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.

Optimism where there is none: Asymmetric belief updating observed with valence-neutral life events.

How people update their beliefs when faced with new information is integral to everyday life. A sizeable body of literature suggests that people's belief updating is optimistically biased, such that their beliefs are updated more in response to good news than bad news. However, recent research demonstrates that findings previously interpreted as evidence of optimistic belief updating may be the result of flaws in experimental design, rather than motivated reasoning. In light of this controversy, we conduct three pre-registered variations of the standard belief updating paradigm (combined N = 300) in which we test for asymmetric belief updating with neutral, non-valenced stimuli using analytic approaches found in previous research. We find evidence of seemingly biased belief updating with neutral stimuli - results that cannot be attributed to a motivational, valence-based, optimism account - and further show that there is uninterpretable variability across samples and analytic techniques. Jointly, these results serve to highlight the methodological flaws in current optimistic belief updating research.

Reconsidering evidence of moral contagion in online social networks.

The ubiquity of social media use and the digital data traces it produces has triggered a potential methodological shift in the psychological sciences away from traditional, laboratory-based experimentation. The hope is that, by using computational social science methods to analyse large-scale observational data from social media, human behaviour can be studied with greater statistical power and ecological validity. However, current standards of null hypothesis significance testing and correlational statistics seem ill-suited to markedly noisy, high-dimensional social media datasets. We explore this point by probing the moral contagion phenomenon, whereby the use of moral-emotional language increases the probability of message spread. Through out-of-sample prediction, model comparisons and specification curve analyses, we find that the moral contagion model performs no better than an implausible XYZ contagion model. This highlights the risks of using purely correlational evidence from large observational datasets and sounds a cautionary note for psychology's merge with big data.