🤖 AI Summary
This study investigates whether a disadvantaged political party can strategically reallocate its candidates across fixed electoral districts to secure more seats. It formally introduces and defines the “re-contesting” problem for the first time and provides a systematic analysis of its computational complexity. Employing polynomial-time many-one reductions, parameterized complexity theory, and axiomatic methods, the work delineates precise boundaries between tractable and intractable cases: it characterizes conditions under which the problem is solvable in polynomial time versus NP-hard, and establishes reduction and separation relationships among multiple modeling variants. These results offer a rigorous theoretical foundation for understanding the computational feasibility of strategic electoral interventions under fixed district boundaries.
📝 Abstract
Redistricting efforts have gathered contemporary attention in both quotidian and scholarly debates, particularly in the United States where efforts to redraw congressional districts to favor either of the two major parties in 12 states -- such as California, Texas, and Ohio -- have captured the public eye. The treatment of redistricting in computational social choice has essentially focused on the process of determining"appropriate"districts. In this work, we are interested in understanding the gamut of options left for the"losing"party, and so we consider the flip side of the problem: Given fixed/predetermined districts, can a given party still make their candidates win by strategically placing them in certain districts? We dub this as"recampaigning"to capture the intuition that a party would redirect their campaigning efforts from one district to another. We model recampaigning as a computational problem, consider natural variations of the model, and study those new models through the lens of (1) (polynomial-time many-one) interreducibilities, (2) separations/collapses (both unconditional and axiomatic-sufficient), and (3) both worst-case and parametrized complexity.