Manipulating Elections by Selecting Issues
Constructive election control considers the problem of an adversary who seeks to sway the outcome of an electoral process in order to ensure that their favored candidate wins. In this thesis, we first consider the computational problem of constructive election control via issue selection. In this problem, a party decides which political issues to focus on to ensure victory for the favored candidate. We also consider a variation in which the goal is to maximize the number of voters supporting the favored candidate. We present strong negative results, showing, for example, that the latter problem is inapproximable for any constant factor. On the positive side, we show that when issues are binary, the problem becomes tractable in several cases, and admits a 2-approximation in the two-candidate case. Finally, we develop integer programming and heuristic methods for these problems. We also consider the related problem of constructing ideological point estimates for a population when given the past voting history of that population. We develop a technique to generate point estimates in any number of dimensions using a simple neural network, and relate how the derived point estimates can be used in an instance of constructive election control via issue selection.