-based simulations and an online multiplayer experiment, we provide a proof of concept showing how rewiring algorithms can increase the accuracy of collective estimations—even in the absence of knowledge of the ground truth. However, we also find that the algorithms' effects are contingent on the distribution of estimates initially held by individuals before communication occurs. CCS Concepts: •Human-centered computing → Collaborative and social computing• Applied computing → Psychology. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper focuses on the determinants of transfer market valuations of Italian top division football players over the period 2007-2017. We use data provided by transfermarkt.de to estimate the association between players' characteristics and their transfer valuation. Additionally, by applying panel regression techniques, we separate team and season effects from individual attributes. We find an inverted U-shaped association with age and a positive association with goals, assists, and minutes played in national and international competitions. Moreover, the financial sustainability of the clubs appears to be a key factor since potential accountability turmoil may cause a reduction in the bargaining power of clubs and on the players' market values consequently. Finally, season fixed effects show a statistically significant and strong negative trend of players' market appraisals. That result should be taken seriously by representatives of Serie A League as well as the Italian Football Federation because the recently pursued strategy of adjusting the decrease of conventional revenues (e.g., ticket sales, TV rights and sponsorship) with the capital gains coming from player transfers proves to be rather risky. [ABSTRACT FROM AUTHOR]

This study aims to predict the aggregate of earnings and announcement returns with the help of this purpose, by a selection of Twitter social media as a media approved by the US Stock Exchange and Securities Organization, data related to 345 companies selected from the list of top 500 companies in the United States, for the four years 2016-2019 (Market participant tweets about sample companies from October 2015 to March 2020), extracted and used Was analyzed from Stata software. The results showed that the volume of earnings news published by companies on Twitter could predict earnings surprises, and the content of earnings news published by companies on Twitter can predict earnings surprises. The results also showed that the volume of earning news published by companies on Twitter could predict announcement returns and the content of earning news published by companies on Twitter can predict announcement returns. The results can also be considered a strategy in content analysis of market-quality news based on a list of specialized financial words.

We analyze how advice from an AI affects complementarities between humans and AI, in particular what humans know that an AI does not know: "unique human knowledge." In a multi-method study consisting of an analytical model, experimental studies, and a simulation study, our main finding is that human choices converge toward similar responses improving individual accuracy. However, as overall individual accuracy of the group of humans improves, the individual unique human knowledge decreases. Based on this finding, we claim that humans interacting with AI behave like "Borgs," that is, cyborg creatures with strong individual performance but no human individuality. We argue that the loss of unique human knowledge may lead to several undesirable outcomes in a host of human-AI decision environments. We demonstrate this harmful impact on the "wisdom of crowds." Simulation results based on our experimental data suggest that groups of humans interacting with AI are far less effective as compared to human groups without AI assistance. We suggest mitigation techniques to create environments that can provide the best of both worlds (e.g., by personalizing AI advice). We show that such interventions perform well individually as well as in wisdom of crowds settings. [ABSTRACT FROM AUTHOR]

We examine whether passivity on firms' corporate Facebook pages moderates the relationship between the sentiment among social media users toward the firms and their future stock returns. We find that a longer period of posting passivity is likely to reverse a positive effect of positive sentiment on the firm's future stock prices. For negative sentiment, we find that social media passivity results in amplified negative sentiment on the firm's future stock prices. Moreover, we find that the longer the firm is passive, the longer it takes to overcome the negative effects of the posting passivity by resuming posting. Data Availability: Data are available from commercial databases and public sources identified in the paper. JEL Classification: D91, G40, M15, M40, M41. [ABSTRACT FROM AUTHOR]

Cities around the world have amassed a variety of data. A main challenge lies in transforming these big data into meaningful knowledge that can inform a city's strategic decisions and enhance urban sustainability. Along with this challenge is the debate about "Wisdom of Crowds" (WOC) in the Petabyte Age. Using the City of Arlington, Texas as a case study, this research explores a hybrid approach for social inquiries with the aid of WOC and spatial learning techniques to leverage data for developing predictive models to support a city's service planning. The results indicate that there exist temporal and spatial patterns of service demands, spatial correlation between demands for code compliance and water services, as well as association with neighborhood characteristics. The findings point to opportunities for further data integration and data mining, organizational collaboration, and resource management to improve the efficiency of service provision in cities. [ABSTRACT FROM AUTHOR]

Abstract 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.

Not only is social media a new channel to obtain financial market information, it has also become a venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to both existing and new amateur analysts on social media and its implications for online investor communities. We find that monetary incentive is effective in increasing the amount of content output and generating more interest from the community, but it leads to neither better nor worse stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of the role of monetary incentive in stimulating the sharing of value-relevant information by investors in social media communities. [ABSTRACT FROM AUTHOR]

This study aims to predict the aggregate of earnings and announcement returns with the help of this purpose, by a selection of Twitter social media as a media approved by the US Stock Exchange and Securities Organization, data related to 345 companies selected from the list of top 500 companies in the United States, for the four years 2016-2019 (Market participant tweets about sample companies from October 2015 to March 2020), extracted and used Was analyzed from Stata software. The results showed that the volume of earnings news published by companies on Twitter could predict earnings surprises, and the content of earnings news published by companies on Twitter can predict earnings surprises. The results also showed that the volume of earning news published by companies on Twitter could predict announcement returns and the content of earning news published by companies on Twitter can predict announcement returns. The results can also be considered a strategy in content analysis of market-quality news based on a list of specialized financial words. [ABSTRACT FROM AUTHOR]

Software upcycling, a form of software reuse, is a concept that efficiently generates novel, innovative, and value-added development projects by utilizing knowledge extracted from past projects. However, how to integrate the materials derived from these projects for upcycling remains uncertain. This study defines a systematic model for upcycling cases and develops the Sharing Upcycling Cases with Context and Evaluation for Efficient Software Development (SUCCEED) system to support the implementation of new upcycling initiatives by effectively sharing cases within the organization. To ascertain the efficacy of upcycling within our proposed model and system, we formulated three research questions and conducted two distinct experiments. Through surveys, we identified motivations and characteristics of shared upcycling-relevant development cases. Development tasks were divided into groups, those that employed the SUCCEED system and those that did not, in order to discern the enhancements brought about by upcycling. As a result of this research, we accomplished a comprehensive structuring of both technical and experiential knowledge beneficial for development, a feat previously unrealizable through conventional software reuse, and successfully realized reuse in a proactive and closed environment through construction of the wisdom of crowds for upcycling cases. Consequently, it becomes possible to systematically perform software upcycling by leveraging knowledge from existing projects for streamlining of software development. [ABSTRACT FROM AUTHOR]

A combination of point estimates from multiple judges often provides a more accurate aggregate estimate than a point estimate from a single judge, a phenomenon called "the wisdom of crowds." However, if the judges use shared information when forming their estimates, the simple average will end up overemphasizing this common component at the expense of the judges' private information. A decision maker could in theory obtain a more accurate estimate by appropriately combining all information behind the judges' opinions. Although this information underlies the judges' individual estimates, it is typically unobservable and thus cannot be directly aggregated by a decision maker. In this article, we propose a weighting of judges' individual estimates that appropriately combines their collective information within a single estimation problem. Judges are asked to provide both a point estimate of the quantity of interest and a prediction of the average estimate that will be given by all other judges. Predictions of others are then used as part of a criterion to determine weights that are applied to each judge's estimate to form an aggregate estimate. Our weighting procedure is robust to noise in the judges' responses and can be expressed in closed form. We use both simulation and data from a collection of experimental studies to illustrate that the weighting procedure outperforms existing methods. An R package called metaggR implements our method and is available on the Comprehensive R Archive Network. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported by the Indiana University Kelley School of Business and INSEAD. Supplemental Material: The data files and e-companion are available at https://doi.org/10.1287/mnsc.2022.4648. [ABSTRACT FROM AUTHOR]

On the edge of our continents, oceanic crust meets continental crust. At passive margins, those where there is no active tectonics, subduction or transform faulting, these crustal types are connected as sharp continent‐ocean boundaries (COB) or as diffuse continent‐ocean transition (COT) zones. Passive margins are hard to explore and consequently relatively little is known about their morphology or the geological processes of their formation. Here we elicit and analyze seismic image interpretations of the passive margin offshore East India conducted by 17 groups of geoscientists to better understand the differences, or lack therein, of COB or COT interpretations of the margin. The group interpretations provide a wide range of margin models, five of which are abrupt COB based and 11 which are diffuse COT based. However, interpretations within the COB set vary in the placement of the boundary line between continental and oceanic crust, the boundary placement lying within the range of interpreted COT zones, with the average COB location falling in the center of the interpreted COT zones. These crowd‐sourced results are then compared with 10 published interpretations across the margin, which show COB and COT zones falling in the same area. These findings raise questions as to the real differences in COB and COT models and the geological processes involved in their formation. Considering this, we discuss the implications for passive margin models and the use of Wisdom of Crowds‐type approaches in reflecting on both the range of interpretation‐based models and in the value of determining "average" model approaches. Key Points: Passive margin models are conventionally interpreted as abrupt continent‐ocean boundaries or diffuse transitionsWe run a group experiment to explore the range and variability in interpretations of a seismic section from the Eastern India marginExpert elicitation and crowd‐sourced experiments are helpful to identify areas of uncertainty within passive margin model building [ABSTRACT FROM AUTHOR]

A long-standing debate is whether social influence improves the collective wisdom of a crowd or undermines it. This paper addresses the question based on a naïve learning setting in influence systems theory: in our models individuals evolve their estimates of an unknown truth according to the weighted-average opinion dynamics. A formal mathematization is provided with rigorous theoretical analysis. We obtain various conditions for improving, optimizing, and undermining the crowd accuracy, respectively. We prove that if the wisdom of a finite-size group is improved, then the collective estimate converges to the truth as the group size increases. We show that whether social influence improves or undermines the wisdom is determined by the social power allocations in the influence system: if the influence system allocates relatively larger social power to relatively more accurate individuals, it improves the wisdom; on the contrary, if the influence system assigns less social power to more accurate individuals, it undermines the wisdom. At a population level, individuals' susceptibilities to interpersonal influence and network centralities are both crucial. To improve the wisdom, more accurate individuals should be less susceptible and have larger network centralities. Particularly, in democratic influence networks, if relatively more accurate individuals are relatively less susceptible, the wisdom is improved; if more accurate individuals are more susceptible, the wisdom is undermined, which is consistent with the reported empirical evidence. Our investigation provides a theoretical framework for understanding the role social influence plays in the emergence of collective wisdom. [ABSTRACT FROM AUTHOR]

This study evaluates the interdependence between pricing and expectations. We investigated not only the ways in which traders' thoughts determined asset prices, but also the feedback process from prices to expectations. In our laboratory market, subjects were asked to estimate the number of balls in a jar and trade an asset whose value was equal to that number. Our asset market, where transactions were eventually settled at the asset value, was like futures markets. The subjects alternately repeated the process of guessing and transaction. A double-auction was used to design our market. Our findings indicated a downward bias in the subjects' estimates, which led to lower transaction prices, since the price converged to the equilibrium price that was determined by the median of estimates. The subjects' experience in our laboratory markets had no systematic effect on the accuracy of estimates, but made them less heterogenous. Our subjects were apt to revise their estimates with reference to prices in a market. We examined the estimation revision process of the subjects using the partial adjustment model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Is forecasting ability a stable trait? While domain knowledge and reasoning abilities are necessary for making accurate forecasts, research shows that knowing how accurate forecasters have been in the past is the best predictor of future accuracy. However, unlike the measurement of other traits, evaluating forecasting skill requires substantial time investment. Forecasters must make predictions about events that may not resolve for many days, weeks, months, or even years into the future before their accuracy can be estimated. Our work builds upon methods such as cultural consensus theory and proxy scoring rules to show talented forecasters can be discriminated in real time, without requiring any event resolutions. We define a peer similarity-based intersubjective evaluation method and test its utility in a unique longitudinal forecasting experiment. Because forecasters predicted all events at the same points in time, many of the confounds common to forecasting tournaments or observational data were eliminated. This allowed us to demonstrate the effectiveness of our method in real time, as time progressed and more information about forecasters became available. Intersubjective accuracy scores, which can be obtained immediately after the forecasts are made, were both valid and reliable estimators of forecasting talent. We also found that asking forecasters to make meta-predictions about what they expect others to believe can serve as an incentive-compatible method of intersubjective evaluation. Our results indicate that selecting small groups of, or even single forecasters, based on intersubjective accuracy can yield subsequent forecasts that approximate the actual accuracy of much larger crowd aggregates. [ABSTRACT FROM AUTHOR]

Learners faced with competing statements that each havesupport from multiple sources must decide whom to trust.Lacking firsthand knowledge, they frequently trust themajority. Yet, majorities can be misleading if most membersare relying on hearsay from just a few members withfirsthand knowledge. Thus, past work has emphasized theimportance of informational independence when decidingwhom to trust, showing that children and adults do considerinformational independence important in certain contexts.However, because informational independence precludesgroup deliberation, we ask whether children make the reverseinference and devalue informational independence whenfacing a problem that could benefit from deliberation. In twostudies, children and adults ignore informationalindependence when attempting to answer abstract reasoningquestions. However, for a question type for whichdeliberative reasoning would be of doubtful benefit, childrenand adults seek advice from multiple independent sourcesrather than a deliberative group.

Not only is social media a new channel to obtain financial market information, it has also become a venue for investors to share and exchange investment ideas. We examine the performance consequences of providing monetary incentive to both existing and new amateur analysts on social media and its implications for online investor communities. We find that monetary incentive is effective in increasing the amount of content output and generating more interest from the community, but it leads to neither better nor worse stock recommendations. Additional analysis suggests that monetary incentive results in wider stock and industry coverage, a sign of increased content diversity. This study contributes to the understanding of the role of monetary incentive in stimulating the sharing of value-relevant information by investors in social media communities. [ABSTRACT FROM AUTHOR]

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.

How should we combine disagreeing expert judgments on the likelihood of an event? A common solution is simple averaging, which allows independent individual errors to cancel out. However, judgments can be correlated due to an overlap in their information, resulting in a miscalibration in the simple average. Optimal weights for weighted averaging are typically unknown and require past data to estimate reliably. This paper proposes an algorithm to aggregate probabilistic judgments under shared information. Experts are asked to report a prediction and a meta-prediction. The latter is an estimate of the average of other individuals' predictions. In a Bayesian setup, I show that if average prediction is a consistent estimator, the percentage of predictions and meta-predictions that exceed the average prediction should be the same. An "overshoot surprise" occurs when the two measures differ. The Surprising Overshoot algorithm uses the information revealed in an overshoot surprise to correct for miscalibration in the average prediction. Experimental evidence suggests that the algorithm performs well in moderate to large samples and in aggregation problems where individuals disagree in their predictions. [ABSTRACT FROM AUTHOR]

In their simplest form, consensus gentium arguments for theism argue that theism is true on the basis that everyone believes that theism is true. While such arguments may have been popular in history, they have all but fallen from grace in the philosophy of religion. In this short paper, we reconsider the neglected topic of consensus gentium arguments, paying particular attention to the value of such arguments when deployed in the defence of theistic belief. We argue that while consensus gentium arguments are unlikely to offer anything close to overwhelming support for theism, their probative value is nevertheless underappreciated, and that they have been unfairly maligned as a consequence. [ABSTRACT FROM AUTHOR]