People must integrate disparate sources of information when making decisions, especially in social contexts. But information does not always flow freely. It can be constrained by social networks1,2,3 and distorted by zealots and automated bots4. Here we develop a voter game as a model system to study information flow in collective decisions. Players are assigned to competing groups (parties) and placed on an ‘influence network’ that determines whose voting intentions each player can observe. Players are incentivized to vote according to partisan interest, but also to coordinate their vote with the entire group. Our mathematical analysis uncovers a phenomenon that we call information gerrymandering: the structure of the influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes. We confirm the predicted effects of information gerrymandering in social network experiments with n = 2,520 human subjects. Furthermore, we identify extensive information gerrymandering in real-world influence networks, including online political discussions leading up to the US federal elections, and in historical patterns of bill co-sponsorship in the US Congress and European legislatures. Our analysis provides an account of the vulnerabilities of collective decision-making to systematic distortion by restricted information flow. Our analysis also highlights a group-level social dilemma: information gerrymandering can enable one party to sway decisions in its favour, but when multiple parties engage in gerrymandering the group loses its ability to reach consensus and remains trapped in deadlock.
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All data necessary to reproduce the results are available at https://github.com/jplotkin/InformationGerrymandering.
All scripts necessary to reproduce the results are available at https://github.com/jplotkin/InformationGerrymandering.
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We acknowledge funding from the Defense Advanced Research Projects Agency NGS2 program (grant D17AC00005; to A.J.S., J.B.P., M.M., A.A.A. and D.G.R.), the Ethics and Governance of Artificial Intelligence Initiative of the Miami Foundation (to D.G.R.), the Templeton World Charity Foundation and the John Templeton Foundation (to D.G.R.), the Army Research Office (grant W911NF-17-1-0083; to J.B.P.) and the David & Lucile Packard Foundation (to J.B.P.). The views, opinions, and findings expressed are those of the authors and should not be interpreted as representing the official views or policies of the Department of Defense or the US Government.
The authors declare no competing interests.
Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Peer review information Nature thanks Carl T. Bergstrom, Wei Chen and Brian Uzzi for their contribution to the peer review of this work.
This file contains details of experimental methods and pre-registration, mathematical and computational models and empirical datasets discussed in the main text.
How to gerrymander an influence network. We show how we take a set of gerrymandered electoral districts and use them to construct a fully interconnected influence network which displays information gerrymandering. The process we describe is precisely that used to construct our experimental influence networks (main text Figure 3), which produce highly skewed outcomes in the voter game despite neither team having an intrinsic advantage.