Effects of information quantity and quality on collective decisions in human groups

Dans cette thèse, nous nous sommes intéressés à l'impact de la quantité et de la qualité de l'information échangée entre individus d'un groupe sur leurs performances collectives dans deux types de tâches bien spécifiques. Dans une première série d'expériences, les sujets devaient estimer des quantités séquentiellement, et pouvaient réviser leurs estimations après avoir reçu comme information sociale l'estimation moyenne d'autres sujets. Nous contrôlions cette information sociale à l'aide de participants virtuels (dont nous contrôlions le nombre) donnant une information (dont nous contrôlions la valeur), à l'insu des sujets. Nous avons montré que lorsque les sujets ont peu de connaissance préalable sur une quantité à estimer, (les logarithmes de) leurs estimations suivent une distribution de Laplace. La médiane étant un bon estimateur du centre d'une distribution de Laplace, nous avons défini la performance collective comme la proximité de la médiane (du logarithme) des estimations à la vraie valeur. Nous avons trouvé qu'après influence sociale, et lorsque les agents virtuels fournissent une information correcte, la performance collective augmente avec la quantité d'information fournie (fraction d'agents virtuels). Nous avons aussi analysé la sensibilité à l'influence sociale des sujets, et trouvé que celle-ci augmente avec la distance entre l'estimation personnelle et l'information sociale. Ces analyses ont permis de définir 5 traits de comportement : garder son opinion, adopter celle des autres, faire un compromis, amplifier l'information sociale ou au contraire la contredire. Nos résultats montrent que les sujets qui adoptent l'opinion des autres sont ceux qui améliorent le mieux leur performance, car ils sont capables de bénéficier de l'information apportée par les agents virtuels. Nous avons ensuite utilisé ces analyses pour construire et calibrer un modèle d'estimation collective, qui reproduit quantitativement les résultats expérimentaux et prédit qu'une quantité limitée d'information incorrecte peut contrebalancer un biais cognitif des sujets consistant à sous-estimer les quantités, et ainsi améliorer la performance collective. D'autres expériences ont permis de valider cette prédiction. Dans une seconde série d'expériences, des groupes de 22 piétons devaient se séparer en clusters de la même "couleur", sans indice visuel (les couleurs étaient inconnues), après une courte période de marche aléatoire. Pour les aider à accomplir leur tâche, nous avons utilisé un système de filtrage de l'information disponible (analogue à un dispositif sensoriel tel que la rétine), prenant en entrée l'ensemble des positions et couleurs des individus, et retournant un signal sonore aux sujets (émit par des tags attachés à leurs épaules) lorsque la majorité de leurs k plus proches voisins était de l'autre couleur que la leur. La règle consistait à s'arrêter de marcher lorsque le signal stoppait. Nous avons étudié l'impact de diverses valeurs de k sur le temps et la qualité de la ségrégation, définie comme le nombre de clusters à l'instant final, par analogie avec les phénomènes de séparation de phase (une ségrégation "parfaite" correspondant à la formation de deux clusters bien distincts). Nous avons trouvé que le temps de ségrégation est optimisé pour k = 7 ~ 9, et que la qualité de la ségrégation augmente avec k jusqu'à k = 7 ~ 9 également, valeur au-delà de laquelle elle sature. Notre dispositif nous a également permis d'enregistrer les positions des piétons durant les expériences, ce qui nous a permis de caractériser et modéliser les interactions des piétons avec le bord de l'arène et entre eux durant la marche aléatoire. À l'aide d'une procédure de minimisation d'erreur, nous avons reconstruit les formes fonctionnelles précises des interactions et construit un modèle fin de mouvement collectif de piétons.In this thesis, we were interested in the impact of the quantity and quality of information ex- changed between individuals in a group on their collective performance in two very specific types of tasks. In a first series of experiments, subjects had to estimate quantities sequentially, and could revise their estimates after receiving the average estimate of other subjects as social information. We controlled this social information through virtual participants (which number we controlled) giving information (which value we controlled), unknowingly to the subjects. We showed that when subjects have little prior knowledge about a quantity to estimate, (the loga- rithms of) their estimates follow a Laplace distribution. Since the median is a good estimator of the center of a Laplace distribution, we defined collective performance as the proximity of the median (log) estimate to the true value. We found that after social influence, and when the information provided by the virtual agents is correct, the collective performance increases with the amount of information provided (fraction of virtual agents). We also analysed subjects' sensitivity to social influence, and found that it increases with the distance between personal estimate and social information. These analyses made it possible to define five behavioral traits: to keep one's opinion, to adopt that of others, to compromise, to amplify social information or to contradict it. Our results showed that the subjects who adopt the opinion of others are the ones who best improve their performance because they are able to benefit from the infor- mation provided by the virtual agents. We then used these analyses to construct and calibrate a model of collective estimation, which quantitatively reproduced the experimental results and predicted that a limited amount of incorrect information can counterbalance a cognitive bias that makes subjects underestimate quantities, and thus improve collective performance. Further experiments have validated this prediction. In a second series of experiments, groups of 22 pedestrians had to segregate into clusters of the same "color", without visual cue (the colors were unknown), after a short period of random walk. To help them accomplish their task, we used an information filtering system (analogous to a sensory device such as the retina), taking all the positions and colors of individuals in input, and returning an acoustic signal to the subjects (emitted by tags attached to their shoulders) when the majority of their k nearest neighbors was of a different color from theirs. The rule was to stop walking when the signal stopped. We studied the impact of various values of k on segregation time and quality, defined as the number of clusters at final time, by analogy with phase separation phenomena (a segregation was considered "perfect" when two distinct clusters were formed). We found that segregation time is optimized for k = 7 ~ 9, and that segregation quality increases with k up to k = 7 ~ 9 as well, value beyond which it saturates. Our device has also allowed us to record the positions of the pedestrians during the experiments, which allowed us to characterize and model the interactions of pedestrians with the border of the arena and between them during the random walk phase. Using an error minimization procedure, we were able to reconstruct the precise functional forms of the interactions and built a fine model of collective pedestrian motion

Debiasing the crowd: selectively exchanging social information improves collective decision making

Collective decision making is ubiquitous across biological systems. However, biases at the individual level can impair the quality of collective decisions. One prime bias is the human tendency to underestimate quantities. We performed estimation experiments in human groups, in which we re-wired the structure of information exchange, favouring the exchange of estimates closest to an overestimation of the median, expected to approximate the truth. We show that this re-wiring of social information exchange counteracts the underestimation bias and boosts collective decisions compared to random exchange. Underlying this result are a human tendency to herd, to trust large numbers more than small numbers, and to follow disparate social information less. We introduce a model that reproduces all the main empirical results, and predicts conditions for optimising collective decisions. Our results show that leveraging existing knowledge on biases can boost collective decision making, paving the way for combating other cognitive biases threatening collective systems

Effets de la quantité et de la qualité de l'information sur les décisions collectives dans les groupes humains

In this thesis, we were interested in the impact of the quantity and quality of information ex- changed between individuals in a group on their collective performance in two very specific types of tasks. In a first series of experiments, subjects had to estimate quantities sequentially, and could revise their estimates after receiving the average estimate of other subjects as social information. We controlled this social information through virtual participants (which number we controlled) giving information (which value we controlled), unknowingly to the subjects. We showed that when subjects have little prior knowledge about a quantity to estimate, (the loga- rithms of) their estimates follow a Laplace distribution. Since the median is a good estimator of the center of a Laplace distribution, we defined collective performance as the proximity of the median (log) estimate to the true value. We found that after social influence, and when the information provided by the virtual agents is correct, the collective performance increases with the amount of information provided (fraction of virtual agents). We also analysed subjects' sensitivity to social influence, and found that it increases with the distance between personal estimate and social information. These analyses made it possible to define five behavioral traits: to keep one's opinion, to adopt that of others, to compromise, to amplify social information or to contradict it. Our results showed that the subjects who adopt the opinion of others are the ones who best improve their performance because they are able to benefit from the infor- mation provided by the virtual agents. We then used these analyses to construct and calibrate a model of collective estimation, which quantitatively reproduced the experimental results and predicted that a limited amount of incorrect information can counterbalance a cognitive bias that makes subjects underestimate quantities, and thus improve collective performance. Further experiments have validated this prediction. In a second series of experiments, groups of 22 pedestrians had to segregate into clusters of the same "color", without visual cue (the colors were unknown), after a short period of random walk. To help them accomplish their task, we used an information filtering system (analogous to a sensory device such as the retina), taking all the positions and colors of individuals in input, and returning an acoustic signal to the subjects (emitted by tags attached to their shoulders) when the majority of their k nearest neighbors was of a different color from theirs.Dans cette thèse, nous nous sommes intéressés à l'impact de la quantité et de la qualité de l'information échangée entre individus d'un groupe sur leurs performances collectives dans deux types de tâches bien spécifiques. Dans une première série d'expériences, les sujets devaient estimer des quantités séquentiellement, et pouvaient réviser leurs estimations après avoir reçu comme information sociale l'estimation moyenne d'autres sujets. Nous contrôlions cette information sociale à l'aide de participants virtuels (dont nous contrôlions le nombre) donnant une information (dont nous contrôlions la valeur), à l'insu des sujets. Nous avons montré que lorsque les sujets ont peu de connaissance préalable sur une quantité à estimer, (les logarithmes de) leurs estimations suivent une distribution de Laplace. La médiane étant un bon estimateur du centre d'une distribution de Laplace, nous avons défini la performance collective comme la proximité de la médiane (du logarithme) des estimations à la vraie valeur. Nous avons trouvé qu'après influence sociale, et lorsque les agents virtuels fournissent une information correcte, la performance collective augmente avec la quantité d'information fournie (fraction d'agents virtuels). Nous avons aussi analysé la sensibilité à l'influence sociale des sujets, et trouvé que celle-ci augmente avec la distance entre l'estimation personnelle et l'information sociale. Ces analyses ont permis de définir 5 traits de comportement : garder son opinion, adopter celle des autres, faire un compromis, amplifier l'information sociale ou au contraire la contredire. Nos résultats montrent que les sujets qui adoptent l'opinion des autres sont ceux qui améliorent le mieux leur performance, car ils sont capables de bénéficier de l'information apportée par les agents virtuels. Nous avons ensuite utilisé ces analyses pour construire et calibrer un modèle d'estimation collective, qui reproduit quantitativement les résultats expérimentaux et prédit qu'une quantité limitée d'information incorrecte peut contrebalancer un biais cognitif des sujets consistant à sous-estimer les quantités, et ainsi améliorer la performance collective. D'autres expériences ont permis de valider cette prédiction. Dans une seconde série d'expériences, des groupes de 22 piétons devaient se séparer en clusters de la même "couleur", sans indice visuel (les couleurs étaient inconnues), après une courte période de marche aléatoire. Pour les aider à accomplir leur tâche, nous avons utilisé un système de filtrage de l'information disponible (analogue à un dispositif sensoriel tel que la rétine), prenant en entrée l'ensemble des positions et couleurs des individus, et retournant un signal sonore aux sujets (émit par des tags attachés à leurs épaules) lorsque la majorité de leurs k plus proches voisins était de l'autre couleur que la leur. La règle consistait à s'arrêter de marcher lorsque le signal stoppait

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

International audienc

Interactions between communities improve the resilience of multicultural societies

Previous work by the same authors showed that favoring openness, trust and cohesion within homogeneous communities strengthens their resilience. Here, we extend the social networks model developed there, and study the resilience of a society composed of two communities with different properties. We find that societies with more disparate communities are less resilient, while encouraging communication between communities strongly improves the resilience of the whole.National Research Foundation (NRF)Published versionThis work is an outcome of the Future Resilient Systems project at the Singapore-ETH Centre (SEC) supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) programme. Hans J. Herrmann thanks FUNCAP for financial support

Modeling the resilience of social networks to lockdowns regarding the dynamics of meetings

Modern societies are facing more numerous and diverse hazards than ever before, and at an ever-increasing pace. One way to face such catastrophes and dampen their potential harm is to enhance the resilience of social systems. Achieving such a goal requires a better understanding of the mechanisms underlying social resilience. We tackle this issue by simulating dynamic social networks based on the Jin–Girvan–Newman model, and investigate how quickly such networks recover after a lockdown. We first find that the recovery time increases with the strictness of the lockdown, but quickly saturates. Next, we study how the recovery time depends on characteristics of the network. The recovery time is independent of the network size, decreases fast with the rate of random meetings (inverse dependence), and increases with the rate of meeting break-ups. The dependence of the recovery time on the rate of social meetings depends on the maximum number of meetings occurring per time step. Our results suggest that more open, trustful and cohesive communities are more resilient.ISSN:0378-4371ISSN:1873-211

Strategic disinformation outperforms honesty in competition for social influence

Competition for social influence is a major force shaping societies, from baboons guiding their troop in different directions, to politicians competing for voters, to influencers competing for attention on social media. Social influence is invariably a competitive exercise with multiple influencers competing for it. We study which strategy maximizes social influence under competition. Applying game theory to a scenario where two advisers compete for the attention of a client, we find that the rational solution for advisers is to communicate truthfully when favored by the client, but to lie when ignored. Across seven pre-registered studies, testing 802 participants, such a strategic adviser consistently outcompeted an honest adviser. Strategic dishonesty outperformed truth-telling in swaying individual voters, the majority vote in anonymously voting groups, and the consensus vote in communicating groups. Our findings help explain the success of political movements that thrive on disinformation, and vocal underdog politicians with no credible program

Strategic disinformation outperforms honesty in competition for social influence

Competition for social influence is a major force shaping societies, from baboons guiding their troop in different directions, to politicians competing for voters, to influencers competing for attention on social media. Social influence is invariably a competitive exercise with multiple influencers competing for it. We study which strategy maximizes social influence under competition. Applying game theory to a scenario where two advisers compete for the attention of a client, we find that the rational solution for advisers is to communicate truthfully when favoured by the client, but to lie when ignored. Across seven pre-registered studies, testing 802 participants, such a strategic adviser consistently outcompeted an honest adviser. Strategic dishonesty trumped truth-telling in swaying individual voters, the majority vote in anonymously voting groups, and the consensus vote in communicating groups. Our findings help explain the success of political movements that thrive on disinformation, and vocal underdog politicians with no credible program

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

International audienceA major problem that resulted from the massive use of social media networks is the diffusion of incorrect information. However, 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, we find that incorrect social information can help a group perform better when it overestimates the true value, by partly compensating a human underestimation bias. 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