Searcharxiv⌕ Search

subject

cs.GT

cs.GT: explore 136 source-linked works published from 2026 to 2026, with original documents and citations.

This collection is a preview while coverage and quality are evaluated.

Search within this collection

Coverage and selection

Includes records with this source-supplied label or an explicit phrase match in their metadata. Matches indicate a mention, not proof that a paper uses a method or tests a material. Source versions are consolidated by DOI.

Sources: arxiv. Collection updated 2026-09-16. Counts describe this index, not the complete source archives.

arXiv2026

Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers

Recent machine learning research has increasingly focused on equilibrium analysis in non-cooperative games rather than solely on optimal solutions. Many such problems involve shared constraints and can be formulated as Generalized Nash Equilibrium Problems (GNEPs). For strongly monotone games, existing methods compute consensus-based variational GNEs (v-GNEs) by exchanging Lagrange multipliers. We propose a fully distributed continuous-time algorithm for shared linear equality constraints that converges without multiplier exchange and reaches any GNE, reducing communication overhead and improving privacy. Discrete-time schemes are also provided, and the method is validated on a multi-robot placement task.

cs.LG↗
arXiv2026

A composite generalization of Ville's martingale theorem using e-processes

We provide a composite version of Ville's theorem that an event has zero measure if and only if there exists a nonnegative martingale which explodes to infinity when that event occurs. This is a classic result connecting measure-theoretic probability to the sequence-by-sequence game-theoretic probability, recently developed by Shafer and Vovk. Our extension of Ville's result involves appropriate composite generalizations of nonnegative martingales and measure-zero events: these are respectively provided by ``e-processes'', and a new inverse capital outer measure. We then develop a novel line-crossing inequality for sums of random variables which are only required to have a finite first moment, which we use to prove a composite version of the strong law of large numbers (SLLN). This allows us to show that violation of the SLLN is an event of outer measure zero and that our e-process explodes to infinity on every such violating sequence, while this is provably not achievable with a nonnegative (super)martingale.

math.PR↗
arXiv2026

Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers

We study the problem in which a central planner sequentially allocates a single resource to multiple strategic agents using their utility reports at each round, but without using any monetary transfers. We consider general agent utility distributions and two standard settings: a finite horizon $T$ and an infinite horizon with $γ$ discounts. We provide general tools to characterize the convergence rate between the optimal mechanism for the central planner and the first-best allocation if true agent utilities were available. This heavily depends on the utility distributions, yielding rates anywhere between $1/\sqrt T$ and $1/T$ for the finite-horizon setting, and rates faster than $\sqrt{1-γ}$, including exponential rates for the infinite-horizon setting as agents are more patient $γ\to 1$. On the algorithmic side, we design mechanisms based on the promised-utility framework to achieve these rates and leverage structure on the utility distributions. Intuitively, the more flexibility the central planner has to reward or penalize any agent while incurring little social welfare cost, the faster the convergence rate. In particular, discrete utility distributions typically yield the slower rates $1/\sqrt T$ and $\sqrt{1-γ}$, while smooth distributions with density typically yield faster rates $1/T$ (up to logarithmic factors) and $1-γ$.

cs.GT↗
arXiv2026

Scale-robust Auctions

We study auctions that are robust at any scale, i.e., they can be applied to sell both expensive and cheap items and achieve the best multiplicative approximation of the optimal revenue in the worst case. We first show that it is without loss of optimality to restrict attention to scale-invariant mechanisms whenever the family of possible distributions is closed under every positive rescaling. This conclusion uses no regularity or other distributional shape restriction. We then solve the two-agent, single-item problem with values drawn i.i.d. from an unknown regular distribution when only a high value bidder can receive a positive allocation. The robustly optimal mechanism in this class randomizes between the second-price auction, with probability approximately 0.806, and a markup auction that offers the item to the highest-valued bidder at a price equal to 2.447 times the second-highest value. Its worst-case approximation ratio is approximately 1.907.

cs.GT↗
arXiv2026

Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games

Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measurement- or payoff-based optimization schemes. Such learning schemes exhibit several advantages, especially in relation to filtering out noisy observations. However, they may exhibit several limitations when applied in a distributed setup. In multi-player weakly-acyclic games, and when each player applies an independent copy of the learning dynamics, convergence to (usually desirable) pure Nash equilibria cannot be guaranteed. Prior work has only focused on a small class of games, namely potential and coordination games. To address this main limitation, this paper introduces a novel payoff-based learning scheme for distributed optimization, namely aspiration-based perturbed learning automata (APLA). In this class of dynamics, and contrary to standard reinforcement-based learning schemes, each player's probability distribution for selecting actions is reinforced both by repeated selection and an aspiration factor that captures the player's satisfaction level. We provide a stochastic stability analysis of APLA in multi-player positive-utility games under the presence of noisy observations. This is the first part of the paper that characterizes stochastic stability in generic non-zero-sum games by establishing equivalence of the induced infinite-dimensional Markov chain with a finite dimensional one. In the second part, stochastic stability is further specialized to weakly acyclic games.

cs.LG↗
arXiv2026

Distributed Places and Safe Net Reduction

Being able to find small Petri nets with the same behaviour as formal specifications of concurrent systems benefits both effective verification and practical implementation of such systems. This paper considers specifications given in the form of compositionally defined safe nets. The paper discusses a novel concept of distributed place which implements the behaviour of an individual net place. It is shown that if distributed places cover a safe Petri net, then it is possible to delete some places without changing the behaviour. Crucially, the reduction is both static and local, making it computationally feasible in practice. The resulting reduction technique is then applied to an algebra of safe Petri nets (boxes) derived compositionally from process (box) expressions. Though the original derivation can yield exponentially large boxes, prior research demonstrated that if a box expression does not involve cyclic behaviours, the exponential number of places can be reduced down to polynomial (quadratic). In this paper, using distributed places, it is show that similar optimisation can also be achieved in the case of process expressions with iteration.

cs.GT↗
arXiv2026

Coalition Formation with Limited Information Sharing for Local Energy Management

Distributed energy systems with prosumers require new methods for coordinating energy exchange among agents. Coalitional control provides a framework in which agents form groups to cooperatively reduce costs; however, existing bottom-up coalition-formation methods typically require full information sharing, raising privacy concerns and imposing significant computational overhead. In this work, we propose a limited information coalition-formation algorithm that requires only limited aggregate information exchange among agents. By constructing an upper bound on the value of candidate coalitions, we eliminate the need to solve optimisation problems for each potential merge, significantly reducing computational complexity while limiting information exchange. We prove that the proposed method guarantees cost no greater than that of decentralised operation. Coalition strategies are optimised using a distributed approach based on the Alternating Direction Method of Multipliers (ADMM), further limiting information sharing within coalitions. We embed the framework within a model predictive control scheme and evaluate it on real-world data, demonstrating improved economic performance over decentralised control with substantially lower computational cost than full-information approaches.

cs.GT↗
arXiv2026

Learning Fair Allocation of Indivisible Items from Limited Feedback

We study a setting in which an algorithm must output a fair allocation of indivisible items while "learning on the job". More specifically, the algorithm is to output an allocation satisfying EF1, PROP1, or similar fairness notions; however, the algorithm initially has no information about the agents' valuations, and can only learn about them by (repeatedly) proposing an allocation, and obtaining feedback about a fairness violation in the allocation. Importantly, the observed fairness violation may be adversarially chosen. The algorithm's goal is to converge to a fair allocation in rounds polynomial in the number of agents and items, ideally with only polynomial computation. We prove two main results: first, when the valuations are additive, then even for mixed items (goods and chores), an allocation satisfying EF1 or PROP1 can be found in polynomial time using the corresponding feedback. These results are instantiations of a more general framework which maintains a polytope of candidate valuations consistent with all past feedback. The algorithm repeatedly constructs putative valuations and uses them to propose allocations; the observed violations then define separating hyperplanes, allowing the algorithm to emulate the ellipsoid method. When the valuations are monotone, we present an algorithm which is guaranteed to find an EF1 allocation in polynomially many iterations; however, its internal calculations are not guaranteed to be polynomial. The algorithm again maintains putative valuations, and only considers allocations in which each agent obtains an interval plus one additional item with respect to an arbitrary ordering of the items. It is known that such an EF1 allocation always exists; however, because the existence proof is non-constructive, the internal step of constructing an allocation from the putative valuations is not known to take polynomial time.

cs.GT↗
arXiv2026

Multi-Winner Voting with Argumentative Ballots

We introduce multi-winner voting with argumentative ballots (MVArg) and investigate theoretical properties. As our conceptual contribution, we generalise approval ballots to argumentative ballots, thereby allowing voters to express defeasible preferences over candidates. We accordingly generalise voter cohesion and justified representation axioms JR, PJR and EJR. As our theoretical contribution, we establish several key results. First, MVArg is strictly more expressive than multi-winner voting with approval ballots (MV). Second, our notions of cohesion and justified representation are conservative generalisations of their counterparts in MV. Third, the MVArg counterpart of JR can always be satisfied, whereas the counterparts of PJR and EJR cannot always be. Fourth, although verifying whether a winner set satisfies the MVArg counterpart of JR is already coNP-hard, such a winner set can be constructed in polynomial time. All definitions, propositions, auxiliary lemmas and theorems have been formalised and mechanically checked in Lean 4.

cs.GT↗
arXiv2026

Graphon Design for Human-Machine Coordination under Bounded Rationality: Optimality of Stochastic Block Models

Coordination is a desirable feature in multi-agent systems, ranging from robotic swarms to socioeconomic networks. This paper is concerned with promoting coordination among heterogeneous agents, e.g., machines and humans, interacting in a stag-hunt game. In our model the agents exhibit bounded rationality at different levels, which leads to uncertainty and a propensity for errors during learning and decision-making processes. This paper addresses the problem of designing a network topology that maximizes a global metric of coordination under such constraints. While optimizing over the discrete space of finite graphs is generally computationally intractable, we employ a mean-field approach to lift the problem into the space of graphons. Within this framework, we analyze agents following a logit learning dynamics. Using calculus of variations, we show that for systems with a bimodal rationality profile, it suffices to search for optimal graphons in the ensemble of stochastic block models. We then propose a water-filling algorithm to find a locally optimal graphon. Finite graphs can then be sampled from the optimized graphon, bypassing the inherent combinatorial complexities of discrete graph optimization.

eess.SY↗
arXiv2026

Fine-Tuning Autobidders with Group Relative Policy Optimization

Automated bidding (autobidding) is a core component of modern online advertising systems. Within this component, advertisers delegate sequential bid decisions to algorithms that must maximize campaign value while adhering to constraints such as a limited budget and a target cost-per-click (CPC). One of the approaches to resolve the autobidding problem is to formulate it as a Markov decision process and use reinforcement learning (RL) to train a bid generation function. The standard RL framework is actor-critic, which consists of an actor network that generates actions and a critic network that estimates the value of those actions. In our setting, the action is typically a bid or related pacing multiplier, and the value is the expected return from the auction given the bid. However, the alternating training of actor-critic RL models leads to instability and reduced robustness to noise. To address these issues, we adapt the Group Relative Policy Optimization (GRPO) framework to the autobidding setting. This framework is a \emph{critic-free} policy-gradient method originally developed for large language model post-training, where the ground-truth target is unknown. The autobidding setting shares this property, since the optimal bid is unknown in advance. Moreover, GRPO in the LLM domain is used to fine-tune the pre-trained model, and we use the same technique to enhance the performance of the strong heuristic baseline. We empirically compare Autobidding GRPO with actor-critic models, simple heuristics, and controller-based methods on the BAT, iPinYou, and AuctionNet benchmarks. Extensive experiments show that Autobidding GRPO consistently outperforms baselines in clicks and is the best or second-best method in conversion volume.

cs.GT↗
arXiv2026

A Constant Metric Distortion Protocol for Approval Voting Given Plurality Polls

Approval voting is a simple and well-regarded voting rule: voters submit approval ballots (subsets of the candidates) and the candidate receiving the most approvals wins. One major limitation of approval voting is that it is not clear which candidates voters should approve if they have an underlying strict order over the candidates. In this paper, we initiate the study of approval voting under a simple kind of information: plurality polls. That is, we assume that for each candidate we know the share of voters who rank this candidate as their top choice. Using these plurality polls, we suggest a simple protocol parameterized by a fraction $k \in (0,1)$: every voter should approve the smallest prefix of their preference list containing the first choices of at least a fraction $k$ of the voters. We evaluate this protocol via the framework of metric distortion and show that for the optimal choice of $k$, this rule achieves a metric distortion of $2 + \sqrt{5} \simeq 4.236$. The proof techniques we use for this statement also show that the Bucklin voting rule has a metric distortion of at most $5$. Finally, we evaluate the robustness of our protocol to noise and show that the upper bounds obtained for our protocol are tight.

cs.GT↗
arXiv2026

Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers

In applications, it is often required to test objects or people to determine their qualities in terms of certain metrics. However, besides being naturally noisy, the test results can be corrupted by adversarial behaviors of objects or people being tested (test takers). For example, dishonest test takers can cheat in the exams to distort the test results. With the development of AI technologies, such distortions driven by cheating using AI technologies are becoming more commonplace and severe. In this paper, we propose optimal testing strategies which can still recover needed test results even if there are cheaters polluting the results. The proposed testing strategies will optimally re-test selected group of test takers using different testing security measures. We determine the optimal testing strategies using a dynamic programming method.

cs.CR↗
arXiv2026

Adaptive Strategies for GR(1) Games

We consider two-player GR(1) games on graphs, where the system player Eve must satisfy \[ \Box\Diamond A_1\land\cdots\land\Box\Diamond A_m \;\implies\; \Box\Diamond G_1\land\cdots\land\Box\Diamond G_n \] against the environment player Adam. Here $A_1,\ldots,A_m$ are assumptions on the environment, $G_1,\ldots,G_n$ are guarantees the system must provide, and $\Box\Diamond S$ denotes ``always eventually $S$''. Traditional static strategies are overly conservative: they may actively violate assumptions to trivially satisfy the implication, or abandon all guarantees when any assumption is violated. Existing methods to prevent such behaviors incur doubly exponential blowup. We introduce an adaptive framework treating Adam as a non-adversarial agent with unknown objectives. Eve monitors which assumptions Adam actually meets and adapts her strategy at runtime to maximize satisfied guarantees. Central to our approach is a novel algorithm for monitoring liveness properties $\Box\Diamond S$, enabling Eve to maintain real-time likelihood estimates of which assumptions will be fulfilled. Eve pre-computes strategies optimal for different assumption subsets, deploying a probability distribution over them that dynamically adjusts based on monitor outputs. We prove that when assumptions are violated, Eve's randomized adaptive strategy converges asymptotically to the deterministic strategy maximizing guarantees. A prototype demonstrates effectiveness and superior computational performance compared to the state of the art.

cs.LO↗
arXiv2026

From the Social Choice Problem to a Collusion-Proof Tendering Mechanism for Dynamic Stochastic Projects

The VCG family and the AGV mechanism are two classical approaches to efficient implementation in the static social choice problem. In 2024, Csóka et al. showed that AGV has critical weaknesses. In contrast, the transferable-utility Guaranteed Utility Mechanism (TU-GUM) retains all the standard desirable properties of AGV while adding further ones, including collusion-proofness, because it implements efficiency in Guaranteed Utility Equilibrium. TU-GUM also applies to a more general dynamic setting with multiple extensions. Moreover, TU-GUM is a special case of an even more general and robust mechanism that combines contingent first-price tendering with the coordinated execution of dynamic stochastic multi-agent projects through a surprisingly simple rule. This paper summarizes and connects existing results from a different perspective, with some minor new observations.

econ.TH↗
arXiv2026

Networked Multi-Resource Defense Capabilities in a General Lotto Game

Ensuring the security of complex systems involves the strategic allocation of defensive resources to prevent various types of attacks from succeeding. A defender often has multiple types of defensive assets at its disposal, where it must decide how to optimally deploy their heterogeneous capabilities across different attack types. In this paper, we formulate a multi-resource allocation problem in the form of a General Lotto game where a defender possesses various types of resources. A feature that we introduce is that their individual effectiveness against different types of attacks is characterized by a network weight matrix. In our analysis, we derive upper and lower bounds on the performance of the defender, and provide numerical evidence suggesting that they are tight. For the case of two attack types, we analytically prove that the bounds coincide, establishing an exact equilibrium characterization. We then numerically compare our proposed networked multi-resource architecture to an independent-defense benchmark from the existing literature. These results highlight fundamental and tractable structures underlying multi-attack-type defense problems.

cs.GT↗
arXiv2026

Peer Oversight in Collective Decision Making

This article introduces peer $k$-oversight, a property of sequential collective decision mechanisms requiring at least $k$ agents to be responsible for every harmful outcome. It is shown that whenever $k$-oversight can be achieved by redistributing control over the decisions in a mechanism, it can be achieved using just $k$ agents. A polynomial-time algorithm is also presented that determines whether such a redistribution exists and, when it does, constructs one. These results establish peer oversight as a tractable design principle for multiagent decision-making systems.

cs.GT↗
arXiv2026

A 1.283 Price-of-Anarchy Bound for the Repeated Virtual First-Price Auction

We study the repeated allocation of a single indivisible resource among $n$ strategic players. Each player $i$ has a privately known value distribution $D_i$, and values are drawn independently across players and periods. The goal is to find fair and efficient mechanisms. We apply the repeated first-price auction with equal initial endowments of virtual money. We show that each player can asymptotically secure the same fair-floor guarantee $f(D_i)$ as in Csóka 2026; consequently, the mechanism is $1.283$-optimal. This provides a simpler and more robust alternative mechanism for this special case and may also help derive sharper upper bounds on the price of anarchy.

cs.GT↗
Compare source metadata on this page
WorkPublishedSource identifierSource
Fully Distributed GNE Algorithms for Multi-Robot Placement without Consensus on Multipliers2026-08-292608.29388arxiv
A composite generalization of Ville's martingale theorem using e-processes2026-08-282203.04485arxiv
Near-Optimal Mechanisms for Resource Allocation Without Monetary Transfers2026-08-282408.10066arxiv
Scale-robust Auctions2026-08-282510.21231arxiv
Aspiration-based Perturbed Learning Automata in Games with Noisy Utility Measurements. Part A: Stochastic Stability in Non-zero-Sum Games2026-08-282511.11602arxiv
Distributed Places and Safe Net Reduction2026-08-282512.13538arxiv
Coalition Formation with Limited Information Sharing for Local Energy Management2026-08-282603.28562arxiv
Learning Fair Allocation of Indivisible Items from Limited Feedback2026-08-282606.31457arxiv
Multi-Winner Voting with Argumentative Ballots2026-08-282608.23247arxiv
Graphon Design for Human-Machine Coordination under Bounded Rationality: Optimality of Stochastic Block Models2026-08-282608.27851arxiv
Fine-Tuning Autobidders with Group Relative Policy Optimization2026-08-282608.28199arxiv
A Constant Metric Distortion Protocol for Approval Voting Given Plurality Polls2026-08-282608.28340arxiv
Optimal Adversarial Testing: Extracting Honest Test Results from Dishonest Test Takers2026-08-282608.28362arxiv
Adaptive Strategies for GR(1) Games2026-09-172608.28391arxiv
From the Social Choice Problem to a Collusion-Proof Tendering Mechanism for Dynamic Stochastic Projects2026-08-282608.28722arxiv
Networked Multi-Resource Defense Capabilities in a General Lotto Game2026-09-122608.28732arxiv
Peer Oversight in Collective Decision Making2026-08-282608.28754arxiv
A 1.283 Price-of-Anarchy Bound for the Repeated Virtual First-Price Auction2026-08-282609.05499arxiv

These are bibliographic comparisons, not experimental rankings. Follow the original document for methods and conditions.