Guest, O;
Kanayet, FJ;
Love, BC;
(2019)
Cognitive Capacity Limits and Electoral Districting.
Journal of Computational Social Science
, 2
(2)
pp. 119-131.
10.1007/s42001-019-00053-9.
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Abstract
Partisan gerrymandering poses a threat to democracy. Moreover, the complexity of the districting task may exceed human capacities. One potential solution is using computational models to automate the districting process by optimizing objective and open criteria, such as how spatially compact districts are. We formulated one such model that minimized pairwise distance between voters within a district. Using US Census Bureau data, we confirmed our prediction that the difference in compactness between the computed and actual districts would be greatest for states that are large and therefore difficult for humans to properly district given their limited capacities. The computed solutions highlighted differences in how humans and machines solve this task with machine solutions more fully optimized and displaying emergent properties not evident in human solutions. These results suggest a division of labour in which humans debate and formulate districting criteria whereas machines optimize the criteria to draw the district boundaries. We discuss how criteria can be expanded beyond notions of compactness to include other factors, such as respecting municipal boundaries, historic communities, relevant legislation, etc.
| Type: | Article |
|---|---|
| Title: | Cognitive Capacity Limits and Electoral Districting |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1007/s42001-019-00053-9 |
| Publisher version: | http://dx.doi.org/10.1007/s42001-019-00053-9 |
| Language: | English |
| Additional information: | Copyright information © The Author(s) 2019. Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
| Keywords: | Gerrymandering, Computational redistricting, Weighted k-means, Cognitive limitations |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Experimental Psychology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10079875 |
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