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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]

A fundamental question in the social sciences is how collectives of individuals form intelligent judgments. This article tests the hypothesis that genetically-diverse groups make better collective judgments than genetically more homogenous groups. Two studies were conducted (a total of N = 602 participants) in which sets of twins (both monozygotic and dizygotic) were required to perform the task of making numerical judgments. The accuracy of the judgments made by pairs of participants—who were either co-twins (i.e., genetically-related) or were not related—was then compared. The results indicate that the judgments made by unrelated pairs were more accurate than those of the genetically-related twins. Critically, however, this superior performance was found only among monozygotic twins, evidencing the role of genetic relatedness in collective judgment. This research provides the first empirical demonstration of the benefit of genetic diversity for collective judgments, shedding light on the origins of the 'wisdom of crowds' phenomenon. • Past studies suggested that combining individuals' judgments improves accuracy. • Accuracy is leveraged when individuals vary in terms of background, education, and demography. • We proposed that genetic diversity contributes to the accuracy derived from combining judgments. • We collected a total of 602 monozygotic and dizygotic twins, who made numerical judgments. • Genetically-heterogeneous pairs of judges were more accurate than genetically-homogenous ones. [ABSTRACT FROM AUTHOR]

The past few decades have witnessed a prevalence of applying dynamical models to the study of social networks. This paper reviews recent advances in the investigation of social networks with a predominant focus on agent-based models. Starting from classical models of opinion dynamics, we survey several recently developed models on opinion formation and social power evolution. These models extend the classical models' cognitive assumption that individuals' opinions evolve on a single issue by incorporating various sociological or psychological hypotheses to account for the evolution of opinions over multiple or a sequence of interdependent issues. We summarize basic results on the asymptotic behaviors of these models and discuss their sociological interpretations. In addition, we show how these models play a role in the emergence of collective intelligence by applying them to a naïve learning setting. Novel results that reveal how individuals successfully learn an unknown truth over issue sequences are presented. Finally, we conclude the paper and discuss potential directions for future research. [ABSTRACT FROM AUTHOR]

Are people who agree on something more likely to be right and competent? Evidence suggests that people tend to make this inference. However, standard wisdom of crowds approaches only provide limited normative grounds. Using simulations and analytical arguments, we argue that when individuals make independent and unbiased estimates, under a wide range of parameters, individuals whose answers converge with each other tend to have more accurate answers and to be more competent. In 6 experiments (UK participants, total N = 1197), we show that participants infer that informants who agree have more accurate answers and are more competent, even when they have no priors, and that these inferences are weakened when the informants were systematically biased. In conclusion, we speculate that inferences from convergence to accuracy and competence might help explain why people deem scientists competent, even if they have little understanding of science. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

By combining financial information from the financial market with social textual information from social media, we apply a two-level information fusion approach to examine the effects of peer engagement on social media on stock price synchronicity and compare the effects between epidemic and non-epidemic contexts. On the first level, single pieces of information are fused at the firm-year level and deep learning models are used to measure the characteristics of peer engagement – informativeness, diversity, information diffusion degree, and expert proportion – which are constructed grounded in the theory of the wisdom of crowds (WoC). On the second level, all measurements at the firm-year level are fused into a full sample to conduct regression analysis. The experimental results show that peer engagement reduces stock price synchronicity. This suggests that high synchronicity could be mitigated through effective guidance from peer engagement activities. We also find that during epidemics, synchronicity is much higher, and group diversity and experts have stronger effects in lowering synchronicity, while the effects of informativeness and information diffusion are hampered. This has implications for combatting the adverse effects of epidemic outbreaks on financial markets. • A two-level information fusion approach for causality identification is proposed. • A measurement of post quality based on deep learning algorithms is developed. • The effects of peer engagement on social media on price synchronicity are identified. • The effects of peer engagement are compared during epidemic and non-epidemic contexts. [ABSTRACT FROM AUTHOR]

• Model class: social networks influence information aggregation where influence dynamics is random due to randomness of communication. • Key insight: (mathematical proof that) consensus level is highly sensitive to early communication. • Take away 1: classic influence measures fail to acknowledge early impact. • Take away 2: wisdom of crowds seems illusive as consensus does not reveal information about truth. We study social learning in a social network setting where agents receive independent noisy signals about the truth. Agents naïvely update beliefs by repeatedly taking weighted averages of neighbors' opinions. The weights are fixed in the sense of representing average frequency and intensity of social interaction. However, the way people communicate is random such that agents do not update their belief in exactly the same way at every point in time. Our findings, based on Theorem 1, Corollary 1 and simulated examples, suggest the following. Even if the social network does not privilege any agent in terms of influence, a large society almost always fails to converge to the truth. We conclude that wisdom of crowds seems an illusive concept and bares the danger of mistaking consensus for truth. [ABSTRACT FROM AUTHOR]

Highlights • This study examines how the manipulation influences the evolution of follow-up ratings over time. • Results show a statistically non-significant difference in the average rating scores before and after manipulation. • The long-term effect is weaker than the short-term one. • The manipulated high-rating score of the vendor will increase the diversity of the consumers' taste preferences, which will lead to diverse evaluations among users. The wisdom of crowds corrects the inflation of the ratings. Abstract Vendors on online shopping platforms have incentives to provide misinformation by manipulating consumer ratings. However, few studies have empirically examined whether temporal self-boosting rating manipulation changes the follow-up dynamic pattern of online ratings. This study empirically identifies manipulated ratings and examines how this manipulation influences the evolution of follow-up ratings over time. Rating records of more than 30,000 restaurants are extracted from the server log of a restaurant review website. The results show a statistically nonsignificant difference in the average rating scores between before and after manipulation, indicating that manipulation does not influence follow-up rating dynamics. Moreover, the difference in average ratings between before and after manipulation decreases within a long time window, which indicates that the long-term effect is weaker than the short-term one. The results are further validated by an online field experiment. We then construct a simulation model to reveal the underlying mechanisms of why the follow-up raters could correct the bias of rating manipulation. The manipulated high-rating score of the vendor will increase the diversity of consumers' individual preferences, which leads to diverse evaluations among users. The aggregation of online ranking by taking the average score of ratings helps form a decentralized climate of opinion and finally helps correct the biased rating. This study challenges the common belief that self-boosting misinformation affects the performance of online commerce. The nonsignificant effect of rating manipulation on rating dynamics echoes the theory of the wisdom of crowds. Furthermore, this study has managerial implications for both online vendors and online rating platforms. We suggested that the system should degrade social influences among users in order to reduce the possible impacts by manipulated ratings. [ABSTRACT FROM AUTHOR]

Abstract Survey research on sensitive questions is challenging because respondents often answer untruthfully or completely refuse to answer. Existing indirect questioning techniques address the problem of social desirability bias at the expense of decreasing estimates' efficiency. We suggest the Wisdom of Crowds survey design that does not pose a tradeoff between anonymity and efficiency as an alternative. We outline the conditions necessary for the technique to work and test them empirically. Moreover, we compare the Wisdom of Crowd estimate of a right-wing populist party's vote share to alternative indirect questioning techniques' estimates as well as to the official election result in the 2017 German federal election. Provided its conditions are met, the Wisdom of Crowds design performs best in terms of both bias and efficiency. We conclude that the Wisdom of Crowds design is an important addition to social scientists' survey methodology toolbox. [ABSTRACT FROM AUTHOR]

Many researchers and practitioners have attempted to predict financial market trends for excess returns using multiple information sources including social media. Recent studies have investigated the relation between public sentiment and stock price movements and demonstrated that investment decisions are affected by public opinion. In this paper, we design a novel framework that combines the wisdom of crowds and technical analysis for financial market prediction using a new fusion strategy. A machine learning technique called deep random subspace ensembles (DRSE), which integrates deep learning algorithms and ensemble learning methods, is proposed according to the characteristics of the prediction task. Based on collected real-world datasets, the experimental results show that our proposed method outperforms the baseline models in predicting stock market by at least 14.2% in terms of AUC value, indicating the efficacy of DRSE as a viable mechanism for financial market prediction. [ABSTRACT FROM AUTHOR]

Nowadays, the wisdom of crowds aids in data labeling via crowd sensors. One of the successful tools worth mentioning is Amazon Mechanical Turk. Uncertainty in crowd labels, however, deteriorates the result of the learning algorithm. In some applications, such as weather and stock forecasting and object tracking, the temporal dependency of data (dynamics) is effective in decreasing label uncertainty. In order to benefit from the existing knowledge in label dynamics, the current study first employs a traditional state-space model and it shows that these models have serious drawbacks, for instance, sensors with a low coverage rate and the existence of a random labeler are the main challenges posed in this process. Then, an appropriate dynamic model for crowd sensors is presented and the Bayesian filter is applied so that true label inference and system parameter learning are performed jointly. The present work will show that the proposed method is robust enough to meet these challenges and performs better in comparison to the existing methods. The results of experiments on synthetic and real data confirm this issue. [ABSTRACT FROM AUTHOR]

Larger groups often have a greater ability to solve cognitive tasks compared to smaller ones or lone individuals. This is well established in social insects, navigating flocks of birds, and in groups of prey collectively vigilant for predators. Research in social insects has convincingly shown that improved cognitive performance can arise from self-organised local interactions between individuals that integrates their contributions, often referred to as swarm intelligence. This emergent collective intelligence has gained in popularity and been directly applied to groups of other animals, including fish. Despite being a likely mechanism at least partially explaining group performance in vertebrates, I argue here that other possible explanations are rarely ruled out in empirical studies. Hence, evidence for self-organised collective (or ‘swarm’) intelligence in fish is not as strong as it would first appear. These other explanations, the ‘pool-of-competence’ and the greater cognitive ability of individuals when in larger groups, are also reviewed. Also discussed is why improved group performance in general may be less often observed in animals such as shoaling fish compared to social insects. This review intends to highlight the difficulties in exploring collective intelligence in animal groups, ideally leading to further empirical work to illuminate these issues. [ABSTRACT FROM AUTHOR]

Background: The quest for an effective system capable of monitoring and predicting the trends of epidemic diseases is a critical issue for communities worldwide. With the prevalence of Internet access, more and more researchers today are using data from both search engines and social media to improve the prediction accuracy. In particular, a prediction market system (PMS) exploits the wisdom of crowds on the Internet to effectively accomplish relatively high accuracy.Objective: This study presents the architecture of a PMS and demonstrates the matching mechanism of logarithmic market scoring rules. The system was implemented to predict infectious diseases in Taiwan with the wisdom of crowds in order to improve the accuracy of epidemic forecasting.Methods: The PMS architecture contains three design components: database clusters, market engine, and Web applications. The system accumulated knowledge from 126 health professionals for 31 weeks to predict five disease indicators: the confirmed cases of dengue fever, the confirmed cases of severe and complicated influenza, the rate of enterovirus infections, the rate of influenza-like illnesses, and the confirmed cases of severe and complicated enterovirus infection.Results: Based on the winning ratio, the PMS predicts the trends of three out of five disease indicators more accurately than does the existing system that uses the five-year average values of historical data for the same weeks. In addition, the PMS with the matching mechanism of logarithmic market scoring rules is easy to understand for health professionals and applicable to predict all the five disease indicators.Conclusions: The PMS architecture of this study affords organizations and individuals to implement it for various purposes in our society. The system can continuously update the data and improve prediction accuracy in monitoring and forecasting the trends of epidemic diseases. Future researchers could replicate and apply the PMS demonstrated in this study to more infectious diseases and wider geographical areas, especially the under-developed countries across Asia and Africa. [ABSTRACT FROM AUTHOR]

This study tests non-representative expectation surveys as a method for forecasting elections. For dichotomous forecasts of the 2013 German election (e.g., who will be chancellor, which parties will enter parliament), two non-representative citizen samples performed equally well than a benchmark group of experts. For vote-share forecasts, the sample of more knowledgeable and interested citizens performed similar to experts and quantitative models, and outperformed the less informed citizens. Furthermore, both citizen samples outperformed prediction markets but provided less accurate forecasts than representative polls. The results suggest that non-representative surveys can provide a useful low-cost forecasting method, in particular for small-scale elections, where it may not be feasible or cost-effective to use established methods such as representative polls or prediction markets. [ABSTRACT FROM AUTHOR]

There has been much interest in group judgment and the so-called ‘wisdom of crowds’. In many real world contexts, members of groups not only share a dependence on external sources of information, but they also communicate with one another, thus introducing correlations among their responses that can diminish collective accuracy. This has long been known, but it has—to date—not been examined to what extent different kinds of communication networks may give rise to systematically different effects on accuracy. We argue that equations that relate group accuracy, individual accuracy, and group diversity (see Hogarth, 1978; Page, 2007) are useful theoretical tools for understanding group performance in the context of research on group structure. In particular, these equations may serve to identify the kind of group structures that improve individual accuracy without thereby excessively diminishing diversity so that the net positive effect is an improvement even on the level of collective accuracy. Two experiments are reported where two structures (the complete network and a small world network) are investigated from this perspective. It is demonstrated that the more constrained network (the small world network) outperforms the network with a free flow of information. [ABSTRACT FROM AUTHOR]

Abstract: This paper discusses a crisis of accountability that arises when scientific collaborations are massively epistemically distributed. We argue that social models of epistemic collaboration, which are social analogs to what Patrick Suppes called a “model of the experiment,” must play a role in creating accountability in these contexts. We also argue that these social models must accommodate the fact that the various agents in a collaborative project often have ineliminable, messy, and conflicting interests and values; any story about accountability in a massively distributed collaboration must therefore involve models of such interests and values and their methodological and epistemic effects. [Copyright &y& Elsevier]

Does discussion in large groups help or hinder the wisdom of crowds? To give rise to the wisdom of crowds, by which large groups can yield surprisingly accurate answers, aggregation mechanisms such as averaging of opinions or majority voting rely on diversity of opinions, and independence between the voters. Discussion tends to reduce diversity and independence. On the other hand, discussion in small groups has been shown to improve the accuracy of individual answers. To test the effects of discussion in large groups, we gave groups of participants (N = 1958 participants in groups of size ranging from 22 to 212; mean 59) one of three types of problems (demonstrative, factual, ethical) to solve, first individually, and then through discussion. For demonstrative (logical or mathematical) problems, discussion improved individual answers, as well as the answers reached through aggregation. For factual problems, discussion improved individual answers, and either improved or had no effect on the answers reached through aggregation. Our results suggest that, for problems which have a correct answer, discussion in large groups does not detract from the effects of the wisdom of crowds, and tends on the contrary to improve on it. [ABSTRACT FROM AUTHOR]

Abstract: Groupthink is a known weakness leading to a number of problems relating primarily to false senses of consensus. But, positive group ‘wisdom’ is an ideal which many aspire to make happen but few manage to achieve in practice. The mystery of the group comes at a number of levels and raises various issues. What is the relative importance of how groups assemble? How they are motivated? The value of inducement? How can group work be assessed and how is a ‘good’ group identified? How is positive and not negative group working achieved? How is group working linked to what the group achieves? In the area of policy use of indicators the function of the group becomes more critical. In an age of transparency in decision making and calls for more evidence-based policy, the importance of good group work is becoming vital if the project is to succeed. Based on research undertaken around the European Union between 2009 and 2010 this paper explores some of these questions by providing a series of ‘rich pictures’ of indicator use, the meanings ascribed to the pictures by the group members and some insights regarding the dynamics of the groups that rest behind them and how this may have influenced the stories told by the pictures. We argue that in many ways the pictures represent a window to the understanding of the groups use of indicators. [Copyright &y& Elsevier]

Many studies report the “wonders of aggregation” and that groups (often) yield better decisions than individuals. Can this “wisdom of crowds”-effect be used to forecast elections? Forecasting models in first-past-the-post systems need to translate vote shares into seat shares by some formula; however, the seat–vote ratio alters from election to election. To circumvent this problem, this paper proposes citizen forecasting, which aggregates citizens’ local expectations to directly forecast constituencies. Using data from the 2010 British Election Study, this paper finds (1) that groups are better forecasters than individuals, (2) that citizen forecasting correctly predicts a hung parliament, and (3) that marginality and group size are important predictors for “getting it right”. [Copyright &y& Elsevier]

Abstract: The complexity of policy decision-making raises the need to elicit opinions from large and heterogeneous groups of stakeholders with broad and diverse sets of expertise. Existing options for elicitation include small face-to-face panels of experts by using the Nominal Group Technique (NGT), large Delphi panels whose members do not interact with each other face-to-face, and crowdsourcing, which involves an open call for input issued to a large community of people. In an attempt to close the gap between the practical needs of policy makers and the methodological challenges associated with eliciting opinions of large, diverse, and distributed groups, we have developed a new online elicitation system and methodology called ExpertLens. By optimizing the direct interactions of NGT with the larger number of Delphi participants and the wisdom of “selected crowds,” our approach is designed to save on the costs associated with traditional expert panels, while increasing accuracy in elicitation by reducing the potential for group process losses that can occur in large, diverse, and non-collocated panels whose members interact via asynchronous online discussion boards. The ExpertLens approach is iterative, does not require participants to develop consensus, and determines what the group “thinks” by statistically analyzing data collected in all rounds of the elicitation. This paper describes the ExpertLens system and methodology, briefly discusses recent ExpertLens trials, provides conceptual arguments for why it is an appropriate model for eliciting expert opinions, illustrates its main components and analytics by using an infrastructure investment example, and discusses a research agenda for testing the underlying tenets of the ExpertLens approach. [Copyright &y& Elsevier]

Abstract: Aim: To describe systems for capturing and optimising collective knowledge and insight in areas of complexity and uncertainty in surgical oncology, with particular reference to the Delphi process and related systems. Methods: Internet search engines (Google, Google Scholar) and four databases (SCOPUS, PubMed, Medline and Embase) were searched to find English language articles on the use of The Delphi Process and related systems in surgical oncology, using a variety of search terms. Findings: There are a number of established systems for co-opting group knowledge and facilitating collective decision-making. These find applications in commerce, industry, government and defence. They have also been applied to problems in surgical oncology, for example using the Delphi process to optimise the management of colorectal cancers and metastases. Conclusions: Collective decision making tools find practical applications in the allocation of resources and in clinical decision making in fields of surgical oncology practice where there is a wide range of evidence and expert opinion. Such methodologies set new standards for the collating of professional expertise and for the writing of “best clinical practice” guidelines in the cancer subspecialities. [Copyright &y& Elsevier]

Ever since Galton's classic demonstration of the wisdom of crowds in estimating the weight of a slaughtered ox, scholars of the mind and the public alike have been fascinated by the counterintuitive accuracy achieved by simply averaging a number of people's estimates. Surprisingly, individuals can, to some extent, harness the wisdom of crowds within the confines of their own mind by averaging self-generated, nonredundant estimates. [ABSTRACT FROM AUTHOR]

Researchers across cognitive science, economics, and evolutionary biology have studied the ubiquitous phenomenon of social learning-the use of information about other people's decisions to make your own. Decision-making with the benefit of the accumulated knowledge of a community can result in superior decisions compared to what people can achieve alone. However, groups of people face two coupled challenges in accumulating knowledge to make good decisions: (1) aggregating information and (2) addressing an informational public goods problem known as the exploration-exploitation dilemma. Here, we show how a Bayesian social sampling model can in principle simultaneously optimally aggregate information and nearly optimally solve the exploration-exploitation dilemma. The key idea we explore is that Bayesian rationality at the level of a population can be implemented through a more simplistic heuristic social learning mechanism at the individual level. This simple individual-level behavioral rule in the context of a group of decision-makers functions as a distributed algorithm that tracks a Bayesian posterior in population-level statistics. We test this model using a large-scale dataset from an online financial trading platform. [ABSTRACT FROM AUTHOR]

Abstract: Simulation use in healthcare is growing at a rapid pace. It is time to evolve our communication techniques from the e-mail distribution lists to instant contact with the wisdom of crowds. Twitter is one tool to tap the expertise of a broad range of people to take simulation to the next level. [Copyright &y& Elsevier]