@conference {1525299,
	title = {The Relationship between Gerrymandering Classification and Voter Incentives},
	booktitle = {AI for Social Good Workshop},
	year = {2020},
	abstract = {
	Gerrymandering is the process of drawing electoral district maps in order to manipulate the outcomes of elections. Increasingly, computers are involved in both drawing biased districts and attempts to measure and regulate this practice. The most highprofile proposals to measure partisan gerrymandering use past voting data to classify a map as gerrymandered (or not). Prior work studies the ability of these metrics to detect gerrymandering, but does not explore how the metrics could affect voter behavior or be circumvented via strategic voting. We show that using past voting data for this classification can affect strategyproofness by introducing a game which models the iterative sequence of voting and redrawing districts under regulation that bans outlier maps. In experiments, we show that a heuristic can find strategies for this game including on real North Carolin maps and voting data. Finally, we address questions from a recent US Supreme Court case that relate to our model. This is a summary of {\textquotedblleft}Meddling Metrics: the Effects of Measuring and Constraining Partisan Gerrymandering on Voter Incentives{\textquotedblright} appearing in EC2020



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},
	author = {Brian Brubach and Aravind Srinivasan and Shawn Zhao}
}
