Combining advice: the weight of a dissenting opinion in the consensus

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.

Probing the Hypersalience Hypothesis-An Adapted Judge-Advisor System Tested in Individuals With Psychotic-Like Experiences

Individuals with psychotic-like experiences and psychosis gather and use information differently than controls; in particular they seek and rely on less information or over-weight currently available information. A new paradigm, the judge-advisor system, has previously been used to investigate these processes. Results showed that psychosis-prone individuals tend to seek less advice but at the same time use the available advice more. Some theoretical models, like the hypersalience of evidence-matching hypothesis, predict that psychosis-prone individuals weight recently available information to a greater extent and thus provide an explanation for increased advice-weighting scores in psychosis-prone individuals. To test this model, we adapted the previously used judge-advisor system by letting participants receive consecutively multiple pieces of advice. To meet this aim, we recruited a large MTurk community sample (N = 1,396), which we split in a group with high levels of psychotic-like experiences (at least 2 SD above the mean, n = 80) and a group with low levels of psychotic-like experiences (maximum 0.5 SD above the mean, n = 1,107), using the Community Assessment of Psychic Experiences' positive subscale. First, participants estimated five people's age based on photographs. Then, they received consecutive advice in the form of manipulated age estimates by allegedly previous participants, with outliers in some trials. After each advice, participants could adjust their estimate. This procedure allowed us to investigate how participants weighted each currently presented advice. In addition to being more confident in their final estimates and in line with our preregistered hypothesis, participants with more frequent psychotic-like experiences did weight currently available advice more than participants with less frequent psychotic-like experiences. This effect was especially pronounced in response to outliers, as fine-grained post-hoc analysis suggested. Result thus support models predicting an overcorrection in response to new incoming information and challenges an assumed general belief inflexibility in people with psychotic experiences.

Strategies for integrating disparate social information

Social information use is widespread in the animal kingdom, helping individuals rapidly acquire useful knowledge and adjust to novel circumstances. In humans, the highly interconnected world provides ample opportunities to benefit from social information but also requires navigating complex social environments with people holding disparate or conflicting views. It is, however, still largely unclear how people integrate information from multiple social sources that (dis)agree with them, and among each other. We address this issue in three steps. First, we present a judgement task in which participants could adjust their judgements after observing the judgements of three peers. We experimentally varied the distribution of this social information, systematically manipulating its variance (extent of agreement among peers) and its skewness (peer judgements clustering either near or far from the participant's judgement). As expected, higher variance among peers reduced their impact on behaviour. Importantly, observing a single peer confirming a participant's own judgement markedly decreased the influence of other-more distant-peers. Second, we develop a framework for modelling the cognitive processes underlying the integration of disparate social information, combining Bayesian updating with simple heuristics. Our model accurately accounts for observed adjustment strategies and reveals that people particularly heed social information that confirms personal judgements. Moreover, the model exposes strong inter-individual differences in strategy use. Third, using simulations, we explore the possible implications of the observed strategies for belief updating. These simulations show how confirmation-based weighting can hamper the influence of disparate social information, exacerbate filter bubble effects and deepen group polarization. Overall, our results clarify what aspects of the social environment are, and are not, conducive to changing people's minds.

Obstacles to the spread of unintuitive beliefs

Many socially significant beliefs are unintuitive, from the harmlessness of GMOs to the efficacy of vaccination, and they are acquired via deference toward individuals who are more confident, more competent or a majority. In the two-step flow model of communication, a first group of individuals acquires some beliefs through deference and then spreads these beliefs more broadly. Ideally, these individuals should be able to explain why they deferred to a given source - to provide arguments from expertise - and others should find these arguments convincing. We test these requirements using a perceptual task with participants from the US and Japan. In Experiment 1, participants were provided with first-hand evidence that they should defer to an expert, leading a majority of participants to adopt the expert's answer. However, when attempting to pass on this answer, only a minority of those participants used arguments from expertise. In Experiment 2, participants receive an argument from expertise describing the expert's competence, instead of witnessing it first-hand. This leads to a significant drop in deference compared with Experiment 1. These experiments highlight significant obstacles to the transmission of unintuitive beliefs.

Majority rules: how good are we at aggregating convergent opinions?

Mathematical models and simulations demonstrate the power of majority rules, i.e. following an opinion shared by a majority of group members. Majority opinion should be followed more when (a) the relative and absolute size of the majority grow, the members of the majority are (b) competent, and (c) benevolent, (d) the majority opinion conflicts less with our prior beliefs and (e) the members of the majority formed their opinions independently. We review the experimental literature bearing on these points. The few experiments bearing on (b) and (c) suggest that both factors are adequately taken into account. Many experiments show that (d) is also followed, with participants usually putting too much weight on their own opinion relative to that of the majority. Regarding factors (a) and (e), in contrast, the evidence is mixed: participants sometimes take into account optimally the absolute and relative size of the majority, as well as the presence of informational dependencies. In other circumstances, these factors are ignored. We suggest that an evolutionary framework can help make sense of these conflicting results by distinguishing between evolutionarily valid cues - that are readily taken into account - and non-evolutionarily valid cues - that are ignored by default.

Taking Advice for Vocational Decisions: Regulatory Fit Effects

This study examined whether taking advice is influenced by regulatory fit and whether this effect is reduced or disappears within certain attribution conditions during vocational decision making. Experiment 1 created a vocational decision setting to compare differences in decision makers’ weight of advice (WOA) between ‘eager strategy’ and ‘vigilant strategy’ advice conditions. Results showed no significant main effect of regulatory orientation or advice strategy, but there was a significant interaction. The WOA value, with fit between regulatory focus and advice strategy, was higher than with a fit violation. Experiment 2 examined whether the regulatory fit effect is reduced or disappears within attribution conditions during vocational decision making. Results showed job seekers more easily take others’ advice under the fit condition, and a significant interaction existed between regulatory fit and attribution. Thus, attribution could reduce the influence of the regulatory fit effect. Implications for vocational consultants, job seekers, and advisors are also discussed.

How social information can improve estimation accuracy in human groups

In our digital and connected societies, the development of social networks, online shopping, and reputation systems raises the questions 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 estimates is close to a Cauchy distribution. We find that social influence helps the group improve its properly defined collective accuracy. We quantify the improvement of the group estimation when additional controlled and reliable information is provided, unbeknownst to the subjects. We show that subjects' sensitivity to social influence permits us to define five robust behavioral traits and increases with the difference between personal and group estimates. We then use our data to build and calibrate a model of collective estimation to analyze the impact on the group performance of the quantity and quality of information received by individuals. The model quantitatively reproduces the distributions of estimates and the improvement of collective performance and accuracy observed in our experiments. Finally, our model predicts that providing a moderate amount of incorrect information to individuals can counterbalance the human cognitive bias to systematically underestimate quantities and thereby improve collective performance.

Modelling Influence and Opinion Evolution in Online Collective Behaviour

Opinion evolution and judgment revision are mediated through social influence. Based on a large crowdsourced in vitro experiment (n = 861), it is shown how a consensus model can be used to predict opinion evolution in online collective behaviour. It is the first time the predictive power of a quantitative model of opinion dynamics is tested against a real dataset. Unlike previous research on the topic, the model was validated on data which did not serve to calibrate it. This avoids to favor more complex models over more simple ones and prevents overfitting. The model is parametrized by the influenceability of each individual, a factor representing to what extent individuals incorporate external judgments. The prediction accuracy depends on prior knowledge on the participants’ past behaviour. Several situations reflecting data availability are compared. When the data is scarce, the data from previous participants is used to predict how a new participant will behave. Judgment revision includes unpredictable variations which limit the potential for prediction. A first measure of unpredictability is proposed. The measure is based on a specific control experiment. More than two thirds of the prediction errors are found to occur due to unpredictability of the human judgment revision process rather than to model imperfection.