SearcharxivSearch

arXiv · 2507.17981

$k$-Approval Veto: A Spectrum of Voting Rules Balancing Metric Distortion and Minority Protection

Abstract

In the context of single-winner ranked-choice elections between $m$ candidates, we explore the tradeoff between two competing goals in every democratic system: the majority principle (maximizing the social welfare) and the minority principle (safeguarding minority groups from overly bad outcomes).To measure the social welfare, we use the well-established framework of metric distortion subject to various objectives: utilitarian (i.e., total cost), $\alpha$-percentile (e.g., median cost for $\alpha = 1/2$), and egalitarian (i.e., max cost). To measure the protection of minorities, we introduce the $\ell$-mutual minority criterion, which requires that if a sufficiently large (parametrized by $\ell$) coalition $T$ of voters ranks all candidates in $S$ lower than all other candidates, then none of the candidates in $S$ should win. The highest $\ell$ for which the criterion is satisfied provides a well-defined measure of mutual minority protection (ranging from 1 to $m$). Our main contribution is the analysis of a recently proposed class of voting rules called $k$-Approval Veto, offering a comprehensive range of trade-offs between the two principles. This class spans between Plurality Veto (for $k=1$) - a simple voting rule achieving optimal metric distortion - and Vote By Veto (for $k=m$) which picks a candidate from the proportional veto core. We show that $k$-Approval Veto has minority protection at least $k$, and thus, it accommodates any desired level of minority protection. However, this comes at the price of lower social welfare. For the utilitarian objective, the metric distortion increases linearly in $k$. For the $\alpha$-percentile objective, the metric distortion is the optimal value of 5 for $\alpha \ge k/(k+1)$ and unbounded for $\alpha < k/(k+1)$. For the egalitarian objective, the metric distortion is the optimal value of 3 for all values of $k$.

Explore related subjects

Keep this discovery

BibTeXRIS

Fatih Erdem Kizilkaya, David Kempe. 2025-07-23. $k$-Approval Veto: A Spectrum of Voting Rules Balancing Metric Distortion and Minority Protection. https://arxiv.org/abs/2507.17981

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

MMS Allocation for Chores with Online Agent Arrivals

We study the fair allocation of $m$ indivisible chores to $n$ agents with subadditive cost functions arriving online in an arbitrary order. Upon an agent's arrival, we are informed of her cost function and must irrevocably assign her a set of chores. We focus on the Maximin Share (MMS) fairness notion and aim to compute an allocation in which all items are assigned, and no agent incurs a cost more than $\alpha$ times her MMS. Without any prior information about the instance (other than $n$ and $m$), we design an algorithm with a competitive ratio of $O(\min\{n, k\log^{1+\epsilon}k, \log m\})$ for any constant $\epsilon > 0$, where $k$ denotes the number of cost function types. Our bound matches the best known offline approximation guarantees for MMS under subadditive costs and is nearly optimal with respect to all three parameters: we show that even for binary additive cost functions, no online algorithm can achieve a competitive ratio of $o(\min\{n, k\log k, \log m\})$. We then consider the setting in which the $k$ cost function types are known in advance (though the realized types of arriving agents are not). For additive cost functions, we provide an algorithm with a competitive ratio of $O(\min\{\log k, \log(kn)/\log\log(kn)\})$, and show that constant-competitive algorithms do not exist for general $k$, even for the binary additive setting. For binary additive functions when $k \le n$, we propose a $3$-competitive algorithm and establish a lower bound of $2$.

cs.GT

Truncated Noisy Best-Response Algorithms: Toward Game Theoretic Learning with Safety Guarantees

We consider a game theoretic approach to solve multi-agent coordination problems with submodular maximization objectives. It is known for such problems that the Nash equilibria for the corresponding game are always within 50% of the optimal, but that the equilibria which achieve this worst-case bound are not stable. To exploit this instability, we propose a family of algorithms which we call Truncated Noisy Best-Response (TNBR) Algorithms. These algorithms are flexibly characterized by agents asynchronously and stochastically selecting actions from a neighbourhood of their best response payoffs. We compute bounds on the recurrent classes of TNBR algorithms' associated Markov chains. Our bounds fall into two categories: first, "Performance" bounds ensure that TNBR algorithms always have a high-value recurrent state; second, "Safety" bounds ensure that TNBR algorithms never have arbitrarily-bad recurrent states. Furthermore, these two types of bounds are linked by a waterbed-like effect: every game with a poor Safety guarantee necessarily has a favorable Performance guarantee.

cs.GT

Existence of the Core in Approval-Based Committee Elections

We settle the main open question in the theory of approval-based multi-winner elections: we show that there always exists a committee in the core. The core is a stability and group fairness concept. The proof introduces a new voting rule that optimizes an entropy-like objective function over committees and payment systems. All local optima of this objective function lie in the core, which implies that a core committee can be found in polynomial time.

cs.GT