Tournaments in Computational Social Choice: Recent Developments

Tournaments are commonly used to select winning alternatives in scenarios involving pairwise comparisons such as sports competitions and political elections. This survey discusses recent developments in two major lines of work—tournament solutions and single-elimination tournaments—with a focus on how computational social choice has brought new frameworks and perspectives into these decades-old studies.

From Computational Social Choice to Digital Democracy

Digital Democracy (aka e-democracy or interactive democracy) aims to enhance democratic decision-making processes by utilizing digital technology. A common goal of these approaches is to make collective decision-making more engaging, inclusive, and responsive to participants' opinions. For example, online decision-making platforms often provide much more flexibility and interaction possibilities than traditional democratic systems. It is without doubt that the successful design of digital democracy systems presents a multidisciplinary research challenge. I argue that tools and techniques from computational social choice should be employed to aid the design of online decision-making platforms and other digital democracy systems.

Loss Functions, Axioms, and Peer Review

It is common to see a handful of reviewers reject a highly novel paper, because they view, say, extensive experiments as far more important than novelty, whereas the community as a whole would have embraced the paper. More generally, the disparate mapping of criteria scores to final recommendations by different reviewers is a major source of inconsistency in peer review. In this paper we present a framework inspired by empirical risk minimization (ERM) for learning the community's aggregate mapping. The key challenge that arises is the specification of a loss function for ERM. We consider the class of L(p,q) loss functions, which is a matrix-extension of the standard class of Lp losses on vectors; here the choice of the loss function amounts to choosing the hyperparameters p and q. To deal with the absence of ground truth in our problem, we instead draw on computational social choice to identify desirable values of the hyperparameters p and q. Specifically, we characterize p=q=1 as the only choice of these hyperparameters that satisfies three natural axiomatic properties. Finally, we implement and apply our approach to reviews from IJCAI 2017.

Preference Elicitation for Participatory Budgeting

Participatory budgeting enables the allocation of public funds by collecting and aggregating individual preferences. It has already had a sizable real-world impact, but making the most of this new paradigm requires rethinking some of the basics of computational social choice, including the very way in which individuals express their preferences. We attempt to maximize social welfare by using observed votes as proxies for voters’ unknown underlying utilities, and analytically compare four preference elicitation methods: knapsack votes, rankings by value or value for money, and threshold approval votes. We find that threshold approval voting is qualitatively superior, and also performs well in experiments using data from real participatory budgeting elections. This paper was accepted by Yan Chen, decision analysis.

Social Ranking Manipulability for the CP-Majority, Banzhaf and Lexicographic Excellence Solutions

We investigate the issue of manipulability for social ranking rules, where the goal is to rank individuals given the ranking of coalitions formed by them and each individual prefers to reach the highest positions in the social ranking. This problem lies at the intersection of computational social choice and the algorithmic theory of power indices. Different social ranking rules have been recently proposed and studied from an axiomatic point of view. In this paper, we focus on rules representing three classical approaches in social choice theory: the marginal contribution approach, the lexicographic approach and the (ceteris paribus) majority one. We first consider some particular members of these families analysing their resistance to a malicious behaviour of individuals. Then, we analyze the computational complexity of manipulation, and complete our theoretical results with simulations in order to analyse the manipulation frequencies and to assess the effects of manipulations.

Closing the Loop: Bringing Humans into Empirical Computational Social Choice and Preference Reasoning

Research in both computational social choice and preference reasoning uses tools and techniques from computer science, generally algorithms and complexity analysis, to examine topics in group decision making. This has brought tremendous progress in the last decades, creating new avenues for research and results in areas including voting and resource allocation. I argue that of equal importance to the theoretical results are impacts in research and development from the empirical part of the computer scientists toolkit: data, system building, and human interaction. I highlight work by myself and others to establish data driven, application driven research in the computational social choice and preference reasoning areas. Along the way, I highlight interesting application domains and important results from the community in driving this area to make concrete, real-world impact.

Evaluating Approval-Based Multiwinner Voting in Terms of Robustness to Noise

Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting can indeed be robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.

When AI Ethics Goes Astray: A Case Study of Autonomous Vehicles

This article discusses the dangers of the Moral Machine (MM) experiment, alerting against both its uses for normative ends and the whole approach it is built upon to address ethical issues. It explores additional methodological limits of the experiment on top of those already identified by its authors; exhibits the dangers of computational moral systems for modern democracies, such as the “voting-based system” recently developed out of the MM’s data; and provides reasons why ethical decision-making fundamentally excludes computational social choice methods.

Protecting Elections by Recounting Ballots

Complexity of voting manipulation is a prominent topic in computational social choice. In this work, we consider a two-stage voting manipulation scenario. First, a malicious party (an attacker) attempts to manipulate the election outcome in favor of a preferred candidate by changing the vote counts in some of the voting districts. Afterwards, another party (a defender), which cares about the voters' wishes, demands a recount in a subset of the manipulated districts, restoring their vote counts to their original values. We investigate the resulting Stackelberg game for the case where votes are aggregated using two variants of the Plurality rule, and obtain an almost complete picture of the complexity landscape, both from the attacker's and from the defender's perspective.