arXiv subjects

Seyed Majid Zahedi

Publications and source records attributed to Seyed Majid Zahedi.

5 recordsLinked to original sources

arXiv2026

Credit Fairness: Online Fairness In Shared Resource Pools

We study repeated allocation of shared resources among agents with time-varying demands and capped linear utilities. In this setting, independently maximizing the minimum endowment-normalized utility in each round satisfies sharing incentives (agents weakly prefer participating in the mechanism to not participating), strategyproofness (agents have no incentive to misreport their demands), and Pareto efficiency. However, this max-min mechanism can lead to large disparities in the total resources received by agents, even when they have the same average demand. We introduce credit fairness, a property that, together with Pareto efficiency, strengthens sharing incentives by giving agents who lend resources in early rounds priority toward recouping those resources in later rounds. Credit fairness can be achieved in conjunction with either Pareto efficiency or strategyproofness individually, but we show that, under anonymity, it cannot be achieved together with both. We propose a mechanism that is credit fair and Pareto efficient, and evaluate it in a computational resource-sharing setting.

cs.GT↗

arXiv2026

Hydra: Robust Hardware-Assisted Malware Detection

Malware detection using Hardware Performance Counters (HPCs) offers a promising, low-overhead approach for monitoring program behavior. However, a fundamental architectural constraint, that only a limited number of hardware events can be monitored concurrently, creates a significant bottleneck, leading to detection blind spots. Prior work has primarily focused on optimizing machine learning models for a single, statically chosen event set, or on ensembling models over the same feature set. We argue that robustness requires diversifying not only the models, but also the underlying feature sets (i.e., the monitored hardware events) in order to capture a broader spectrum of program behavior. This observation motivates the following research question: Can detection performance be improved by trading temporal granularity for broader coverage, via the strategic scheduling of different feature sets over time? To answer this question, we propose Hydra, a novel detection mechanism that partitions execution traces into time slices and learns an effective schedule of feature sets and corresponding classifiers for deployment. By cycling through complementary feature sets, Hydra mitigates the limitations of a fixed monitoring perspective. Our experimental evaluation shows that Hydra significantly outperforms state-of-the-art single-feature-set baselines, achieving a 19.32% improvement in F1 score and a 60.23% reduction in false positive rate. These results underscore the importance of feature-set diversity and establish strategic multi-feature-set scheduling as an effective principle for robust, hardware-assisted malware detection.

cs.CR↗

arXiv2025

Asymptotically Fair and Truthful Allocation of Public Goods

We study the fair and truthful allocation of m divisible public items among n agents, each with distinct preferences for the items. To aggregate agents' preferences fairly, we focus on finding a core solution. For divisible items, a core solution always exists and can be calculated by maximizing the Nash welfare objective. However, such a solution is easily manipulated; agents might have incentives to misreport their preferences. To mitigate this, the current state-of-the-art finds an approximate core solution with high probability while ensuring approximate truthfulness. However, this approach has two main limitations. First, due to several approximations, the approximation error in the core could grow with n, resulting in a non-asymptotic core solution. This limitation is particularly significant as public-good allocation mechanisms are frequently applied in scenarios involving a large number of agents, such as the allocation of public tax funds for municipal projects. Second, implementing the current approach for practical applications proves to be a highly nontrivial task. To address these limitations, we introduce PPGA, a (differentially) Private Public-Good Allocation algorithm, and show that it attains asymptotic truthfulness and finds an asymptotic core solution with high probability. Additionally, to demonstrate the practical applicability of our algorithm, we implement PPGA and empirically study its properties using municipal participatory budgeting data.

cs.GT↗

arXiv2024

Beyond Theorems: A Counterexample to Potential Markov Game Criteria

There are only limited classes of multi-player stochastic games in which independent learning is guaranteed to converge to a Nash equilibrium. Markov potential games are a key example of such classes. Prior work has outlined sets of sufficient conditions for a stochastic game to qualify as a Markov potential game. However, these conditions often impose strict limitations on the game's structure and tend to be challenging to verify. To address these limitations, Mguni et al. [12] introduce a relaxed notion of Markov potential games and offer an alternative set of necessary conditions for categorizing stochastic games as potential games. Under these conditions, the authors claim that a deterministic Nash equilibrium can be computed efficiently by solving a dual Markov decision process. In this paper, we offer evidence refuting this claim by presenting a counterexample.

cs.GT↗

arXiv2023

IRS: An Incentive-compatible Reward Scheme for Algorand

Founded in 2017, Algorand is one of the world's first carbon-negative, public blockchains inspired by proof of stake. Algorand uses a Byzantine agreement protocol to add new blocks to the blockchain. The protocol can tolerate malicious users as long as a supermajority of the stake is controlled by non-malicious users. The protocol achieves about 100x more throughput compared to Bitcoin and can be easily scaled to millions of nodes. Despite its impressive features, Algorand lacks a reward-distribution scheme that can effectively incentivize nodes to participate in the protocol. In this work, we study the incentive issue in Algorand through the lens of game theory. We model the Algorand protocol as a Bayesian game and propose a novel reward scheme to address the incentive issue in Algorand. We derive necessary conditions to ensure that participation in the protocol is a Bayesian Nash equilibrium under our proposed reward scheme even in the presence of a malicious adversary. We also present quantitative analysis of our proposed reward scheme by applying it to two real-world deployment scenarios. We estimate the costs of running an Algorand node and simulate the protocol to measure the overheads in terms of computation, storage, and networking.

cs.GT↗