TY - GEN
T1 - Big City vs. The great Outdoors
T2 - 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
AU - Borodin, Allan
AU - Lev, Omer
AU - Shah, Nisarg
AU - Strangway, Tyrone
N1 - Publisher Copyright: © 2018 International Joint Conferences on Artificial Intelligence. All right reserved.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Gerrymandering is the process by which parties manipulate boundaries of electoral districts in order to maximize the number of districts they can win. Demographic trends show an increasingly strong correlation between residence and party affiliation; some party's supporters congregate in cities, while others stay in more rural areas. We investigate both theoretically and empirically the effect of this trend on a party's ability to gerrymander in a two-party model (“urban party” and “rural party”). Along the way, we propose a definition of the gerrymandering power of a party, and an algorithmic approach for near-optimal gerrymandering in large instances. Our results suggest that beyond a fairly small concentration of urban party's voters, the gerrymandering power of a party depends almost entirely on the level of concentration, and not on the party's share of the population. As partisan separation grows, the gerrymandering power of both parties converge so that each party can gerrymander to get only slightly more than what its voting share warrants, bringing about, ultimately, a more representative outcome. Moreover, there seems to be an asymmetry between the gerrymandering power of the parties, with the rural party being more capable of gerrymandering.
AB - Gerrymandering is the process by which parties manipulate boundaries of electoral districts in order to maximize the number of districts they can win. Demographic trends show an increasingly strong correlation between residence and party affiliation; some party's supporters congregate in cities, while others stay in more rural areas. We investigate both theoretically and empirically the effect of this trend on a party's ability to gerrymander in a two-party model (“urban party” and “rural party”). Along the way, we propose a definition of the gerrymandering power of a party, and an algorithmic approach for near-optimal gerrymandering in large instances. Our results suggest that beyond a fairly small concentration of urban party's voters, the gerrymandering power of a party depends almost entirely on the level of concentration, and not on the party's share of the population. As partisan separation grows, the gerrymandering power of both parties converge so that each party can gerrymander to get only slightly more than what its voting share warrants, bringing about, ultimately, a more representative outcome. Moreover, there seems to be an asymmetry between the gerrymandering power of the parties, with the rural party being more capable of gerrymandering.
UR - http://www.scopus.com/inward/record.url?scp=85055675014&partnerID=8YFLogxK
U2 - https://doi.org/10.24963/ijcai.2018/14
DO - https://doi.org/10.24963/ijcai.2018/14
M3 - Conference contribution
T3 - IJCAI International Joint Conference on Artificial Intelligence
SP - 98
EP - 104
BT - Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
A2 - Lang, Jerome
Y2 - 13 July 2018 through 19 July 2018
ER -