A refined complexity analysis of fair districting over graphs

Author:

Boehmer NiclasORCID,Koana Tomohiro,Niedermeier Rolf

Abstract

AbstractWe study the NP-hard Fair Connected Districting problem recently proposed by Stoica et al. [AAMAS 2020]: Partition a vertex-colored graph into k connected components (subsequently referred to as districts) so that in every district the most frequent color occurs at most a given number of times more often than the second most frequent color. Fair Connected Districting is motivated by various real-world scenarios where agents of different types, which are one-to-one represented by nodes in a network, have to be partitioned into disjoint districts. Herein, one strives for “fair districts” without any type being in a dominating majority in any of the districts. This is to e.g. prevent segregation or political domination of some political party. We conduct a fine-grained analysis of the (parameterized) computational complexity of Fair Connected Districting. In particular, we prove that it is polynomial-time solvable on paths, cycles, stars, and caterpillars, but already becomes NP-hard on trees. Motivated by the latter negative result, we perform a parameterized complexity analysis with respect to various graph parameters including treewidth, and problem-specific parameters, including, the numbers of colors and districts. We obtain a rich and diverse, close to complete picture of the corresponding parameterized complexity landscape (that is, a classification along the complexity classes FPT, XP, W[1]-hard, and para-NP-hard).

Funder

Deutsche Forschungsgemeinschaft

Technische Universität Berlin

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence

Reference50 articles.

1. Agarwal, A., Elkind, E., Gan, J., Igarashi, A., Suksompong, W., & Voudouris, A. A. (2021). Schelling games on graphs. Artificial Intelleligence 301, 103576.

2. Autry, E. A., Carter, D., Herschlag, G. J., Hunter, Z., & Mattingly, J. C. (2021). Metropolized multiscale forest recombination for redistricting. Multiscale Modelling Simulation, 19(4), 1885–1914.

3. Bachrach, Y., Lev, O., Lewenberg, Y., & Zick, Y. (2016). Misrepresentation in district voting. In Proceedings of the 25th international joint conference on artificial intelligence (IJCAI ’16). AAAI Press, (pp. 81–87).

4. Banerjee, A.V., & Duflo, E. (2011). Poor economics: A radical rethinking of the way to fight global poverty. Public Affairs.

5. Banerjee, A.V., & Pande, R. (2007). Parochial politics: Ethnic preferences and politician corruption.

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