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Metric Distortion under Group-Fair Objectives arXiv.cs.GT Pub Date : 2024-04-22 Georgios Amanatidis, Elliot Anshelevich, Christopher Jerrett, Alexandros A. Voudouris
We consider a voting problem in which a set of agents have metric preferences over a set of alternatives, and are also partitioned into disjoint groups. Given information about the preferences of the agents and their groups, our goal is to decide an alternative to approximately minimize an objective function that takes the groups of agents into account. We consider two natural group-fair objectives
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Facility Location Problems with Capacity Constraints: Two Facilities and Beyond arXiv.cs.GT Pub Date : 2024-04-21 Gennaro Auricchio, Zihe Wang, Jie Zhang
In this paper, we investigate the Mechanism Design aspects of the $m$-Capacitated Facility Location Problem ($m$-CFLP) on a line. We focus on two frameworks. In the first framework, the number of facilities is arbitrary, all facilities have the same capacity, and the number of agents is equal to the total capacity of all facilities. In the second framework, we aim to place two facilities, each with
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A Simplified Analysis of the Ascending Auction to Sell a Matroid Base arXiv.cs.GT Pub Date : 2024-04-18 Britta Peis, Niklas Rieken
We give a simpler analysis of the ascending auction of Bikhchandani, de Vries, Schummer, and Vohra to sell a welfare-maximizing base of a matroid at Vickrey prices. The new proofs for economic efficiency and the charge of Vickrey prices only require a few matroid folklore theorems, therefore shortening the analysis of the design goals of the auction significantly.
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Oracle-Augmented Prophet Inequalities arXiv.cs.GT Pub Date : 2024-04-18 Sariel Har-Peled, Elfarouk Harb, Vasilis Livanos
In the classical prophet inequality settings, a gambler is given a sequence of $n$ random variables $X_1, \dots, X_n$, taken from known distributions, observes their values in this (potentially adversarial) order, and select one of them, immediately after it is being observed, so that its value is as high as possible. The classical \emph{prophet inequality} shows a strategy that guarantees a value
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Maximin Shares in Hereditary Set Systems arXiv.cs.GT Pub Date : 2024-04-17 Halvard Hummel
We consider the problem of fairly allocating a set of indivisible items under the criteria of the maximin share guarantee. Specifically, we study approximation of maximin share allocations under hereditary set system valuations, in which each valuation function is based on the independent sets of an underlying hereditary set systems. Using a lone divider approach, we show the existence of $1/2$-approximate
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Strategic Network Inspection with Location-Specific Detection Capabilities arXiv.cs.GT Pub Date : 2024-04-17 Bastián Bahamondes, Mathieu Dahan
We consider a two-person network inspection game, in which a defender positions a limited number of detectors to detect multiple attacks caused by an attacker. We assume that detection is imperfect, and each detector location is associated with a probability of detecting attacks within its set of monitored network components. The objective of the defender (resp. attacker) is to minimize (resp. maximize)
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Ordinal Maximin Guarantees for Group Fair Division arXiv.cs.GT Pub Date : 2024-04-17 Pasin Manurangsi, Warut Suksompong
We investigate fairness in the allocation of indivisible items among groups of agents using the notion of maximin share (MMS). While previous work has shown that no nontrivial multiplicative MMS approximation can be guaranteed in this setting for general group sizes, we demonstrate that ordinal relaxations are much more useful. For example, we show that if $n$ agents are distributed equally across
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Decreasing Wages in Gig Economy: A Game Theoretic Explanation Using Mathematical Program Networks arXiv.cs.GT Pub Date : 2024-04-16 Pravesh Koirala, Forrest Laine
Gig economy consists of two market groups connected via an intermediary. Popular examples are rideshares where passengers and drivers are mediated via platforms such as Uber and Lyft. In a duopoly market, the platforms must compete to attract not only the passengers by providing a lower rate but also the drivers by providing better wages. While this should indicate better driver payout, as platforms
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Privacy Can Arise Endogenously in an Economic System with Learning Agents arXiv.cs.GT Pub Date : 2024-04-16 Nivasini Ananthakrishnan, Tiffany Ding, Mariel Werner, Sai Praneeth Karimireddy, Michael I. Jordan
We study price-discrimination games between buyers and a seller where privacy arises endogenously--that is, utility maximization yields equilibrium strategies where privacy occurs naturally. In this game, buyers with a high valuation for a good have an incentive to keep their valuation private, lest the seller charge them a higher price. This yields an equilibrium where some buyers will send a signal
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HSVI-based Online Minimax Strategies for Partially Observable Stochastic Games with Neural Perception Mechanisms arXiv.cs.GT Pub Date : 2024-04-16 Rui Yan, Gabriel Santos, Gethin Norman, David Parker, Marta Kwiatkowska
We consider a variant of continuous-state partially-observable stochastic games with neural perception mechanisms and an asymmetric information structure. One agent has partial information, with the observation function implemented as a neural network, while the other agent is assumed to have full knowledge of the state. We present, for the first time, an efficient online method to compute an $\varepsilon$-minimax
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Generative AI for Game Theory-based Mobile Networking arXiv.cs.GT Pub Date : 2024-04-15 Long He, Geng Sun, Dusit Niyato, Hongyang Du, Fang Mei, Jiawen Kang, Mérouane Debbah, and Zhu Han
With the continuous advancement of network technology, various emerging complex networking optimization problems opened up a wide range of applications utilizating of game theory. However, since game theory is a mathematical framework, game theory-based solutions often require the experience and knowledge of human experts. Recently, the remarkable advantages exhibited by generative artificial intelligence
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Servers Placement Scheme Based on All-pay Auction Framework in Mobile Edge Computing arXiv.cs.GT Pub Date : 2024-04-15 Yun Xia
Task offloading plays a pivotal role in mobile edge computing, enabling terminal devices to enhance task execution efficiency and conserve energy. However, servers are reluctant to offer services without compensation. Currently, pricing mechanisms are commonly employed to incentivize servers to serve terminal devices, with servers earning revenue through payments from these devices. Given the rapid
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Faster Game Solving via Hyperparameter Schedules arXiv.cs.GT Pub Date : 2024-04-13 Naifeng Zhang, Stephen McAleer, Tuomas Sandholm
The counterfactual regret minimization (CFR) family of algorithms consists of iterative algorithms for imperfect-information games. In two-player zero-sum games, the time average of the iterates converges to a Nash equilibrium. The state-of-the-art prior variants, Discounted CFR (DCFR) and Predictive CFR$^+$ (PCFR$^+$) are the fastest known algorithms for solving two-player zero-sum games in practice
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Facility Assignment with Fair Cost Sharing: Equilibrium and Mechanism Design arXiv.cs.GT Pub Date : 2024-04-13 Mengfan Ma, Mingyu Xiao, Tian Bai, Xin Cheng
In the one-dimensional facility assignment problem, m facilities and n agents are positioned along the real line. Each agent will be assigned to a single facility to receive service. Each facility incurs a building cost, which is shared equally among the agents utilizing it. Additionally, each agent independently bears a connection cost to access a facility. Thus, an agent's cost is the sum of the
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Do Large Language Models Learn Human-Like Strategic Preferences? arXiv.cs.GT Pub Date : 2024-04-11 Jesse Roberts, Kyle Moore, Doug Fisher
We evaluate whether LLMs learn to make human-like preference judgements in strategic scenarios as compared with known empirical results. We show that Solar and Mistral exhibit stable value-based preference consistent with human in the prisoner's dilemma, including stake-size effect, and traveler's dilemma, including penalty-size effect. We establish a relationship between model size, value based preference
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QI-DPFL: Quality-Aware and Incentive-Boosted Federated Learning with Differential Privacy arXiv.cs.GT Pub Date : 2024-04-12 Wenhao Yuan, Xuehe Wang
Federated Learning (FL) has increasingly been recognized as an innovative and secure distributed model training paradigm, aiming to coordinate multiple edge clients to collaboratively train a shared model without uploading their private datasets. The challenge of encouraging mobile edge devices to participate zealously in FL model training procedures, while mitigating the privacy leakage risks during
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Auctions with LLM Summaries arXiv.cs.GT Pub Date : 2024-04-11 Kumar Avinava Dubey, Zhe Feng, Rahul Kidambi, Aranyak Mehta, Di Wang
We study an auction setting in which bidders bid for placement of their content within a summary generated by a large language model (LLM), e.g., an ad auction in which the display is a summary paragraph of multiple ads. This generalizes the classic ad settings such as position auctions to an LLM generated setting, which allows us to handle general display formats. We propose a novel factorized framework
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Robustness of voting mechanisms to external information in expectation arXiv.cs.GT Pub Date : 2024-04-11 Yiling Chen, Jessie Finocchiaro
Analyses of voting algorithms often overlook informational externalities shaping individual votes. For example, pre-polling information often skews voters towards candidates who may not be their top choice, but who they believe would be a worthwhile recipient of their vote. In this work, we aim to understand the role of external information in voting outcomes. We study this by analyzing (1) the probability
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Tree Splitting Based Rounding Scheme for Weighted Proportional Allocations with Subsidy arXiv.cs.GT Pub Date : 2024-04-11 Xiaowei Wu, Shengwei Zhou
We consider the problem of allocating $m$ indivisible items to a set of $n$ heterogeneous agents, aiming at computing a proportional allocation by introducing subsidy (money). It has been shown by Wu et al. (WINE 2023) that when agents are unweighted a total subsidy of $n/4$ suffices (assuming that each item has value/cost at most $1$ to every agent) to ensure proportionality. When agents have general
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Altruism Improves Congestion in Series-Parallel Nonatomic Congestion Games arXiv.cs.GT Pub Date : 2024-04-10 Colton Hill, Philip N. Brown
Self-interested routing polices from individual users in a system can collectively lead to poor aggregate congestion in routing networks. The introduction of altruistic agents, whose goal is to benefit other agents in the system, can seemingly improve aggregate congestion. However, it is known in that in some network routing problems, altruistic agents can actually worsen congestion compared to that
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Algorithms and Analysis for Optimizing Robust Objectives in Fair Machine Learning arXiv.cs.GT Pub Date : 2024-04-10 Cyrus Cousins
The original position or veil of ignorance argument of John Rawls, perhaps the most famous argument for egalitarianism, states that our concept of fairness, justice, or welfare should be decided from behind a veil of ignorance, and thus must consider everyone impartially (invariant to our identity). This can be posed as a zero-sum game, where a Daemon constructs a world, and an adversarial Angel then
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Towards a Game-theoretic Understanding of Explanation-based Membership Inference Attacks arXiv.cs.GT Pub Date : 2024-04-10 Kavita Kumari, Murtuza Jadliwala, Sumit Kumar Jha, Anindya Maiti
Model explanations improve the transparency of black-box machine learning (ML) models and their decisions; however, they can also be exploited to carry out privacy threats such as membership inference attacks (MIA). Existing works have only analyzed MIA in a single "what if" interaction scenario between an adversary and the target ML model; thus, it does not discern the factors impacting the capabilities
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Mechanism Design for ZK-Rollup Prover Markets arXiv.cs.GT Pub Date : 2024-04-09 Wenhao Wang, Lulu Zhou, Aviv Yaish, Fan Zhang, Ben Fisch, Benjamin Livshits
In ZK-Rollups, provers spend significant computational resources to generate validity proofs. Their costs should be compensated properly, so a sustainable prover market can form over time. Existing transaction fee mechanisms (TFMs) such as EIP-1559, however, do not work in this setting, as EIP-1559 only generates negligible revenue because of burning, while provers often create or purchase specialized
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Design and Characterization of Strategy-Proof Mechanisms for Two-Facility Game on a Line arXiv.cs.GT Pub Date : 2024-04-09 Pinyan Lu, Zihan Luo, Jialin Zhang
We focus on the problem of placing two facilities along a linear space to serve a group of agents. Each agent is committed to minimizing the distance between her location and the closest facility. A mechanism is an algorithm that maps the reported agent locations to the facility locations. We are interested in mechanisms without money that are deterministic, strategy-proof, and provide a bounded approximation
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General Lotto Games with Scouts: Information versus Strength arXiv.cs.GT Pub Date : 2024-04-08 Jan-Tino Brethouwer, Bart van Ginkel, Roy Lindelauf
We introduce General Lotto games with Scouts: a General Lotto game with asymmetric information. There are two players, Red and Blue, who both allocate resources to a field. However, scouting capabilities afford Blue to gain information, with some probability, on the number of Red's resources before allocating his own. We derive optimal strategies for this game in the case of a single field. In addition
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Fair Lotteries for Participatory Budgeting arXiv.cs.GT Pub Date : 2024-04-08 Haris Aziz, Xinhang Lu, Mashbat Suzuki, Jeremy Vollen, Toby Walsh
In pursuit of participatory budgeting (PB) outcomes with broader fairness guarantees, we initiate the study of lotteries over discrete PB outcomes. As the projects have heterogeneous costs, the amount spent may not be equal ex ante and ex post. To address this, we develop a technique to bound the amount by which the ex-post spend differs from the ex-ante spend -- the property is termed budget balanced
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The Hyperdrive Protocol: An Automated Market Maker for Fixed and Variable Rates arXiv.cs.GT Pub Date : 2024-04-07 Jonny Rhea, Alex Towle, Mihai Cosma
Hyperdrive is a protocol designed to facilitate the trading of fixed and variable rate assets. The protocol's unique pricing model consolidates liquidity into a single pool which addresses the challenges of fragmented liquidity across terms, eliminates the need for rollovers, and allows terms to be issued on demand. Its design meaningfully improves trading efficiency, liquidity provisioning, and user
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Game-theoretic Distributed Learning Approach for Heterogeneous-cost Task Allocation with Budget Constraints arXiv.cs.GT Pub Date : 2024-04-05 Weiyi Yang, Xiaolu Liu, Lei He, Yonghao Du, Yingwu Chen
This paper investigates heterogeneous-cost task allocation with budget constraints (HCTAB), wherein heterogeneity is manifested through the varying capabilities and costs associated with different agents for task execution. Different from the centralized optimization-based method, the HCTAB problem is solved using a fully distributed framework, and a coalition formation game is introduced to provide
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A Reduction from Multi-Parameter to Single-Parameter Bayesian Contract Design arXiv.cs.GT Pub Date : 2024-04-04 Matteo Castiglioni, Junjie Chen, Minming Li, Haifeng Xu, Song Zuo
The main result of this paper is an almost approximation-preserving polynomial-time reduction from the most general multi-parameter Bayesian contract design (BCD) to single-parameter BCD. That is, for any multi-parameter BCD instance $I^M$, we construct a single-parameter instance $I^S$ such that any $\beta$-approximate contract (resp. menu of contracts) of $I^S$ can in turn be converted to a $(\beta
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Bounds of Block Rewards in Honest PinFi Systems arXiv.cs.GT Pub Date : 2024-04-01 Qi He, Yunwei Mao, Ju Li
PinFi is a class of novel protocols for decentralized pricing of dissipative assets, whose value naturally declines over time. Central to the protocol's functionality and its market efficiency is the role of liquidity providers (LPs). This study addresses critical stability and sustainability challenges within the protocol, namely: the propensity of LPs to prefer selling in external markets over participation
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Shill-Proof Auctions arXiv.cs.GT Pub Date : 2024-03-30 Andrew Komo, Scott Duke Kominers, Tim Roughgarden
In a single-item auction, a duplicitous seller may masquerade as one or more bidders in order to manipulate the clearing price. This paper characterizes auction formats that are shill-proof: a profit-maximizing seller has no incentive to submit any shill bids. We distinguish between strong shill-proofness, in which a seller with full knowledge of bidders' valuations can never profit from shilling,
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Settling the Communication Complexity of VCG-based Mechanisms for all Approximation Guarantees arXiv.cs.GT Pub Date : 2024-03-31 Frederick V. Qiu, S. Matthew Weinberg
We consider truthful combinatorial auctions with items $M = [m]$ for sale to $n$ bidders, where each bidder $i$ has a private monotone valuation $v_i : 2^M \to R_+$. Among truthful mechanisms, maximal-in-range (MIR) mechanisms achieve the best-known approximation guarantees among all poly-communication deterministic truthful mechanisms in all previously-studied settings. Our work settles the communication
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Winning Without Observing Payoffs: Exploiting Behavioral Biases to Win Nearly Every Round arXiv.cs.GT Pub Date : 2024-03-29 Avrim Blum, Melissa Dutz
Gameplay under various forms of uncertainty has been widely studied. Feldman et al. (2010) studied a particularly low-information setting in which one observes the opponent's actions but no payoffs, not even one's own, and introduced an algorithm which guarantees one's payoff nonetheless approaches the minimax optimal value (i.e., zero) in a symmetric zero-sum game. Against an opponent playing a minimax-optimal
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TASR: A Novel Trust-Aware Stackelberg Routing Algorithm to Mitigate Traffic Congestion arXiv.cs.GT Pub Date : 2024-03-28 Doris E. M. Brown, Venkata Sriram Siddhardh Nadendla, Sajal K. Das
Stackelberg routing platforms (SRP) reduce congestion in one-shot traffic networks by proposing optimal route recommendations to selfish travelers. Traditionally, Stackelberg routing is cast as a partial control problem where a fraction of traveler flow complies with route recommendations, while the remaining respond as selfish travelers. In this paper, a novel Stackelberg routing framework is formulated
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Ordering Collective Unit Tasks: from Scheduling to Computational Social Choice arXiv.cs.GT Pub Date : 2024-03-28 Martin Durand, Fanny Pascual
We study the collective schedules problem, which consists in computing a one machine schedule of a set of tasks, knowing that a set of individuals (also called voters) have preferences regarding the order of the execution of the tasks. Our aim is to return a consensus schedule. We consider the setting in which all tasks have the same length -- such a schedule can therefore also be viewed as a ranking
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Detecting and taking Project Interactions into account in Participatory Budgeting arXiv.cs.GT Pub Date : 2024-03-28 Martin Durand, Fanny Pascual
The aim of this paper is to introduce models and algorithms for the Participatory Budgeting problem when projects can interact with each other. In this problem, the objective is to select a set of projects that fits in a given budget. Voters express their preferences over the projects and the goal is then to find a consensus set of projects that does not exceed the budget. Our goal is to detect such
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Gamu Blue: A Practical Tool for Game Theory Security Equilibria arXiv.cs.GT Pub Date : 2024-03-28 Ameer Taweel, Burcu Yıldız, Alptekin Küpçü
The application of game theory in cybersecurity enables strategic analysis, adversarial modeling, and optimal decision-making to address security threats' complex and dynamic nature. Previous studies by Abraham et al. and Bi\c{c}er et al. presented various definitions of equilibria to examine the security aspects of games involving multiple parties. Nonetheless, these definitions lack practical and
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Efficient Preference Elicitation in Iterative Combinatorial Auctions with Many Participants arXiv.cs.GT Pub Date : 2024-03-28 Ryota Maruo, Hisashi Kashima
We study the problem of achieving high efficiency in iterative combinatorial auctions (ICAs). ICAs are a kind of combinatorial auction where the auctioneer interacts with bidders to gather their valuation information using a limited number of queries, aiming for efficient allocation. Preference elicitation, a process that incrementally asks bidders to value bundles while refining the outcome allocation
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Collective schedules: axioms and algorithms arXiv.cs.GT Pub Date : 2024-03-27 Martin Durand, Fanny Pascual
The collective schedules problem consists in computing a schedule of tasks shared between individuals. Tasks may have different duration, and individuals have preferences over the order of the shared tasks. This problem has numerous applications since tasks may model public infrastructure projects, events taking place in a shared room, or work done by co-workers. Our aim is, given the preferred schedules
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The Metric Distortion of Randomized Social Choice Functions: C1 Maximal Lottery Rules and Simulations arXiv.cs.GT Pub Date : 2024-03-27 Fabian Frank, Patrick Lederer
The metric distortion of a randomized social choice function (RSCF) quantifies its worst-case approximation ratio of the optimal social cost when the voters' costs for alternatives are given by distances in a metric space. This notion has recently attracted significant attention as numerous RSCFs that aim to minimize the metric distortion have been suggested. However, such tailored voting rules usually
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Local (coarse) correlated equilibria in non-concave games arXiv.cs.GT Pub Date : 2024-03-27 Mete Şeref Ahunbay
We investigate local notions of correlated equilibria, distributions of actions for smooth games such that players do not incur any regret against modifications of their strategies along a set of continuous vector fields. Our analysis shows that such equilibria are intrinsically linked to the projected gradient dynamics of the game. We identify the equivalent of coarse equilibria in this setting when
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Prediction-sharing During Training and Inference arXiv.cs.GT Pub Date : 2024-03-26 Yotam Gafni, Ronen Gradwohl, Moshe Tennenholtz
Two firms are engaged in a competitive prediction task. Each firm has two sources of data -- labeled historical data and unlabeled inference-time data -- and uses the former to derive a prediction model, and the latter to make predictions on new instances. We study data-sharing contracts between the firms. The novelty of our study is to introduce and highlight the differences between contracts that
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An Equilibrium Analysis of the Arad-Rubinstein Game arXiv.cs.GT Pub Date : 2024-03-25 Christian Ewerhart, Stanisław Kaźmierowski
Colonel Blotto games with discrete strategy spaces effectively illus- trate the intricate nature of multidimensional strategic reasoning. This paper studies the equilibrium set of such games where, in line with prior experimental work, the tie-breaking rule is allowed to be flexible. We begin by pointing out that equilibrium constructions known from the literature extend to our class of games. However
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Efficient Method for Finding Optimal Strategies in Chopstick Auctions with Uniform Objects Values arXiv.cs.GT Pub Date : 2024-03-25 Stanisław Kaźmierowski, Marcin Dziubiński
We propose an algorithm for computing Nash equilibria (NE) in a class of conflicts with multiple battlefields with uniform battlefield values and a non-linear aggregation function. By expanding the symmetrization idea of Hart [9], proposed for the Colonel Blotto game, to the wider class of symmetric conflicts with multiple battlefields, we reduce the number of strategies of the players by an exponential
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On the Stability of Learning in Network Games with Many Players arXiv.cs.GT Pub Date : 2024-03-23 Aamal Hussain, Dan Leonte, Francesco Belardinelli, Georgios Piliouras
Multi-agent learning algorithms have been shown to display complex, unstable behaviours in a wide array of games. In fact, previous works indicate that convergent behaviours are less likely to occur as the total number of agents increases. This seemingly prohibits convergence to stable strategies, such as Nash Equilibria, in games with many players. To make progress towards addressing this challenge
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Strategic Network Creation for Enabling Greedy Routing arXiv.cs.GT Pub Date : 2024-03-22 Julian Berger, Tobias Friedrich, Pascal Lenzner, Paraskevi Machaira, Janosch Ruff
In this paper, we present the first game-theoretic network creation model that incorporates greedy routing, i.e., the agents in our model are embedded in some metric space and strive for creating a network where all-pairs greedy routing is enabled. In contrast to graph-theoretic shortest paths, our agents route their traffic along greedy paths, which are sequences of nodes where the distance in the
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Approximation Algorithms for School Assignment: Group Fairness and Multi-criteria Optimization arXiv.cs.GT Pub Date : 2024-03-22 Santhini K. A., Kamesh Munagala, Meghana Nasre, Govind S. Sankar
We consider the problem of assigning students to schools, when students have different utilities for schools and schools have capacity. There are additional group fairness considerations over students that can be captured either by concave objectives, or additional constraints on the groups. We present approximation algorithms for this problem via convex program rounding that achieve various trade-offs
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Freshness-aware Block Propagation Optimization in 6G-based Web 3.0: An Evolutionary Game Approach arXiv.cs.GT Pub Date : 2024-03-19 Jinbo Wen, Jiawen Kang, Zehui Xiong, Hongyang Du, Zhaohui Yang, Dusit Niyato, Meng Shen, Yutao Jiao, Yang Zhang
Driven by the aspiration to establish a decentralized digital economy, Web 3.0 is emerging as the fundamental technology for digital transformation. Incorporating the promising sixth-generation (6G) technology with large bandwidth and space-air-ground integrated coverage, 6G-based Web 3.0 holds great potential in empowering users with enhanced data control and facilitating secure peer-to-peer transactions
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Cooperative Agri-Food Export under Minimum Quantity Commitments arXiv.cs.GT Pub Date : 2024-03-18 Luis A. Guardiola, Behzad Hezarkhani, Ana Meca
International trade can be a profitable business for agri-food communities. However, access to international markets can be costly and thus unattainable for small and medium sized enterprises (SMEs). This problem is exacerbated under trade policies which require minimum quantity commitments (MQCs) on export volumes, e.g., licensing tariff rate quota (TRQ) mechanisms. We show how cooperative exporting
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Expanding the Resolution Boundary of Outcome-Based Imperfect-Recall Abstraction in Games with Ordered Signals arXiv.cs.GT Pub Date : 2024-03-18 Yanchang Fu, Junge Zhang, Dongdong Bai, Lingyun Zhao, Jialu Song, Kaiqi Huang
In the development of advanced Texas Hold'em AI systems, abstraction technology has garnered widespread attention due to its significant effect in simplifying game complexity. This study adopts a more specific model, the games of ordered signal, to describe Texas Hold'em-style games and optimizes this model to streamline its mathematical representation and broaden its applicability. By transitioning
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A Simple 2-Approximation Algorithm For Minimum Manhattan Network Problem arXiv.cs.GT Pub Date : 2024-03-18 Md. Musfiqur Rahman Sanim, Safrunnesa Saira, Fatin Faiaz Ahsan, Rajon Bardhan, S. M. Ferdous
Given a n points in two dimensional space, a Manhattan Network G is a network that connects all n points with either horizontal or vertical edges, with the property that for any two point in G should be connected by a Manhattan path and distance between this two points is equal to Manhattan Distance. The Minimum Manhattan Network problem is to find a Manhattan network with minimum network length, i
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Subgame Optimal and Prior-Independent Online Algorithms arXiv.cs.GT Pub Date : 2024-03-15 Jason Hartline, Aleck Johnsen, Anant Shah
This paper takes a game theoretic approach to the design and analysis of online algorithms and illustrates the approach on the finite-horizon ski-rental problem. This approach allows beyond worst-case analysis of online algorithms. First, we define "subgame optimality" which is stronger than worst case optimality in that it requires the algorithm to take advantage of an adversary not playing a worst
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DDPS: Dynamic Differential Pricing-based Edge Offloading System with Energy Harvesting Devices arXiv.cs.GT Pub Date : 2024-03-15 Hai Xue, Yun Xia, Neal N. Xiong, Di Zhang, Songwen Pei
Mobile edge computing (MEC) paves the way to alleviate the burden of energy and computation of mobile users (MUs) by offloading tasks to the network edge. To enhance the MEC server utilization by optimizing its resource allocation, a well-designed pricing strategy is indispensable. In this paper, we consider the edge offloading scenario with energy harvesting devices, and propose a dynamic differential
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The Query Complexity of Contracts arXiv.cs.GT Pub Date : 2024-03-14 Paul Dütting, Michal Feldman, Yoav Gal-Tzur, Aviad Rubinstein
Algorithmic contract design is a new frontier in the intersection of economics and computation, with combinatorial contracts being a core problem in this domain. A central model within combinatorial contracts explores a setting where a principal delegates the execution of a task, which can either succeed or fail, to an agent. The agent can choose any subset among a given set of costly actions, where
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Sequential Contracts arXiv.cs.GT Pub Date : 2024-03-14 Tomer Ezra, Michal Feldman, Maya Schlesinger
We study the principal-agent setting, where a principal delegates the execution of a costly project to an agent. In the classical model, the agent chooses an action among a set of available actions. Every action is associated with some cost, and leads to a stochastic outcome for the project. The agent's action is hidden from the principal, who only observes the outcome. The principal incentivizes the
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All-pay Auction Based Profit Maximization in End-to-End Computation Offloading System arXiv.cs.GT Pub Date : 2024-03-14 Hai Xue, Yun Xia, Di Zhang, Honghua Wei, Xiaolong Xu
Pricing is an important issue in mobile edge computing. How to appropriately determine the bid of end user (EU) is an incentive factor for edge cloud (EC) to offer service. In this letter, we propose an equilibrium pricing scheme based on the all-pay auction model in end-to-end collaboration environment, wherein all EUs can acquire the service at a lower price than the own value of the required resource
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Data Monetization Pathways and Complex Dynamic Game Equilibrium Analysis in the Energy Industry arXiv.cs.GT Pub Date : 2024-03-12 Zongxian Wang, Jie Song
As the most critical production factor in the era of the digital economy, data will have a significant impact on social production and development. Energy enterprises possess data that is interconnected with multiple industries, characterized by diverse needs, sensitivity, and long-term nature. The path to monetizing energy enterprises' data is challenging yet crucial. This paper explores the game-theoretic
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Multi-Apartment Rent Division arXiv.cs.GT Pub Date : 2024-03-12 Ariel D. Procaccia, Benjamin Schiffer, Shirley Zhang
Rent division is the well-studied problem of fairly assigning rooms and dividing rent among a set of roommates within a single apartment. A shortcoming of existing solutions is that renters are assumed to be considering apartments in isolation, whereas in reality, renters can choose among multiple apartments. In this paper, we generalize the rent division problem to the multi-apartment setting, where
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The Flow Game: Leximin and Leximax Core Imputations arXiv.cs.GT Pub Date : 2024-03-09 Rohith R. Gangam, Naveen Garg, Parnian Shahkar, Vijay V. Vazirani
Recently [Vaz24] gave mechanisms for finding leximin and leximax core imputations for the assignment game and remarked, "Within the area of algorithm design, the "right" technique for solving several types of algorithmic questions was first discovered in the context of matching and later these insights were applied to other problems. We expect a similar phenomenon here." One of the games explicitly
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Synthesis of Robust Optimal Strategies in Weighted Timed Games arXiv.cs.GT Pub Date : 2024-03-11 Benjamin Monmege, Julie Parreaux, Pierre-Alain Reynier
Weighted Timed Games (WTG for short) are the most widely used model to describe controller synthesis problems involving real-time issues. The synthesized strategies rely on a perfect measure of time elapse, which is not realistic in practice. In order to produce strategies tolerant to timing imprecisions, we rely on a notion of robustness first introduced for timed automata. More precisely, WTGs are