Counteracting estimation bias and social influence to improve the wisdom of crowds

Stakeholder diversity matters: employing the wisdom of crowds for data-poor fisheries assessments

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.

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.

Individual-centralized seeding strategy for influence maximization in information-limited networks

Peer effects can directly or indirectly rely on interaction networks to drive people to follow ideas or behaviours triggered by a few individuals, and such effects can be largely improved by targeting the so-called influential individuals. In this article, we study the current most promising seeding strategy used in field experiments, the one-hop strategy, where the underlying interaction networks are generally too impractical or prohibitively expensive to be obtained, and propose an individual-centralized seeding approach to target influential seeds in information-limited networks. The presented strategy works by reasonable follow-up questions to respondents, such as Who do you think has more connections/friends?, and constructs the seeding set by those nodes with the most nominations. In this manner, the proposed method could acquire more information about the studied interaction network from the inference of respondents without surveying additional individuals. We evaluate our strategy on networks from various experimental datasets. Results show that the obtained seeds are much more influential compared to the one-hop strategy and other methods. We also show how the proposed approach could be implemented in field studies and potentially provide better interventions in real scenarios.

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.

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 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.

Elicitation and aggregation of multimodal estimates improve wisdom of crowd effects on ordering tasks

We present a wisdom of crowds study where participants are asked to order a small set of images based on the number of dots they contain and then to guess the respective number of dots in each image. We test two input elicitation interfaces-one elicits the two modalities of estimates jointly and the other independently. We show that the latter interface yields higher quality estimates, even though the multimodal estimates tend to be more self-contradictory. The inputs are aggregated via optimization and voting-rule based methods to estimate the true ordering of a larger universal set of images. We demonstrate that the quality of collective estimates from the simpler yet more computationally-efficient voting methods is comparable to that achieved by the more complex optimization model. Lastly, we find that using multiple modalities of estimates from one group yields better collective estimates compared to mixing numerical estimates from one group with the ordinal estimates from a different group.

Citizen science to approach machine learning to society: Detecting loneliness in older adults

Background

Even if we are not aware of it, machine learning techniques are part of our daily lives. It is of the utmost interest that citizens become familiar with the use of these techniques and discover their potential to solve everyday problems.

Objective and Methods

In this article, we describe the methodology and results of a highly replicable citizen science project that allows citizens to get closer to the scientific process and understand the potential of machine learning to solve a social problem of interest to them. For this purpose, we have chosen a problem of social relevance in contemporary societies, namely the detection of loneliness in older adults. Citizens are challenged to apply machine learning techniques to identify levels of loneliness from natural language.

Results

The results of this project suggest that citizens are willing to engage in science when the challenges posed are of social interest to them. A total of 1517 citizens actively engaged in the project. A database containing 1112 texts about loneliness expressions was collected. An accuracy of 83.12% using the logistic regression algorithm and 62.23% accuracy when using the Naïve Bayes algorithm was reached in detecting loneliness from texts.

Conclusions

Detecting loneliness using machine learning techniques is an attractive and relevant topic that allows citizens to be involved in science and introduces them to machine learning practices. The methodology of this project can be replicated in other places around the world.

Meta-Analysis of COVID-19 Metabolomics Identifies Variations in Robustness of Biomarkers

The global COVID-19 pandemic resulted in widespread harms but also rapid advances in vaccine development, diagnostic testing, and treatment. As the disease moves to endemic status, the need to identify characteristic biomarkers of the disease for diagnostics or therapeutics has lessened, but lessons can still be learned to inform biomarker research in dealing with future pathogens. In this work, we test five sets of research-derived biomarkers against an independent targeted and quantitative Liquid Chromatography-Mass Spectrometry metabolomics dataset to evaluate how robustly these proposed panels would distinguish between COVID-19-positive and negative patients in a hospital setting. We further evaluate a crowdsourced panel comprising the COVID-19 metabolomics biomarkers most commonly mentioned in the literature between 2020 and 2023. The best-performing panel in the independent dataset-measured by F1 score (0.76) and AUROC (0.77)-included nine biomarkers: lactic acid, glutamate, aspartate, phenylalanine, β-alanine, ornithine, arachidonic acid, choline, and hypoxanthine. Panels comprising fewer metabolites performed less well, showing weaker statistical significance in the independent cohort than originally reported in their respective discovery studies. Whilst the studies reviewed here were small and may be subject to confounders, it is desirable that biomarker panels be resilient across cohorts if they are to find use in the clinic, highlighting the importance of assessing the robustness and reproducibility of metabolomics analyses in independent populations.

Analysis of noise and bias errors in intelligence information systems

An intelligence information system (IIS) is a particular kind of information systems (IS) devoted to the analysis of intelligence relevant to national security. Professional and military intelligence analysts play a key role in this, but their judgments can be inconsistent, mainly due to noise and bias. The team-oriented aspects of the intelligence analysis process complicates the situation further. To enable analysts to achieve better judgments, the authors designed, implemented, and validated an innovative IIS for analyzing UK Military Signals Intelligence (SIGINT) data. The developed tool, the Team Information Decision Engine (TIDE), relies on an innovative preference learning method along with an aggregation procedure that permits combining scores by individual analysts into aggregated scores. This paper reports on a series of validation trials in which the performance of individual and team-oriented analysts was accessed with respect to their effectiveness and efficiency. Results show that the use of the developed tool enhanced the effectiveness and efficiency of intelligence analysis process at both individual and team levels.

The network science of collective intelligence

In the last few years, breakthroughs in computational and experimental techniques have produced several key discoveries in the science of networks and human collective intelligence. This review presents the latest scientific findings from two key fields of research: collective problem-solving and the wisdom of the crowd. I demonstrate the core theoretical tensions separating these research traditions and show how recent findings offer a new synthesis for understanding how network dynamics alter collective intelligence, both positively and negatively. I conclude by highlighting current theoretical problems at the forefront of research on networked collective intelligence, as well as vital public policy challenges that require new research efforts.

The distribution of initial estimates moderates the effect of social influence on the wisdom of the crowd

Whether, and under what conditions, groups exhibit “crowd wisdom” has been a major focus of research across the social and computational sciences. Much of this work has focused on the role of social influence in promoting the wisdom of the crowd versus leading the crowd astray and has resulted in conflicting conclusions about how social network structure determines the impact of social influence. Here, we demonstrate that it is not enough to consider the network structure in isolation. Using theoretical analysis, numerical simulation, and reanalysis of four experimental datasets (totaling 2885 human subjects), we find that the wisdom of crowds critically depends on the interaction between (i) the centralization of the social influence network and (ii) the distribution of the initial individual estimates. By adopting a framework that integrates both the structure of the social influence and the distribution of the initial estimates, we bring previously conflicting results under one theoretical framework and clarify the effects of social influence on the wisdom of crowds.

The motor Wisdom of the Crowd

Wisdom of the Crowd is the aggregation of many individual estimates to obtain a better collective one. Because of its enormous social potential, this effect has been thoroughly investigated, but predominantly on tasks that involve rational thinking (such as estimating a number). Here we tested this effect in the context of drawing geometrical shapes, which still enacts cognitive processes but mainly involves visuomotor control. We asked more than 700 school students to trace five patterns shown on a touchscreen and then aggregated their individual trajectories to improve the match with the original pattern. Our results show the characteristics of the strongest examples of Wisdom of the Crowd. First, the aggregate trajectory can be up to 5 times more accurate than the individual ones. Second, this great improvement requires aggregating trajectories from different individuals (rather than trials from the same individual). Third, the aggregate trajectory outperforms more than 99% of individual trajectories. Fourth, while older individuals outperform younger ones, a crowd of young individuals outperforms the average older one. These results demonstrate for the first time Wisdom of the Crowd in the realm of motor control, opening the door to further studies of human and also animal behavioural trajectories and their mechanistic underpinnings.

Designing and Implementing Deliberative Processes for Health Technology Assessment: A Good Practices Report of a Joint HTAi/ISPOR Task Force

Objectives

Deliberative processes for health technology assessment (HTA) are intended to facilitate participatory decision making, using discussion and open dialogue between stake-holders. Increasing attention is being given to deliberative processes, but guidance is lacking for those who wish to design or use them. Health Technology Assessment International (HTAi) and ISPOR—The Professional Society for Health Economics and Outcomes Research initiated a joint Task Force to address this gap.

Methods

The joint Task Force consisted of fifteen members with different backgrounds, perspectives, and expertise relevant to the field. It developed guidance and a checklist for deliberative processes for HTA. The guidance builds upon the few, existing initiatives in the field, as well as input from the HTA community following an established consultation plan. In addition, the guidance was subject to two rounds of peer review.

Results

A deliberative process for HTA consists of procedures, activities, and events that support the informed and critical examination of an issue and the weighing of arguments and evidence to guide a subsequent decision. Guidance and an accompanying checklist are provided for (i) developing the governance and structure of an HTA program and (ii) informing how the various stages of an HTA process might be managed using deliberation.

Conclusions

The guidance and the checklist contain a series of questions, grouped by six phases of a model deliberative process. They are offered as practical tools for those wishing to establish or improve deliberative processes for HTA that are fit for local contexts. The tools can also be used for independent scrutiny of deliberative processes.

Designing and Implementing Deliberative Processes for Health Technology Assessment: A Good Practices Report of a Joint HTAi/ISPOR Task Force

OBJECTIVES

Deliberative processes for health technology assessment (HTA) are intended to facilitate participatory decision making, using discussion and open dialogue between stakeholders. Increasing attention is being given to deliberative processes, but guidance is lacking for those who wish to design or use them. Health Technology Assessment International (HTAi) and ISPOR-The Professional Society for Health Economics and Outcomes Research initiated a joint Task Force to address this gap.

METHODS

The joint Task Force consisted of 15 members with different backgrounds, perspectives, and expertise relevant to the field. It developed guidance and a checklist for deliberative processes for HTA. The guidance builds upon the few, existing initiatives in the field, as well as input from the HTA community following an established consultation plan. In addition, the guidance was subject to 2 rounds of peer review.

RESULTS

A deliberative process for HTA consists of procedures, activities, and events that support the informed and critical examination of an issue and the weighing of arguments and evidence to guide a subsequent decision. Guidance and an accompanying checklist are provided for (i) developing the governance and structure of an HTA program and (ii) informing how the various stages of an HTA process might be managed using deliberation.

CONCLUSIONS

The guidance and the checklist contain a series of questions, grouped by 6 phases of a model deliberative process. They are offered as practical tools for those wishing to establish or improve deliberative processes for HTA that are fit for local contexts. The tools can also be used for independent scrutiny of deliberative processes.

Prefrontal control of social influence in risk decision making

To optimize our decisions, we may change our mind by utilizing social information. Here, we examined how changes of mind were modulated by Social Misalignment Sensitivity (SMS), egocentric tendency, and decision preferences in a decision-making paradigm including both risk and social information. Combining functional magnetic resonance imaging with computational modeling, we showed that both SMS and egocentric tendency modulated changes of mind under the influence of social information. While SMS was represented in the dorsal anterior cingulate cortex (dACC) and superior parietal gyrus (SPG) in the socially aligned situation, a distributed brain network was activated in the misaligned condition, including not only the dACC and SPG but also superior frontal gyrus and precuneus. These results suggest that SMS is related to a monitoring brain system, the scope of which varies according to the level of misalignment with social majority. The dorsolateral prefrontal cortex selectively interacted with SMS among the participants with a low switching threshold, indicating that its regulation on SMS may be sensitive to inter-individual variation. Our findings highlight the predominant roles of SMS and the prefrontal control system towards changes of mind under social influence.

Do social identity and cognitive diversity correlate in environmental stakeholders? A novel approach to measuring cognitive distance within and between groups

Groups with higher cognitive diversity, i.e. variations in how people think and solve problems, are thought to contribute to improved performance in complex problem-solving. However, embracing or even engineering adequate cognitive diversity is not straightforward and may even jeopardize social inclusion. In response, those that want to promote cognitive diversity might make a simplified assumption that there exists a link between identity diversity, i.e. range of social characteristics, and variations in how people perceive and solve problems. If this assumption holds true, incorporating diverse identities may concurrently achieve cognitive diversity to the extent essential for complex problem-solving, while social inclusion is explicitly acknowledged. However, currently there is a lack of empirical evidence to support this hypothesis in the context of complex social-ecological systems-a system wherein human and environmental dimensions are interdependent, where common-pool resources are used or managed by multiple types of stakeholders. Using a fisheries example, we examine the relationship between resource stakeholders' identities and their cognitive diversity. We used cognitive mapping techniques in conjunction with network analysis to measure cognitive distances within and between stakeholders of various social types (i.e., identities). Our results empirically show that groups with higher identity diversity also demonstrate more cognitive diversity, evidenced by disparate characteristics of their cognitive maps that represent their understanding of fishery dynamics. These findings have important implications for sustainable management of common-pool resources, where the inclusion of diverse stakeholders is routine, while our study shows it may also achieve higher cognitive coverage that can potentially lead to more complete, accurate, and innovative understanding of complex resource dynamics.

Crowd control: Reducing individual estimation bias by sharing biased social information

Cognitive biases are widespread in humans and animals alike, and can sometimes be reinforced by social interactions. One prime bias in judgment and decision-making is the human tendency to underestimate large quantities. Previous research on social influence in estimation tasks has generally focused on the impact of single estimates on individual and collective accuracy, showing that randomly sharing estimates does not reduce the underestimation bias. Here, we test a method of social information sharing that exploits the known relationship between the true value and the level of underestimation, and study if it can counteract the underestimation bias. We performed estimation experiments in which participants had to estimate a series of quantities twice, before and after receiving estimates from one or several group members. Our purpose was threefold: to study (i) whether restructuring the sharing of social information can reduce the underestimation bias, (ii) how the number of estimates received affects the sensitivity to social influence and estimation accuracy, and (iii) the mechanisms underlying the integration of multiple estimates. Our restructuring of social interactions successfully countered the underestimation bias. Moreover, we find that sharing more than one estimate also reduces the underestimation bias. Underlying our results are a human tendency to herd, to trust larger estimates than one's own more than smaller estimates, and to follow disparate social information less. Using a computational modeling approach, we demonstrate that these effects are indeed key to explain the experimental results. Overall, our results show that existing knowledge on biases can be used to dampen their negative effects and boost judgment accuracy, paving the way for combating other cognitive biases threatening collective systems.

Impact of sharing full versus averaged social information on social influence and estimation accuracy

The recent developments of social networks and recommender systems have dramatically increased the amount of social information shared in human communities, challenging the human ability to process it. As a result, sharing aggregated forms of social information is becoming increasingly popular. However, it is unknown whether sharing aggregated information improves people's judgments more than sharing the full available information. Here, we compare the performance of groups in estimation tasks when social information is fully shared versus when it is first averaged and then shared. We find that improvements in estimation accuracy are comparable in both cases. However, our results reveal important differences in subjects' behaviour: (i) subjects follow the social information more when receiving an average than when receiving all estimates, and this effect increases with the number of estimates underlying the average; (ii) subjects follow the social information more when it is higher than their personal estimate than when it is lower. This effect is stronger when receiving all estimates than when receiving an average. We introduce a model that sheds light on these effects, and confirms their importance for explaining improvements in estimation accuracy in all treatments.

Accuracy-Risk Trade-Off Due to Social Learning in Crowd-Sourced Financial Predictions

A critical question relevant to the increasing importance of crowd-sourced-based finance is how to optimize collective information processing and decision-making. Here, we investigate an often under-studied aspect of the performance of online traders: beyond focusing on just accuracy, what gives rise to the trade-off between risk and accuracy at the collective level? Answers to this question will lead to designing and deploying more effective crowd-sourced financial platforms and to minimizing issues stemming from risk such as implied volatility. To investigate this trade-off, we conducted a large online Wisdom of the Crowd study where 2037 participants predicted the prices of real financial assets (S&P 500, WTI Oil and Gold prices). Using the data collected, we modeled the belief update process of participants using models inspired by Bayesian models of cognition. We show that subsets of predictions chosen based on their belief update strategies lie on a Pareto frontier between accuracy and risk, mediated by social learning. We also observe that social learning led to superior accuracy during one of our rounds that occurred during the high market uncertainty of the Brexit vote.

The diversity bonus in pooling local knowledge about complex problems

Recently, theoreticians have hypothesized that diverse groups, as opposed to groups that are homogeneous, may have relative merits [S. E. Page, The Diversity Bonus (2019)]-all of which lead to more success in solving complex problems. As such, understanding complex, intertwined environmental and social issues may benefit from the integration of diverse types of local expertise. However, efforts to support this hypothesis have been frequently made through laboratory-based or computational experiments, and it is unclear whether these discoveries generalize to real-world complexities. To bridge this divide, we combine an Internet-based knowledge elicitation technique with theoretical principles of collective intelligence to design an experiment with local stakeholders. Using a case of striped bass fisheries in Massachusetts, we pool the local knowledge of resource stakeholders represented by graphical cognitive maps to produce a causal model of complex social-ecological interdependencies associated with fisheries ecosystems. Blinded reviews from a scientific expert panel revealed that the models of diverse groups outranked those from homogeneous groups. Evaluation via stochastic network analysis also indicated that a diverse group more adequately modeled complex feedbacks and interdependencies than homogeneous groups. We then used our data to run Monte Carlo experiments wherein the distributions of stakeholder-driven cognitive maps were randomly reproduced and virtual groups were generated. Random experiments also predicted that knowledge diversity improves group success, which was measured by benchmarking group models against an ecosystem-based fishery management model. We also highlight that diversity must be moderated through a proper aggregation process, leading to more complex yet parsimonious models.

Enhanced or distorted wisdom of crowds? An agent-based model of opinion formation under social influence

We propose an agent-based model of collective opinion formation to study the wisdom of crowds under social influence. The opinion of an agent is a continuous positive value, denoting its subjective answer to a factual question. The wisdom of crowds states that the average of all opinions is close to the truth, i.e., the correct answer. But if agents have the chance to adjust their opinion in response to the opinions of others, this effect can be destroyed. Our model investigates this scenario by evaluating two competing effects: (1) agents tend to keep their own opinion (individual conviction), (2) they tend to adjust their opinion if they have information about the opinions of others (social influence). For the latter, two different regimes (full information vs. aggregated information) are compared. Our simulations show that social influence only in rare cases enhances the wisdom of crowds. Most often, we find that agents converge to a collective opinion that is even farther away from the true answer. Therefore, under social influence the wisdom of crowds can be systematically wrong.

Individual error correction drives responsive self-assembly of army ant scaffolds

Human-designed infrastructures and networks relying on centralized or hierarchical control are susceptible to single-point catastrophic failure when disrupted. By contrast, most complex biological systems employ distributed control and can be more robust to perturbations. In field experiments with Eciton burchellii army ants, we show that scaffold structures, self-assembled by living ants, emerge in response to disrupted traffic on inclines, facilitating traffic flow and stemming losses of foragers and prey. Informed by our observations, we present a theoretical model based on proportional control and negative feedback, which may be relevant to many distributed systems in which group-level properties can be modified through individual error sensing and correction. The mechanism is simple, and ants only require information about their individual state.

An inherent strength of evolved collective systems is their ability to rapidly adapt to dynamic environmental conditions, offering resilience in the face of disruption. This is thought to arise when individual sensory inputs are filtered through local interactions, producing an adaptive response at the group level. To understand how simple rules encoded at the individual level can lead to the emergence of robust group-level (or distributed) control, we examined structures we call “scaffolds,” self-assembled by Eciton burchellii army ants on inclined surfaces that aid travel during foraging and migration. We conducted field experiments with wild E. burchellii colonies, manipulating the slope over which ants traversed, to examine the formation of scaffolds and their effects on foraging traffic. Our results show that scaffolds regularly form on inclined surfaces and that they reduce losses of foragers and prey, by reducing slipping and/or falling of ants, thus facilitating traffic flow. We describe the relative effects of environmental geometry and traffic on their growth and present a theoretical model to examine how the individual behaviors underlying scaffold formation drive group-level effects. Our model describes scaffold growth as a control response at the collective level that can emerge from individual error correction, requiring no complex communication among ants. We show that this model captures the dynamics observed in our experiments and is able to predict the growth—and final size—of scaffolds, and we show how the analytical solution allows for estimation of these dynamics.

The wisdom of stalemates: consensus and clustering as filtering mechanisms for improving collective accuracy

Groups of organisms, from bacteria to fish schools to human societies, depend on their ability to make accurate decisions in an uncertain world. Most models of collective decision-making assume that groups reach a consensus during a decision-making bout, often through simple majority rule. In many natural and sociological systems, however, groups may fail to reach consensus, resulting in stalemates. Here, we build on opinion dynamics and collective wisdom models to examine how stalemates may affect the wisdom of crowds. For simple environments, where individuals have access to independent sources of information, we find that stalemates improve collective accuracy by selectively filtering out incorrect decisions (an effect we call stalemate filtering). In complex environments, where individuals have access to both shared and independent information, this effect is even more pronounced, restoring the wisdom of crowds in regions of parameter space where large groups perform poorly when making decisions using majority rule. We identify network properties that tune the system between consensus and accuracy, providing mechanisms by which animals, or evolution, could dynamically adjust the collective decision-making process in response to the reward structure of the possible outcomes. Overall, these results highlight the adaptive potential of stalemate filtering for improving the decision-making abilities of group-living animals.

The impact of incorrect social information on collective wisdom in human groups

A major problem resulting from the massive use of social media is the potential spread of incorrect information. Yet, very few studies have investigated the impact of incorrect information on individual and collective decisions. We performed experiments in which participants had to estimate a series of quantities, before and after receiving social information. Unbeknownst to them, we controlled the degree of inaccuracy of the social information through 'virtual influencers', who provided some incorrect information. We find that a large proportion of individuals only partially follow the social information, thus resisting incorrect information. Moreover, incorrect information can help improve group performance more than correct information, when going against a human underestimation bias. We then design a computational model whose predictions are in good agreement with the empirical data, and sheds light on the mechanisms underlying our results. Besides these main findings, we demonstrate that the dispersion of estimates varies a lot between quantities, and must thus be considered when normalizing and aggregating estimates of quantities that are very different in nature. Overall, our results suggest that incorrect information does not necessarily impair the collective wisdom of groups, and can even be used to dampen the negative effects of known cognitive biases.

Collective Computation in Animal Fission-Fusion Dynamics

Recent work suggests that collective computation of social structure can minimize uncertainty about the social and physical environment, facilitating adaptation. We explore these ideas by studying how fission-fusion social structure arises in spider monkey (Ateles geoffroyi) groups, exploring whether monkeys use social knowledge to collectively compute subgroup size distributions adaptive for foraging in variable environments. We assess whether individual decisions to stay in or leave subgroups are conditioned on strategies based on the presence or absence of others. We search for this evidence in a time series of subgroup membership. We find that individuals have multiple strategies, suggesting that the social knowledge of different individuals is important. These stay-leave strategies provide microscopic inputs to a stochastic model of collective computation encoded in a family of circuits. Each circuit represents an hypothesis for how collectives combine strategies to make decisions, and how these produce various subgroup size distributions. By running these circuits forward in simulation we generate new subgroup size distributions and measure how well they match food abundance in the environment using transfer entropies. We find that spider monkeys decide to stay or go using information from multiple individuals and that they can collectively compute a distribution of subgroup size that makes efficient use of ephemeral sources of nutrition. We are able to artificially tune circuits with subgroup size distributions that are a better fit to the environment than the observed. This suggests that a combination of measurement error, constraint, and adaptive lag are diminishing the power of collective computation in this system. These results are relevant for a more general understanding of the emergence of ordered states in multi-scale social systems with adaptive properties-both natural and engineered.

Modular structure within groups causes information loss but can improve decision accuracy

Many animal groups exhibit signatures of persistent internal modular structure, whereby individuals consistently interact with certain groupmates more than others. In such groups, information relevant to a collective decision may spread unevenly through the group, but how this impacts the quality of the resulting decision is not well understood. Here, we explicitly model modularity within animal groups and examine how it affects the amount of information represented in collective decisions, as well as the accuracy of those decisions. We find that modular structure necessarily causes a loss of information, effectively silencing the input from a fraction of the group. However, the effect of this information loss on collective accuracy depends on the informational environment in which the decision is made. In simple environments, the information loss is detrimental to collective accuracy. By contrast, in complex environments, modularity tends to improve accuracy. This is because small group sizes typically maximize collective accuracy in such environments, and modular structure allows a large group to behave like a smaller group (in terms of its decision-making). These results suggest that in naturalistic environments containing correlated information, large animal groups may be able to exploit modular structure to improve decision accuracy while retaining other benefits of large group size.

This article is part of the theme issue ‘Liquid brains, solid brains: How distributed cognitive architectures process information’.

The wisdom of partisan crowds

Theories in favor of deliberative democracy are based on the premise that social information processing can improve group beliefs. While research on the "wisdom of crowds" has found that information exchange can increase belief accuracy on noncontroversial factual matters, theories of political polarization imply that groups will become more extreme-and less accurate-when beliefs are motivated by partisan political bias. A primary concern is that partisan biases are associated not only with more extreme beliefs, but also with a diminished response to social information. While bipartisan networks containing both Democrats and Republicans are expected to promote accurate belief formation, politically homogeneous networks are expected to amplify partisan bias and reduce belief accuracy. To test whether the wisdom of crowds is robust to partisan bias, we conducted two web-based experiments in which individuals answered factual questions known to elicit partisan bias before and after observing the estimates of peers in a politically homogeneous social network. In contrast to polarization theories, we found that social information exchange in homogeneous networks not only increased accuracy but also reduced polarization. Our results help generalize collective intelligence research to political domains.

Adolescents show collective intelligence which can be driven by a geometric mean rule of thumb

How effective groups are in making decisions is a long-standing question in studying human and animal behaviour. Despite the limited social and cognitive abilities of younger people, skills which are often required for collective intelligence, studies of group performance have been limited to adults. Using a simple task of estimating the number of sweets in jars, we show in two experiments that adolescents at least as young as 11 years old improve their estimation accuracy after a period of group discussion, demonstrating collective intelligence. Although this effect was robust to the overall distribution of initial estimates, when the task generated positively skewed estimates, the geometric mean of initial estimates gave the best fit to the data compared to other tested aggregation rules. A geometric mean heuristic in consensus decision making is also likely to apply to adults, as it provides a robust and well-performing rule for aggregating different opinions. The geometric mean rule is likely to be based on an intuitive logarithmic-like number representation, and our study suggests that this mental number scaling may be beneficial in collective decisions.