SearcharxivSearch

arXiv · 2603.10290

Tractable Exclusion Zones for Instant-Runoff Voting on Trees and Beyond

Abstract

Instant-runoff voting (IRV) is often used when voters rank candidates rather than choosing only one favourite. We study IRV under graph-induced metric preferences where each vertex of an unweighted undirected graph hosts one voter and is also a possible candidate location. Voters rank candidates by shortest-path distance with fixed deterministic tie-breaking. We focus on exclusion zones, i.e., sets S such that, whenever at least one candidate lies in S, the IRV winner must also lie in S. Such zones serve as robustness certificates, identifying regions whose participation prevents outside winners from emerging. For general graphs, exclusion-zone verification is co-NP-complete and minimum-zone computation is NP-hard. We show that both problems become polynomial-time solvable on trees. Our main tool is a membership test asking whether a candidate can be forced to lose using opponents from a restricted region. A round-1 reduction shows that any such loss has a witness in which the candidate is eliminated in the first IRV round, enabling a bottom-up dynamic program on trees. We also show that minimum-zone computation has a much smaller search space than its definition suggests. The pairwise-loss graph, obtained from all two-candidate elections, imposes closure constraints on every exclusion zone. With deterministic tie-breaking this graph is a tournament, implying that every nonempty exclusion zone on a tree is generated by the closure of one vertex. Thus, the minimum exclusion zone can be found by testing only linearly many candidate sets. On the opposite front, we refine the intractability range of computing minimum exclusion zones on general graphs, extending it to a much broader class of deterministic elimination rules, dubbed as Strong Forced Elimination.

Explore related subjects

Keep this discovery

BibTeXRIS

Georgios Birmpas, Georgios Chionas, Efthyvoulos Drousiotis, Soodeh Habibi, Marios Mavronicolas, Paul Spirakis. 2026-03-11. Tractable Exclusion Zones for Instant-Runoff Voting on Trees and Beyond. https://arxiv.org/abs/2603.10290

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