Towards completing the puzzle: complexity of control by replacing, adding, and deleting candidates or voters

We investigate the computational complexity of electoral control in elections. Electoral control describes the scenario where the election chair seeks to alter the outcome of the election by structural changes such as adding, deleting, or replacing either candidates or voters. Such control actions have been studied in the literature for a lot of prominent voting rules. We complement those results by solving several open cases for Copeland$$^{\alpha }$$ α , maximin, k-veto, plurality with runoff, veto with runoff, Condorcet, fallback, range voting, and normalized range voting.

Human Factors of Democracy

This chapter takes a new look at different voting methods, asking not how they work but rather how well they let voters express themselves. The most widely used method in the world (“choose one candidate”) is by far the worst at allowing voters to express themselves. This type of ballot has the vocabulary span of a six-month-old infant. Widely used rank-order ballots are modestly better (about the vocabulary of a two-year-old child) but are still very weak as communication devices. New forms of voting, including range voting and majority judgment (where voters grade the choices), offer vastly more ways for voters to express their true sentiments about the choices offered to them. This chapter also assesses how well voters are likely to understand exactly how voting systems work, possibly affecting their trust of the process.

Integrating Multiple Models Using Image-as-Documents Approach for Recognizing Fine-Grained Home Contexts

To implement fine-grained context recognition that is accurate and affordable for general households, we present a novel technique that integrates multiple image-based cognitive APIs and light-weight machine learning. Our key idea is to regard every image as a document by exploiting “tags” derived by multiple APIs. The aim of this paper is to compare API-based models’ performance and improve the recognition accuracy by preserving the affordability for general households. We present a novel method for further improving the recognition accuracy based on multiple cognitive APIs and four modules, fork integration, majority voting, score voting, and range voting.

Approval Voting

This chapter discusses and analyzes approval voting, one of the point-summing methods where each voter is allowed to cast as many votes as he wishes. The form in which approval voting is practiced currently is an example of range-voting, in which there are only two meaningless scores. The approval voting experiments conducted under the conditions of the Orsay experiment of 2007 demonstrates support to the theoretical claims for and against it. The concept of approval judgment, which differs from approval voting in its traditional practice, which makes clear that the evaluations are absolute grades, is also discussed. One of the drawbacks of approval voting is that a candidate preferred by only one voter of the electorate is likely to be elected when strategic behavior is ignored.