Gerrymandering in social networks influences collective decision-making

Information gerrymandering in social networks skews collective decision-making

Democracy requires an informed electorate. However, some technological advances change how information flows through society, with serious consequences for the democratic process. Writing in Nature , Stewart et al. use experiments and computational models to uncover a previously unrecognized obstacle to democratic decision-making.

An analysis shows that information flow between individuals in a social network can be ‘gerrymandered’ to skew perceptions of how others in the community will vote — which can alter the outcomes of elections.

social media is starting to compete with, or even replace, nationally visible conversations in print and on broadcast media with ad libitum, personalized discourse on virtual social networks. Instead of broadening their spheres of association, people gravitate towards interactions with ideologically aligned content and similarly minded individuals. Portions of a social network can thus turn into ‘filter bubbles’, in which individuals see only an algorithmically curated subset of the larger conversation. Filter bubbles reinforce political views, or even make them more extreme, and drive political polarization. Stewart and colleagues now describe a related, but distinct, way in which social-network structure can affect voting behaviour.

In geographical gerrymandering, the borders of voting districts are drawn so as to concentrate voters from the opposition party into one or a few districts, leaving the voters for the gerrymandering party in a numerical majority elsewhere5. In information gerrymandering, the way in which voters are concentrated into districts is not what matters; rather, it is the way in which the connections between them are arranged (Fig. 1). Nevertheless, like geographical gerrymandering, information gerrymandering threatens ideas about proportional representation in a democracy.

Figure 1 | Social-network structure affects voters’ perceptions. In these social networks, ten individuals favour orange and eight favour blue. Each individual has four reciprocal social connections. a, In this random network, eight individuals correctly infer from their contacts’ preferences that orange is more popular, eight infer a draw and only two incorrectly infer that blue is more popular. b, When individuals largely interact with like-minded individuals, filter bubbles arise in which all individuals believe that their party is the most popular. Voting gridlock is more likely in such situations, because no one recognizes a need to compromise. c, Stewart et al.1 describe ‘information gerrymandering’, in which the network structure skews voters’ perceptions about others’ preferences. Here, two-thirds of voters mistakenly infer that blue is more popular. This is because blue proponents strategically influence a small number of orange-preferring individuals, whereas orange proponents squander their influence on like-minded individuals who have exclusively orange-preferring contacts, or on blue-preferring individuals who have enough blue-preferring contacts to remain unswayed.

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