A 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.
Human groups can perform extraordinary accurate estimations compared to individuals by simply using the mean, median or geometric mean of the individual estimations [Galton 1907, Surowiecki 2005, Page 2008]. However, this is true only for some tasks and in general these collective estimations show strong biases. The method fails also when allowing for social interactions, which makes the collective estimation worse as individuals tend to converge to the biased result [Lorenz et al. 2011]. Here we show that there is a bright side of this apparently negative impact of social interactions into co…
The aggregation of many independent estimates can outperform the most accurate individual judgment. This centenarian finding, popularly known as the wisdom of crowds, has been applied to problems ranging from the diagnosis of cancer to financial forecasting. It is widely believed that social influence undermines collective wisdom by reducing the diversity of opinions within the crowd. Here, we show that if a large crowd is structured in small independent groups, deliberation and social influence within groups improve the crowd's collective accuracy. We asked a live crowd (N=5180) to respond to…
Social networks continuously change as new ties are created and existing ones fade. It is widely noted that our social embedding exerts a strong influence on what information we receive and how we form beliefs and make decisions. However, most empirical studies on the role of social networks in collective intelligence have overlooked the dynamic nature of social networks and its role in fostering adaptive collective intelligence. It remains unknown (1) how network structures adapt to the attributes of individuals, and (2) whether this adaptation promotes the accuracy of individual and collecti…
Beyond the Wisdom of the Crowd: How Network Topology Distorts Collective Perception
Sapienza University of Rome（罗马第一大学）；Enrico Fermi Research Center（恩里科·费米研究中心）；Sony Computer Science Laboratories - Rome（索尼计算机科学实验室-罗马）；University Tor Vergata（托尔维加塔大学）；INFN（意大利国家核物理研究所）；Complexity Science Hub（复杂性科学中心）
"Wisdom of crowds" refers to the phenomenon that the average opinion of a group of individuals on a given question can be very close to the true answer. It requires a large group diversity of opinions, but the collective error, the difference between the average opinion and the true value, has to be small. We consider a stochastic opinion dynamics where individuals can change their opinion based on the opinions of others (social influence $α$), but to some degree also stick to their initial opinion (individual conviction $β$). We then derive analytic expressions for the dynamics of the collect…
Online discussion threads are important means for individual decision-making and for aggregating collective judgments, e.g. the `wisdom of crowds'. Empirical investigations of the wisdom of crowds are currently ambivalent about the role played by social information. While some findings suggest that social information undermines crowd accuracy due to correlated judgment errors, others show that accuracy improves. We investigate experimentally the accuracy of threads in which participants make magnitude estimates of varying difficulty while seeing a varying number of previous estimates. We demon…
Can we predict top-performing products, services, or businesses by only monitoring the behavior of a small set of individuals? Although most previous studies focused on the predictive power of "hub" individuals with many social contacts, which sources of customer behavioral data are needed to address this question remains unclear, mostly due to the scarcity of available datasets that simultaneously capture individuals' purchasing patterns and social interactions. Here, we address this question in a unique, large-scale dataset that combines individuals' credit-card purchasing history with their…
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: (i) agents tend to keep their own opinion (individual conviction $…
In this study, we build on previous research to understand the conditions within which the Wisdom of the Crowd (WoC) improves or worsens as a result of showing individuals the predictions of their peers. Our main novel contributions are: 1) a dataset of unprecedented size and detail; 2) we observe the novel effect of the importance of the unimodality of the social information shown to individuals: if one does not see only one clear peak in the distribution of the crowd's predictions, the WoC is worsened after social exposure; and 3) we estimate social learning weights that we use to show that …
The average individual is typically a mediocre singer, with a rather restricted capacity to sing a melody in tune. Yet when many singers are assembled to perform collectively, the resulting melody of the crowd is suddenly perceived by an external listener as perfectly tuned -as if it was actually a choral performance- even if each individual singer is out of tune. This collective phenomenon is an example of a wisdom of crowds effect that can be routinely observed in music concerts or other social events, when a group of people spontaneously sings at unison. In this paper we rely on the psychoa…
The large availability of user provided contents on online social media facilitates people aggregation around common interests, worldviews and narratives. However, in spite of the enthusiastic rhetoric about the so called {\em wisdom of crowds}, unsubstantiated rumors -- as alternative explanation to main stream versions of complex phenomena -- find on the Web a natural medium for their dissemination. In this work we study, on a sample of 1.2 million of individuals, how information related to very distinct narratives -- i.e. main stream scientific and alternative news -- are consumed on Facebo…
Here we focus on the description of the mechanisms behind the process of information aggregation and decision making, a basic step to understand emergent phenomena in society, such as trends, information spreading or the wisdom of crowds. In many situations, agents choose between discrete options. We analyze experimental data on binary opinion choices in humans. The data consists of two separate experiments in which humans answer questions with a binary response, where one is correct and the other is incorrect. The questions are answered without and with information on the answers of some prev…
Collective phenomena in systems of interacting agents have helped us understand diverse social, ecological and biological observations. The corresponding explanations are challenged by incorrect information processing. In particular, the models typically assume a shared understanding of signals or a common truth or value system, i.e., an agreement of whether the measurement or perception of information is `right' or `wrong'. It is an open question whether a collective consensus can emerge without these conditions. Here we introduce a model of interacting agents that strive for consensus, howev…
Monitoring and reporting incorrect acts are pervasive for maintaining human cooperation, but in theory it is unclear how they influence each other. To explore their possible interactions we consider spatially structured population where individuals face the collective-risk social dilemma. In our minimal model cooperator players report defection according to the loss of their interests. In parallel we assume a monitoring institution that monitors all group member and identifies wrong behavior with a certain probability. In response to these feedbacks a sanctioning institution develops punishmen…
École Polytechnique Fédéreal de Lausanne（洛桑联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
One goal of online social recommendation systems is to harness the wisdom of crowds in order to identify high quality content. Yet the sequential voting mechanisms that are commonly used by these systems are at odds with existing theoretical and empirical literature on optimal aggregation. This literature suggests that sequential voting will promote herding---the tendency for individuals to copy the decisions of others around them---and hence lead to suboptimal content recommendation. Is there a problem with our practice, or a problem with our theory? Previous attempts at answering this questi…
In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the question of how individuals use social information, and how it affects their decisions. We report experiments performed in France and Japan, in which subjects could update their estimates after having received information from other subjects. We measure and model the impact of this social information at individual and collective scales. We observe and justify that when individuals have little prior knowledge about a quantity, the distribution of the logarithm of their …
It is very common to observe crowds of individuals solving similar problems with similar information in a largely independent manner. We argue here that crowds can become "smarter," i.e., more efficient and robust, by partially following the average opinion. This observation runs counter to the widely accepted claim that the wisdom of crowds deteriorates with social influence. The key difference is that individuals are self-interested and hence will reject feedbacks that do not improve their performance. We propose a control-theoretic methodology to compute the degree of social influence, i.e.…
