Information gerrymandering and undemocratic decisions

Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Strategies and payoffs in the voter game.
Fig. 2: Influence assortment and information gerrymandering.
Fig. 3: Undemocratic outcomes and polarization in the voter game.
Fig. 4: Information gerrymandering on simulated influence networks and empirical networks of political discourse.

Data availability

All data necessary to reproduce the results are available at https://github.com/jplotkin/InformationGerrymandering.

Code availability

All scripts necessary to reproduce the results are available at https://github.com/jplotkin/InformationGerrymandering.

References

  1. 1.

    Barberá, P., Jost, J. T., Nagler, J., Tucker, J. A. & Bonneau, R. Tweeting from left to right: is online political communication more than an echo chamber? Psychol. Sci. 26, 1531–1542 (2015).

  2. 2.

    Del Vicario, M. et al. The spreading of misinformation online. Proc. Natl Acad. Sci. USA 113, 554–559 (2016).

  3. 3.

    Bakshy, E., Messing, S. & Adamic, L. A. Exposure to ideologically diverse news and opinion on Facebook. Science 348, 1130–1132 (2015).

  4. 4.

    Woolley, S. C. Automating power: social bot interference in global politics. First Monday 21, 4 (2016).

  5. 5.

    Lazer, D. M. J. et al. The science of fake news. Science 359, 1094–1096 (2018).

  6. 6.

    Vosoughi, S., Roy, D. & Aral, S. The spread of true and false news online. Science 359, 1146–1151 (2018).

  7. 7.

    Matz, S. C., Kosinski, M., Nave, G. & Stillwell, D. J. Psychological targeting as an effective approach to digital mass persuasion. Proc. Natl Acad. Sci. USA 114, 12714–12719 (2017).

  8. 8.

    Bond, R. M. et al. A 61-million-person experiment in social influence and political mobilization. Nature 489, 295–298 (2012).

  9. 9.

    Pennycook, G. & Rand, D. G. Lazy, not biased: susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning. Cognition 188, 39–50 (2019).

  10. 10.

    Iyengar, S., Sood, G. & Lelkes, Y. Affect, not ideology: a social identity perspective on polarization. Public Opin. Q. 76, 405–431 (2012).

  11. 11.

    Crockett, M. J. Moral outrage in the digital age. Nat. Hum. Behav. 1, 769–771 (2017).

  12. 12.

    Kearns, M., Judd, S., Tan, J. & Wortman, J. Behavioral experiments on biased voting in networks. Proc. Natl Acad. Sci. USA 106, 1347–1352 (2009).

  13. 13.

    Couzin, I. D. et al. Uninformed individuals promote democratic consensus in animal groups. Science 334, 1578–1580 (2011).

  14. 14.

    Newport, F. Americans favor compromise to get things done in Washington. Gallup https://news.gallup.com/poll/220265/americans-favor-compromise-things-done-washington.aspx (2017).

  15. 15.

    Stephanopoulos, N. O. & McGhee, E. Partisan gerrymandering and the efficiency gap. Univ. Chic. Law Rev. 82, 831–900 (2015).

  16. 16.

    Lewis, J. B. & Poole, K. T. Measuring bias and uncertainty in ideal point estimates via the parametric bootstrap. Polit. Anal. 12, 105–127 (2004).

  17. 17.

    Guilbeault, D., Becker, J. & Centola, D. Social learning and partisan bias in the interpretation of climate trends. Proc. Natl Acad. Sci. USA 115, 9714–9719 (2018).

  18. 18.

    Gavrilets, S. & Richerson, P. J. Collective action and the evolution of social norm internalization. Proc. Natl Acad. Sci. USA 114, 6068–6073 (2017).

  19. 19.

    Bail, C. A. et al. Exposure to opposing views on social media can increase political polarization. Proc. Natl Acad. Sci. USA 115, 9216–9221 (2018).

  20. 20.

    Weber, E. U. & Stern, P. C. Public understanding of climate change in the United States. Am. Psychol. 66, 315–328 (2011).

  21. 21.

    Pennycook, G. & Rand, D. G. Fighting misinformation on social media using crowdsourced judgments of news source quality. Proc. Natl Acad. Sci. USA 116, 2521–2526 (2019).

  22. 22.

    Shirado, H. & Christakis, N. A. Locally noisy autonomous agents improve global human coordination in network experiments. Nature 545, 370–374 (2017).

  23. 23.

    Hardin, G. The tragedy of the commons. The population problem has no technical solution; it requires a fundamental extension in morality. Science 162, 1243–1248 (1968).

  24. 24.

    Rand, D. G. & Nowak, M. A. Human cooperation. Trends Cogn. Sci. 17, 413–425 (2013).

  25. 25.

    Fowler, J. H. Legislative cosponsorship networks in the US House and Senate. Soc. Networks 28, 454–465 (2006).

  26. 26.

    Briatte, F. Network patterns of legislative collaboration in twenty parliaments. Netw. Sci. 4, 266–271 (2016).

  27. 27.

    Adamic, L. A. & Glance, N. The political blogosphere and the 2004 U.S. election: divided they blog. In Proc. 3rd International Workshop on Link Discovery 36–43 (ACM, 2005).

  28. 28.

    Conover, M. et al. Political polarization on Twitter. In ICWSM 89–96 (2011).

  29. 29.

    Faris, R. et al. Partisanship, Propaganda, and Disinformation: Online Media and the 2016 U.S. Presidential Election. Berkman Klein Center Research Publication 2017-6 https://ssrn.com/abstract=3019414 (2017).

Download references

Acknowledgements

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.

Author information

A.J.S., D.G.R., M.D. and J.B.P. conceived the project. A.J.S., D.G.R., M.D. and J.B.P. developed the model. A.J.S. ran the simulations with input from J.B.P. and D.G.R. A.J.S., M.M., D.G.R. and J.B.P. designed the experiments. M.M. and A.A.A. ran the experiments. A.J.S., M.M. and J.B.P. analysed the experimental data with input from D.G.R. and A.A.A. A.J.S. analysed the empirical networks with input from D.G.R. and J.B.P. A.J.S. and J.B.P. wrote the paper with input from D.G.R., M.M., A.A.A. and M.D.

Correspondence to Alexander J. Stewart.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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.

Supplementary information

Supplementary Information

This file contains details of experimental methods and pre-registration, mathematical and computational models and empirical datasets discussed in the main text.

Reporting Summary

Video 1

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.