Schulze voting is a recently introduced voting system enjoying unusual popularity and a high degree of real-world use, with users including the Wikimedia foundation, several branches of the Pirate Party, and MTV. It is a Condorcet voting system that determines the winners of an election using information about paths in a graph representation of the election. We resolve the complexity of many electoral control cases for Schulze voting. We find that it falls short of the best known voting systems in terms of control resistance, demonstrating vulnerabilities of concern to some prospective users of the system.

Voting theory has become increasingly integrated with computational social choice and multiagent systems. Computational complexity has been extensively used as a shield against manipulation of voting systems, however for several voting schemes this complexity may cause calculating the winner to be computationally difficult. Of the many voting systems that have been studied with regard to election manipulation, a few have been found to have an unweighted coalitional manipulation problem that is NP-hard for a constant number of manipulators despite having a winner problem that is in P. We survey this interesting class of voting systems and the work that has analyzed their complexity.

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One common problem plaguing crowdsourcing tasks is tuning the set of worker responses: Depending on task requirements, requesters may want a large set of rich and varied worker responses (typically in subjective evaluation tasks) or a more convergent response-set (typically for more objective tasks such as fact-checking). This problem is especially salient in tasks that combine workers’ responses to present a single output: Divergence in these settings could either add richness and complexity to the unified answer, or noise. In this paper we present HiveMind, a system of methods that allow requesters to tune different levels of convergence in worker participation for different tasks simply by adjusting the value of one variable.