arXiv - CS - Computer Science and Game Theory最新文献_第9页

Online Test Synthesis From Requirements: Enhancing Reinforcement Learning with Game Theory 从需求出发的在线测试合成：用博弈论加强强化学习

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-26 DOI: arxiv-2407.18994

Ocan SankurDEVINE, UR, Thierry JéronDEVINE, UR, Nicolas MarkeyDEVINE, UR, David MentréMERCE-France, Reiya Noguchi

引用次数: 0

Nested replicator dynamics, nested logit choice, and similarity-based learning 嵌套复制动态、嵌套对数选择和基于相似性的学习

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-25 DOI: arxiv-2407.17815

Panayotis Mertikopoulos, William H. Sandholm

{"title":"Nested replicator dynamics, nested logit choice, and similarity-based learning","authors":"Panayotis Mertikopoulos, William H. Sandholm","doi":"arxiv-2407.17815","DOIUrl":"https://doi.org/arxiv-2407.17815","url":null,"abstract":"We consider a model of learning and evolution in games whose action sets are\u0000endowed with a partition-based similarity structure intended to capture\u0000exogenous similarities between strategies. In this model, revising agents have\u0000a higher probability of comparing their current strategy with other strategies\u0000that they deem similar, and they switch to the observed strategy with\u0000probability proportional to its payoff excess. Because of this implicit bias\u0000toward similar strategies, the resulting dynamics - which we call the nested\u0000replicator dynamics - do not satisfy any of the standard monotonicity\u0000postulates for imitative game dynamics; nonetheless, we show that they retain\u0000the main long-run rationality properties of the replicator dynamics, albeit at\u0000quantitatively different rates. We also show that the induced dynamics can be\u0000viewed as a stimulus-response model in the spirit of Erev & Roth (1998), with\u0000choice probabilities given by the nested logit choice rule of Ben-Akiva (1973)\u0000and McFadden (1978). This result generalizes an existing relation between the\u0000replicator dynamics and the exponential weights algorithm in online learning,\u0000and provides an additional layer of interpretation to our analysis and results.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"45 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Approximately Strategy-Proof Tournament Rules for Collusions of Size at Least Three 关于规模至少为三的串通的近似策略证明锦标赛规则

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-24 DOI: arxiv-2407.17569

David Mikšaník, Ariel Schvartzman, Jan Soukup

{"title":"On Approximately Strategy-Proof Tournament Rules for Collusions of Size at Least Three","authors":"David Mikšaník, Ariel Schvartzman, Jan Soukup","doi":"arxiv-2407.17569","DOIUrl":"https://doi.org/arxiv-2407.17569","url":null,"abstract":"A tournament organizer must select one of $n$ possible teams as the winner of\u0000a competition after observing all $binom{n}{2}$ matches between them. The\u0000organizer would like to find a tournament rule that simultaneously satisfies\u0000the following desiderata. It must be Condorcet-consistent (henceforth, CC),\u0000meaning it selects as the winner the unique team that beats all other teams (if\u0000one exists). It must also be strongly non-manipulable for groups of size $k$ at\u0000probability $alpha$ (henceforth, k-SNM-$alpha$), meaning that no subset of\u0000$leq k$ teams can fix the matches among themselves in order to increase the\u0000chances any of it's members being selected by more than $alpha$. Our\u0000contributions are threefold. First, wee consider a natural generalization of\u0000the Randomized Single Elimination Bracket rule from [Schneider et al. 2017] to\u0000$d$-ary trees and provide upper bounds to its manipulability. Then, we propose\u0000a novel tournament rule that is CC and 3-SNM-1/2, a strict improvement upon the\u0000concurrent work of [Dinev and Weinberg, 2022] who proposed a CC and 3-SNM-31/60\u0000rule. Finally, we initiate the study of reductions among tournament rules.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents 异构战略代理之间的均值估计数据共享

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-20 DOI: arxiv-2407.15881

Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy

{"title":"Data Sharing for Mean Estimation Among Heterogeneous Strategic Agents","authors":"Alex Clinton, Yiding Chen, Xiaojin Zhu, Kirthevasan Kandasamy","doi":"arxiv-2407.15881","DOIUrl":"https://doi.org/arxiv-2407.15881","url":null,"abstract":"We study a collaborative learning problem where $m$ agents estimate a vector\u0000$muinmathbb{R}^d$ by collecting samples from normal distributions, with each\u0000agent $i$ incurring a cost $c_{i,k} in (0, infty]$ to sample from the\u0000$k^{text{th}}$ distribution $mathcal{N}(mu_k, sigma^2)$. Instead of working\u0000on their own, agents can collect data that is cheap to them, and share it with\u0000others in exchange for data that is expensive or even inaccessible to them,\u0000thereby simultaneously reducing data collection costs and estimation error.\u0000However, when agents have different collection costs, we need to first decide\u0000how to fairly divide the work of data collection so as to benefit all agents.\u0000Moreover, in naive sharing protocols, strategic agents may under-collect and/or\u0000fabricate data, leading to socially undesirable outcomes. Our mechanism\u0000addresses these challenges by combining ideas from cooperative and\u0000non-cooperative game theory. We use ideas from axiomatic bargaining to divide\u0000the cost of data collection. Given such a solution, we develop a Nash\u0000incentive-compatible (NIC) mechanism to enforce truthful reporting. We achieve\u0000a $mathcal{O}(sqrt{m})$ approximation to the minimum social penalty (sum of\u0000agent estimation errors and data collection costs) in the worst case, and a\u0000$mathcal{O}(1)$ approximation under favorable conditions. We complement this\u0000with a hardness result, showing that $Omega(sqrt{m})$ is unavoidable in any\u0000NIC mechanism.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141781624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrated Resource Allocation and Strategy Synthesis in Safety Games on Graphs with Deception 有欺骗性的图上安全游戏中的综合资源分配与策略合成

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-19 DOI: arxiv-2407.14436

Abhishek N. Kulkarni, Matthew S. Cohen, Charles A. Kamhoua, Jie Fu

{"title":"Integrated Resource Allocation and Strategy Synthesis in Safety Games on Graphs with Deception","authors":"Abhishek N. Kulkarni, Matthew S. Cohen, Charles A. Kamhoua, Jie Fu","doi":"arxiv-2407.14436","DOIUrl":"https://doi.org/arxiv-2407.14436","url":null,"abstract":"Deception plays a crucial role in strategic interactions with incomplete\u0000information. Motivated by security applications, we study a class of two-player\u0000turn-based deterministic games with one-sided incomplete information, in which\u0000player 1 (P1) aims to prevent player 2 (P2) from reaching a set of target\u0000states. In addition to actions, P1 can place two kinds of deception resources:\u0000\"traps\" and \"fake targets\" to disinform P2 about the transition dynamics and\u0000payoff of the game. Traps \"hide the real\" by making trap states appear normal,\u0000while fake targets \"reveal the fiction\" by advertising non-target states as\u0000targets. We are interested in jointly synthesizing optimal decoy placement and\u0000deceptive defense strategies for P1 that exploits P2's misinformation. We\u0000introduce a novel hypergame on graph model and two solution concepts: stealthy\u0000deceptive sure winning and stealthy deceptive almost-sure winning. These\u0000identify states from which P1 can prevent P2 from reaching the target in a\u0000finite number of steps or with probability one without allowing P2 to become\u0000aware that it is being deceived. Consequently, determining the optimal decoy\u0000placement corresponds to maximizing the size of P1's deceptive winning region.\u0000Considering the combinatorial complexity of exploring all decoy allocations, we\u0000utilize compositional synthesis concepts to show that the objective function\u0000for decoy placement is monotone, non-decreasing, and, in certain cases, sub- or\u0000super-modular. This leads to a greedy algorithm for decoy placement, achieving\u0000a $(1 - 1/e)$-approximation when the objective function is sub- or\u0000super-modular. The proposed hypergame model and solution concepts contribute to\u0000understanding the optimal deception resource allocation and deception\u0000strategies in various security applications.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"64 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unravelling in Collaborative Learning 合作学习中的解构

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-19 DOI: arxiv-2407.14332

Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus

{"title":"Unravelling in Collaborative Learning","authors":"Aymeric Capitaine, Etienne Boursier, Antoine Scheid, Eric Moulines, Michael I. Jordan, El-Mahdi El-Mhamdi, Alain Durmus","doi":"arxiv-2407.14332","DOIUrl":"https://doi.org/arxiv-2407.14332","url":null,"abstract":"Collaborative learning offers a promising avenue for leveraging decentralized\u0000data. However, collaboration in groups of strategic learners is not a given. In\u0000this work, we consider strategic agents who wish to train a model together but\u0000have sampling distributions of different quality. The collaboration is\u0000organized by a benevolent aggregator who gathers samples so as to maximize\u0000total welfare, but is unaware of data quality. This setting allows us to shed\u0000light on the deleterious effect of adverse selection in collaborative learning.\u0000More precisely, we demonstrate that when data quality indices are private, the\u0000coalition may undergo a phenomenon known as unravelling, wherein it shrinks up\u0000to the point that it becomes empty or solely comprised of the worst agent. We\u0000show how this issue can be addressed without making use of external transfers,\u0000by proposing a novel method inspired by probabilistic verification. This\u0000approach makes the grand coalition a Nash equilibrium with high probability\u0000despite information asymmetry, thereby breaking unravelling.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On sibyl-proof mechanisms 关于防咝咝声机制

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-19 DOI: arxiv-2407.14485

Minghao Pan, Akaki Mamageishvili, Christoph Schlegel

引用次数: 0

Persuading while Learning 边学习边说服

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-19 DOI: arxiv-2407.13964

Itai Arieli, Yakov Babichenko, Dimitry Shaiderman, Xianwen Shi

引用次数: 0

Optimal Strategies in Ranked-Choice Voting 排序选择投票的最佳策略

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-18 DOI: arxiv-2407.13661

Sanyukta Deshpande, Nikhil Garg, Sheldon Jacobson

{"title":"Optimal Strategies in Ranked-Choice Voting","authors":"Sanyukta Deshpande, Nikhil Garg, Sheldon Jacobson","doi":"arxiv-2407.13661","DOIUrl":"https://doi.org/arxiv-2407.13661","url":null,"abstract":"Ranked Choice Voting (RCV) and Single Transferable Voting (STV) are widely\u0000valued; but are complex to understand due to intricate per-round vote\u0000transfers. Questions like determining how far a candidate is from winning or\u0000identifying effective election strategies are computationally challenging as\u0000minor changes in voter rankings can lead to significant ripple effects - for\u0000example, lending support to a losing candidate can prevent their votes from\u0000transferring to a more competitive opponent. We study optimal strategies -\u0000persuading voters to change their ballots or adding new voters - both\u0000algorithmically and theoretically. Algorithmically, we develop efficient\u0000methods to reduce election instances while maintaining optimization accuracy,\u0000effectively circumventing the computational complexity barrier. Theoretically,\u0000we analyze the effectiveness of strategies under both perfect and imperfect\u0000polling information. Our algorithmic approach applies to the ranked-choice\u0000polling data on the US 2024 Republican Primary, finding, for example, that\u0000several candidates would have been optimally served by boosting another\u0000candidate instead of themselves.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Truthful and Almost Envy-Free Mechanism of Allocating Indivisible Goods: the Power of Randomness 真实且几乎不受嫉妒影响的不可分割物品分配机制：随机性的力量

arXiv - CS - Computer Science and Game Theory Pub Date : 2024-07-18 DOI: arxiv-2407.13634

Xiaolin Bu, Biaoshuai Tao

{"title":"Truthful and Almost Envy-Free Mechanism of Allocating Indivisible Goods: the Power of Randomness","authors":"Xiaolin Bu, Biaoshuai Tao","doi":"arxiv-2407.13634","DOIUrl":"https://doi.org/arxiv-2407.13634","url":null,"abstract":"We study the problem of fairly and truthfully allocating $m$ indivisible\u0000items to $n$ agents with additive preferences. Specifically, we consider\u0000truthful mechanisms outputting allocations that satisfy EF$^{+u}_{-v}$, where,\u0000in an EF$^{+u}_{-v}$ allocation, for any pair of agents $i$ and $j$, agent $i$\u0000will not envy agent $j$ if $u$ items were added to $i$'s bundle and $v$ items\u0000were removed from $j$'s bundle. Previous work easily indicates that, when\u0000restricted to deterministic mechanisms, truthfulness will lead to a poor\u0000guarantee of fairness: even with two agents, for any $u$ and $v$,\u0000EF$^{+u}_{-v}$ cannot be guaranteed by truthful mechanisms when the number of\u0000items is large enough. In this work, we focus on randomized mechanisms, where\u0000we consider ex-ante truthfulness and ex-post fairness. For two agents, we\u0000present a truthful mechanism that achieves EF$^{+0}_{-1}$ (i.e., the\u0000well-studied fairness notion EF$1$). For three agents, we present a truthful\u0000mechanism that achieves EF$^{+1}_{-1}$. For $n$ agents in general, we show that\u0000there exist truthful mechanisms that achieve EF$^{+u}_{-v}$ for some $u$ and\u0000$v$ that depend only on $n$ (not $m$). We further consider fair and truthful mechanisms that also satisfy the\u0000standard efficiency guarantee: Pareto-optimality. We provide a mechanism that\u0000simultaneously achieves truthfulness, EF$1$, and Pareto-optimality for\u0000bi-valued utilities (where agents' valuation on each item is either $p$ or $q$\u0000for some $p>qgeq0$). For tri-valued utilities (where agents' valuations on\u0000each item belong to ${p,q,r}$ for some $p>q>rgeq0$) and any $u,v$, we show\u0000that truthfulness is incompatible with EF$^{+u}_{-v}$ and Pareto-optimality\u0000even for two agents.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0