BARGAINING IN COMMITTEES OF REPRESENTATIVES: THE OPTIMAL VOTING RULE

Committees are often made up of representatives of different-sized groups of individuals, and make decisions by means of a voting rule which specifies what vote configurations can pass a decision. This raises the question of the choice of the optimal voting rule, given the different sizes of the groups that members represent. In this paper we take a new departure to address this problem, assuming that the committee is a bargaining scenario in which negotiations take place 'in the shadow of the voting rule' in search of unanimous consensus. That is, a general agreement is looked for, but any winning coalition can enforce an agreement.Voting rule, Bargaining, Nash solution.

Multi-Winner Voting with Approval Preferences

Approval-based committee (ABC) rules are voting rules that output a fixed-size subset of candidates, a so-called committee. ABC rules select committees based on dichotomous preferences, i.e., a voter either approves or disapproves a candidate. This simple type of preferences makes ABC rules widely suitable for practical use. In this book, we summarize the current understanding of ABC rules from the viewpoint of computational social choice. The main focus is on axiomatic analysis, algorithmic results, and relevant applications.Comment: This is a draft of the upcoming book "Multi-Winner Voting with Approval Preferences

Proportionally Representative Clustering

In recent years, there has been a surge in effort to formalize notions of fairness in machine learning. We focus on clustering -- one of the fundamental tasks in unsupervised machine learning. We propose a new axiom ``proportional representation fairness'' (PRF) that is designed for clustering problems where the selection of centroids reflects the distribution of data points and how tightly they are clustered together. Our fairness concept is not satisfied by existing fair clustering algorithms. We design efficient algorithms to achieve PRF both for unconstrained and discrete clustering problems. Our algorithm for the unconstrained setting is also the first known polynomial-time approximation algorithm for the well-studied Proportional Fairness (PF) axiom (Chen, Fain, Lyu, and Munagala, ICML, 2019). Our algorithm for the discrete setting also matches the best known approximation factor for PF.Comment: Revised version includes a new author (Jeremy Vollen) and new results: Our algorithm for the unconstrained setting is also the first known polynomial-time approximation algorithm for the well-studied Proportional Fairness (PF) axiom (Chen, Fain, Lyu, and Munagala, ICML, 2019). Our algorithm for the discrete setting also matches the best known approximation factor for P

Political Quotas and Governance

Reserving political office for members of a particular, usually disadvantaged, group is a common form of political quota in many parts of the world. This has been shown to improve distributional access in favour of reserved groups, but often conjectured (and shown) to come at the cost of governance quality. We develop the first theoretical model to demonstrate the opposite possibility; a reduction in political competition - due to office being restricted to members of a pre-designated group - can improve governance. The model establishes a tight set of predictions regarding when improvements should be expected to occur, and when not. Such predictions are not yielded by alternative theories of political competition, are a priori unlikely to occur by chance, and have never been investigated in the large empirical literature on the effects of political reservations. We first show, in a Maharashtrian sample of rural villages, that governance outcomes dramatically increase under reservations. This is the first such effect documented in the literature. We then demonstrate a non-uniform pattern of improvement that lines up precisely with the predictions of the theory developed here