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

Abstract

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.

The nature of anchor-biased estimates and its application to the wisdom of crowds

“…This could have resulted in an underestimation bias, especially for questions in which the true value was high (this point will be discussed in more detail in the General Discussion). However, as a similar bias has been reported in previous studies (Hertwig 31 et al, 2005;Izard & Dehaene, 2008;Jayles et al, 2017Jayles et al, , 2021aJayles et al, , 2021bKao et al, 2018;Lichtenstein et al, 1978), the current results provide additional evidence that people exhibit bias in numerical estimations even in the absence of anchoring effects.…”

Development of a method for assessing forecast of social impact in regional communities

“…In the article [13], an agent-based model was built using empirical distributions of personal assessments and distributions of sensitivity to social influence, as well as the effect of distance. The model was used to generate forecasts for large groups and a number of overall estimates.…”

“…The analysis of works devoted to the identification of multidirectional influence in social and virtual communities [8][9][10][11][12][13][14] showed that there are no methods for assessing the influence of both "formal" and "informal" leaders, political ties on a regional society, or an individual. Also in the cited works [8][9][10][11][12][13][14] more attention is paid to the influence of collective opinion on the individual, but not vice versa.…”

“…A mathematical model for assessing the susceptibility to social influence of elements of state institutions, media and informal leaders on regional communities from the point of view of attitudes towards political parties is formally set and presented in Fig. 4: , = ∪ ∪ ∪ IMP AA IL MM PP (13) where the matrix IMP -generalized matrix of the influence of various institutions on the corresponding age groups of the regional community.…”

Building Global Societies on Collective Intelligence: Challenges and Opportunities

“…Finally, considering the profound ways social media 'activities' and recommendation algorithms afect collective's cognition; we ind that understanding collective dynamics also requires both exploratory and explanatory analysis of the intertwined efects social-media 'activities', individuals' cognition and 'machines' have on each other. And that more 'crisis-focused' studies on CI behavior (similar to [9,13,25]) need to be conducted, to bring together insights from the emerging ields of łcollective behaviorž [3] and łmachine behaviorž [23]. Ultimately, it is the amalgamation of adoption of policies and guidelines from research, and investigation of emerging crisis disciplines involving humans and machines that could protect both collectives and their deliberation instruments against ongoing digital disruptions.…”