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Haris Aziz

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104 papers

1 author row

AAMAS Conference 2026 Conference Paper

Participation Incentives in Online Cooperative Games

Haris Aziz
Yuhang Guo
Zhaohong Sun

This paper studies cooperative games where coalitions are formed online and the value generated by the grand coalition must be irrevocably distributed among the players at each time step. We investigate the fundamental issue of strategic participation incentives and address these concerns by formalizing participation incentive axioms. Our analysis reveals that existing value-sharing mechanisms fail to meet these criteria. Consequently, we propose a family of equal sharing rules that fulfill these desirable participation incentive axioms. Additionally, we refine our mechanisms under superadditive valuations to ensure individual rationality while preserving the previously established axioms.

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AAMAS Conference 2026 Conference Paper

Social Welfare Maximization in Approval-Based Committee Voting under Uncertainty

Haris Aziz
Yuhang Guo
venkateswara Rao Kagita
Baharak Rastegari
Mashbat Suzuki

Approval voting is widely used for making multi-winner voting decisions. The canonical rule (also called Approval Voting) used in the setting aims to maximize social welfare by selecting candidates with the highest number of approvals. We revisit approval-based multi-winner voting in scenarios where the information regarding the voters’ preferences is uncertain. We present several algorithmic results for problems related to social welfare maximization under uncertainty, including computing the social welfare probability distribution of a given outcome, computing the probability that a givenoutcomeissocialwelfaremaximizing, computinganoutcome that is social welfare maximizing with the highest probability, and understanding how robust an outcome is with respect to social welfare maximization.

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JAAMAS Journal 2025 Journal Article

Coordinating monetary contributions in participatory budgeting

Haris Aziz
Sujit Gujar
Jeremy Vollen

Abstract We formalize a framework for coordinating funding and selecting projects, the costs of which are shared among agents with quasi-linear utility functions and individual budgets. Our model contains the discrete participatory budgeting model as a special case, while capturing other useful scenarios. We propose several important axioms and objectives and study how well they can be simultaneously satisfied. We show that whereas welfare maximization admits an FPTAS, welfare maximization subject to a natural and very weak participation requirement leads to a strong inapproximability. This result is bypassed if we consider some natural restricted valuations, namely laminar single-minded valuations and symmetric valuations. Our analysis for the former restriction leads to the discovery of a new class of tractable instances for the Set Union Knapsack problem, a classical problem in combinatorial optimization.

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IJCAI Conference 2025 Conference Paper

Distance Preservation Games

Haris Aziz
Hau Chan
Patrick Lederer
Shivika Narang
Toby Walsh

We introduce and analyze distance preservation games (DPGs). In DPGs, agents express ideal distances to other agents and need to choose locations in the unit interval while preserving their ideal distances as closely as possible. We analyze the existence and computation of location profiles that are jump stable (i. e. , no agent can benefit by moving to another location) or welfare optimal for DPGs, respectively. Specifically, we prove that there are DPGs without jump stable location profiles and identify important cases where such outcomes always exist and can be computed efficiently. Similarly, we show that finding welfare optimal location profiles is NP-complete and present approximation algorithms for finding solutions with social welfare close to optimal. Finally, we prove that DPGs have a price of anarchy of at most 2.

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AAMAS Conference 2025 Conference Paper

Fair Allocation of Divisible Goods under Non-Linear Valuations

Haris Aziz
Zixu He
Xinhang Lu
Kaiyang Zhou

We study the problem of dividing homogeneous divisible goods among agents with non-linear valuations. Specifically, the value that an agent gains from a given good depends only on the amount of the good they receive, and is not necessarily linear with respect to the amount. For instance, under one-breakpoint piecewiseconstant valuations, each agent specifies a threshold for each good such that this agent receives utility zero (resp. , full utility of the good) when getting an amount below (resp. , at least) the threshold. Given non-linear valuations that are additive across the goods, we focus on designing fair allocation algorithms and consider two wellknown fairness properties: the maximin share (MMS) guarantee and envy-freeness (EF). For MMS, we devise an algorithm which always produces a 1 2𝑛−1 -MMS allocation for 𝑛 agents with arbitrary non-decreasing valuations. It is worth noting that this algorithmic result is almost tight as we give an impossibility of guaranteeing more than 1/𝑛 approximation to MMS, even when agents have onebreakpoint piecewise-constant valuations. For 𝑛 ≤ 3 agents, we show the ratio 1/𝑛 is tight. For EF, we show it is NP-hard to check the existence of an EF and Pareto optimal (PO) allocation for 𝑛 agents and at least three goods, even when agents have one-breakpoint piecewise-constant valuations. We complement the hardness result by considering the case with a single divisible good, and devising a polynomial-time algorithm to check whether an EF and PO allocation exists or not for agents with piecewise-linear valuations.

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AAMAS Conference 2025 Conference Paper

Group Fairness in Multi-period Mobile Facility Location Problems

Haris Aziz
Hau Chan
Xingchen Sha
Toby Walsh
Lirong Xia

We study the group-fair multi-period mobile facility location problems, where agents from different groups are located on a real line and arrive in different periods. Our goal is to locate 𝑘 mobile facilities at each period to serve the arriving agents in order to minimize the maximum total group-fair cost and the maximum average group-fair cost objectives that measure the costs or distances of groups of agents to their corresponding facilities across all periods. We first consider the problems from the algorithmic perspective for both group-fair cost objectives. We then consider the problems from the mechanism design perspective, where the agents’ locations and arrival periods are private. For both objectives, we design deterministic strategyproof mechanisms to elicit the agents’ locations and arrival periods truthfully while optimizing the group-fair cost objectives and show that our mechanisms have almost tight bounds on the approximation ratios for certain periods and settings. Finally, we discuss the extensions of our results to the online setting where agent arrival information is only known at each period.

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NeurIPS Conference 2025 Conference Paper

Learning-Augmented Facility Location Mechanisms for Envy Ratio

Haris Aziz
Yuhang Guo
Alexander Lam
Houyu Zhou

The augmentation of algorithms with predictions of the optimal solution, such as from a machine-learning algorithm, has garnered significant attention in recent years, particularly in facility location problems. Moving beyond the traditional focus on utilitarian and egalitarian objectives, we design learning-augmented facility location mechanisms for the envy ratio objective, a fairness metric defined as the maximum ratio between the utilities of any two agents. For the deterministic setting, we propose a mechanism which utilizes predictions to achieve $\alpha$-consistency and $\frac{\alpha}{\alpha - 1}$-robustness for a selected parameter $\alpha \in [1, 2]$, and prove its optimality. We also resolve open questions raised by Ding et al. [2020], devising a randomized mechanism without predictions to improve upon the best-known approximation ratio from $2$ to $1. 8944$. Building upon these advancements, we construct a novel randomized mechanism which incorporates predictions to achieve improved performance guarantees.