Over the past decade, ride-sharing services have become increasingly important, with U.S. market leaders such as Uber and Lyft expanding to over 900 cities worldwide and facilitating billions of rides annually. This rise reflects their ability to meet users' convenience, efficiency, and affordability needs. However, in busy areas and surge zones, the benefits of these platforms can diminish, prompting riders to relocate to cheaper, more convenient locations or seek alternative transportation. While much research has focused on the strategic behavior of drivers, the strategic actions of riders, especially when it comes to riders walking outside of surge zones, remain under-explored. This paper examines the impact of rider-side strategic behavior on surge dynamics. We investigate how riders' actions influence market dynamics, including supply, demand, and pricing. We show significant impacts, such as spillover effects where demand increases in areas adjacent to surge zones and prices surge in nearby areas. Our theoretical insights and experimental results highlight that rider strategic behavior helps redistribute demand, reduce surge prices, and clear demand in a more balanced way across zones.
{"title":"On Rider Strategic Behavior in Ride-Sharing Platforms","authors":"Jay Mulay, Diptangshu Sen, Juba Ziani","doi":"arxiv-2408.04272","DOIUrl":"https://doi.org/arxiv-2408.04272","url":null,"abstract":"Over the past decade, ride-sharing services have become increasingly\u0000important, with U.S. market leaders such as Uber and Lyft expanding to over 900\u0000cities worldwide and facilitating billions of rides annually. This rise\u0000reflects their ability to meet users' convenience, efficiency, and\u0000affordability needs. However, in busy areas and surge zones, the benefits of\u0000these platforms can diminish, prompting riders to relocate to cheaper, more\u0000convenient locations or seek alternative transportation. While much research has focused on the strategic behavior of drivers, the\u0000strategic actions of riders, especially when it comes to riders walking outside\u0000of surge zones, remain under-explored. This paper examines the impact of\u0000rider-side strategic behavior on surge dynamics. We investigate how riders'\u0000actions influence market dynamics, including supply, demand, and pricing. We\u0000show significant impacts, such as spillover effects where demand increases in\u0000areas adjacent to surge zones and prices surge in nearby areas. Our theoretical\u0000insights and experimental results highlight that rider strategic behavior helps\u0000redistribute demand, reduce surge prices, and clear demand in a more balanced\u0000way across zones.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934978","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}
Recently there has been a large amount of research designing mechanisms for auction scenarios where the bidders are connected in a social network. Different from the existing studies in this field that focus on specific auction scenarios e.g. single-unit auction and multi-unit auction, this paper considers the following question: is it possible to design a scheme that, given a classical auction scenario and a mechanism $tilde{mathcal{M}}$ suited for it, produces a mechanism in the network setting that preserves the key properties of $tilde{mathcal{M}}$? To answer this question, we design meta-mechanisms that provide a uniform way of transforming mechanisms from classical models to mechanisms over networks and prove that the desirable properties are preserved by our meta-mechanisms. Our meta-mechanisms provide solutions to combinatorial auction scenarios in the network setting: (1) combinatorial auction with single-minded buyers and (2) combinatorial auction with general monotone valuation. To the best of our knowledge, this is the first work that designs combinatorial auctions over a social network.
{"title":"Meta-mechanisms for Combinatorial Auctions over Social Networks","authors":"Yuan Fang, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov","doi":"arxiv-2408.04555","DOIUrl":"https://doi.org/arxiv-2408.04555","url":null,"abstract":"Recently there has been a large amount of research designing mechanisms for\u0000auction scenarios where the bidders are connected in a social network.\u0000Different from the existing studies in this field that focus on specific\u0000auction scenarios e.g. single-unit auction and multi-unit auction, this paper\u0000considers the following question: is it possible to design a scheme that, given\u0000a classical auction scenario and a mechanism $tilde{mathcal{M}}$ suited for\u0000it, produces a mechanism in the network setting that preserves the key\u0000properties of $tilde{mathcal{M}}$? To answer this question, we design\u0000meta-mechanisms that provide a uniform way of transforming mechanisms from\u0000classical models to mechanisms over networks and prove that the desirable\u0000properties are preserved by our meta-mechanisms. Our meta-mechanisms provide\u0000solutions to combinatorial auction scenarios in the network setting: (1)\u0000combinatorial auction with single-minded buyers and (2) combinatorial auction\u0000with general monotone valuation. To the best of our knowledge, this is the\u0000first work that designs combinatorial auctions over a social network.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"77 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934974","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}
The issue of fairness in AI arises from discriminatory practices in applications like job recommendations and risk assessments, emphasising the need for algorithms that do not discriminate based on group characteristics. This concern is also pertinent to auctions, commonly used for resource allocation, which necessitate fairness considerations. Our study examines auctions with groups distinguished by specific attributes, seeking to (1) define a fairness notion that ensures equitable treatment for all, (2) identify mechanisms that adhere to this fairness while preserving incentive compatibility, and (3) explore the balance between fairness and seller's revenue. We introduce two fairness notions-group fairness and individual fairness-and propose two corresponding auction mechanisms: the Group Probability Mechanism, which meets group fairness and incentive criteria, and the Group Score Mechanism, which also encompasses individual fairness. Through experiments, we validate these mechanisms' effectiveness in promoting fairness and examine their implications for seller revenue.
{"title":"Balancing Efficiency with Equality: Auction Design with Group Fairness Concerns","authors":"Fengjuan Jia, Mengxiao Zhang, Jiamou Liu, Bakh Khoussainov","doi":"arxiv-2408.04545","DOIUrl":"https://doi.org/arxiv-2408.04545","url":null,"abstract":"The issue of fairness in AI arises from discriminatory practices in\u0000applications like job recommendations and risk assessments, emphasising the\u0000need for algorithms that do not discriminate based on group characteristics.\u0000This concern is also pertinent to auctions, commonly used for resource\u0000allocation, which necessitate fairness considerations. Our study examines\u0000auctions with groups distinguished by specific attributes, seeking to (1)\u0000define a fairness notion that ensures equitable treatment for all, (2) identify\u0000mechanisms that adhere to this fairness while preserving incentive\u0000compatibility, and (3) explore the balance between fairness and seller's\u0000revenue. We introduce two fairness notions-group fairness and individual\u0000fairness-and propose two corresponding auction mechanisms: the Group\u0000Probability Mechanism, which meets group fairness and incentive criteria, and\u0000the Group Score Mechanism, which also encompasses individual fairness. Through\u0000experiments, we validate these mechanisms' effectiveness in promoting fairness\u0000and examine their implications for seller revenue.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934975","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}
Games with incomplete preferences are an important model for studying rational decision-making in scenarios where players face incomplete information about their preferences and must contend with incomparable outcomes. We study the problem of computing Nash equilibrium in a subclass of two-player games played on graphs where each player seeks to maximally satisfy their (possibly incomplete) preferences over a set of temporal goals. We characterize the Nash equilibrium and prove its existence in scenarios where player preferences are fully aligned, partially aligned, and completely opposite, in terms of the well-known solution concepts of sure winning and Pareto efficiency. When preferences are partially aligned, we derive conditions under which a player needs cooperation and demonstrate that the Nash equilibria depend not only on the preference alignment but also on whether the players need cooperation to achieve a better outcome and whether they are willing to cooperate.We illustrate the theoretical results by solving a mechanism design problem for a drone delivery scenario.
{"title":"Nash Equilibrium in Games on Graphs with Incomplete Preferences","authors":"Abhishek N. Kulkarni, Jie Fu, Ufuk Topcu","doi":"arxiv-2408.02860","DOIUrl":"https://doi.org/arxiv-2408.02860","url":null,"abstract":"Games with incomplete preferences are an important model for studying\u0000rational decision-making in scenarios where players face incomplete information\u0000about their preferences and must contend with incomparable outcomes. We study\u0000the problem of computing Nash equilibrium in a subclass of two-player games\u0000played on graphs where each player seeks to maximally satisfy their (possibly\u0000incomplete) preferences over a set of temporal goals. We characterize the Nash\u0000equilibrium and prove its existence in scenarios where player preferences are\u0000fully aligned, partially aligned, and completely opposite, in terms of the\u0000well-known solution concepts of sure winning and Pareto efficiency. When\u0000preferences are partially aligned, we derive conditions under which a player\u0000needs cooperation and demonstrate that the Nash equilibria depend not only on\u0000the preference alignment but also on whether the players need cooperation to\u0000achieve a better outcome and whether they are willing to cooperate.We\u0000illustrate the theoretical results by solving a mechanism design problem for a\u0000drone delivery scenario.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934976","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}
Selecting $k$ out of $m$ items based on the preferences of $n$ heterogeneous agents is a widely studied problem in algorithmic game theory. If agents have approval preferences over individual items and harmonic utility functions over bundles -- an agent receives $sum_{j=1}^tfrac{1}{j}$ utility if $t$ of her approved items are selected -- then welfare optimisation is captured by a voting rule known as Proportional Approval Voting (PAV). PAV also satisfies demanding fairness axioms. However, finding a winning set of items under PAV is NP-hard. In search of a tractable method with strong fairness guarantees, a bounded local search version of PAV was proposed by Aziz et al. It proceeds by starting with an arbitrary size-$k$ set $W$ and, at each step, checking if there is a pair of candidates $ain W$, $bnotin W$ such that swapping $a$ and $b$ increases the total welfare by at least $varepsilon$; if yes, it performs the swap. Aziz et al.~show that setting $varepsilon=frac{n}{k^2}$ ensures both the desired fairness guarantees and polynomial running time. However, they leave it open whether the algorithm converges in polynomial time if $varepsilon$ is very small (in particular, if we do not stop until there are no welfare-improving swaps). We resolve this open question, by showing that if $varepsilon$ can be arbitrarily small, the running time of this algorithm may be super-polynomial. Specifically, we prove a lower bound of~$Omega(k^{log k})$ if improvements are chosen lexicographically. To complement our lower bound, we provide an empirical comparison of two variants of local search -- better-response and best-response -- on several real-life data sets and a variety of synthetic data sets. Our experiments indicate that, empirically, better response exhibits faster running time than best response.
{"title":"A Lower Bound for Local Search Proportional Approval Voting","authors":"Sonja Kraiczy, Edith Elkind","doi":"arxiv-2408.02300","DOIUrl":"https://doi.org/arxiv-2408.02300","url":null,"abstract":"Selecting $k$ out of $m$ items based on the preferences of $n$ heterogeneous\u0000agents is a widely studied problem in algorithmic game theory. If agents have\u0000approval preferences over individual items and harmonic utility functions over\u0000bundles -- an agent receives $sum_{j=1}^tfrac{1}{j}$ utility if $t$ of her\u0000approved items are selected -- then welfare optimisation is captured by a\u0000voting rule known as Proportional Approval Voting (PAV). PAV also satisfies\u0000demanding fairness axioms. However, finding a winning set of items under PAV is\u0000NP-hard. In search of a tractable method with strong fairness guarantees, a\u0000bounded local search version of PAV was proposed by Aziz et al. It proceeds by\u0000starting with an arbitrary size-$k$ set $W$ and, at each step, checking if\u0000there is a pair of candidates $ain W$, $bnotin W$ such that swapping $a$ and\u0000$b$ increases the total welfare by at least $varepsilon$; if yes, it performs\u0000the swap. Aziz et al.~show that setting $varepsilon=frac{n}{k^2}$ ensures\u0000both the desired fairness guarantees and polynomial running time. However, they\u0000leave it open whether the algorithm converges in polynomial time if\u0000$varepsilon$ is very small (in particular, if we do not stop until there are\u0000no welfare-improving swaps). We resolve this open question, by showing that if\u0000$varepsilon$ can be arbitrarily small, the running time of this algorithm may\u0000be super-polynomial. Specifically, we prove a lower bound of~$Omega(k^{log\u0000k})$ if improvements are chosen lexicographically. To complement our lower\u0000bound, we provide an empirical comparison of two variants of local search --\u0000better-response and best-response -- on several real-life data sets and a\u0000variety of synthetic data sets. Our experiments indicate that, empirically,\u0000better response exhibits faster running time than best response.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934977","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}
Chen Qiu, Haobo Fu, Kai Li, Weixin Huang, Jiajia Zhang, Xuan Wang
In ex ante coordinated adversarial team games (ATGs), a team competes against an adversary, and the team members are only allowed to coordinate their strategies before the game starts. The team-maxmin equilibrium with correlation (TMECor) is a suitable solution concept for ATGs. One class of TMECor-solving methods transforms the problem into solving NE in two-player zero-sum games, leveraging well-established tools for the latter. However, existing methods are fundamentally action-based, resulting in poor generalizability and low solving efficiency due to the exponential growth in the size of the transformed game. To address the above issues, we propose an efficient game transformation method based on private information, where all team members are represented by a single coordinator. We designed a structure called private information pre-branch, which makes decisions considering all possible private information from teammates. We prove that the size of the game transformed by our method is exponentially reduced compared to the current state-of-the-art. Moreover, we demonstrate equilibria equivalence. Experimentally, our method achieves a significant speedup of 182.89$times$ to 694.44$times$ in scenarios where the current state-of-the-art method can work, such as small-scale Kuhn poker and Leduc poker. Furthermore, our method is applicable to larger games and those with dynamically changing private information, such as Goofspiel.
{"title":"Enhanced Equilibria-Solving via Private Information Pre-Branch Structure in Adversarial Team Games","authors":"Chen Qiu, Haobo Fu, Kai Li, Weixin Huang, Jiajia Zhang, Xuan Wang","doi":"arxiv-2408.02283","DOIUrl":"https://doi.org/arxiv-2408.02283","url":null,"abstract":"In ex ante coordinated adversarial team games (ATGs), a team competes against\u0000an adversary, and the team members are only allowed to coordinate their\u0000strategies before the game starts. The team-maxmin equilibrium with correlation\u0000(TMECor) is a suitable solution concept for ATGs. One class of TMECor-solving\u0000methods transforms the problem into solving NE in two-player zero-sum games,\u0000leveraging well-established tools for the latter. However, existing methods are\u0000fundamentally action-based, resulting in poor generalizability and low solving\u0000efficiency due to the exponential growth in the size of the transformed game.\u0000To address the above issues, we propose an efficient game transformation method\u0000based on private information, where all team members are represented by a\u0000single coordinator. We designed a structure called private information\u0000pre-branch, which makes decisions considering all possible private information\u0000from teammates. We prove that the size of the game transformed by our method is\u0000exponentially reduced compared to the current state-of-the-art. Moreover, we\u0000demonstrate equilibria equivalence. Experimentally, our method achieves a\u0000significant speedup of 182.89$times$ to 694.44$times$ in scenarios where the\u0000current state-of-the-art method can work, such as small-scale Kuhn poker and\u0000Leduc poker. Furthermore, our method is applicable to larger games and those\u0000with dynamically changing private information, such as Goofspiel.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934937","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}
We study the problem of mechanism design for allocating a set of indivisible items among agents with private preferences on items. We are interested in such a mechanism that is strategyproof (where agents' best strategy is to report their true preferences) and is expected to ensure fairness and efficiency to a certain degree. We first present an impossibility result that a deterministic mechanism does not exist that is strategyproof, fair and efficient for allocating indivisible chores. We then utilize randomness to overcome the strong impossibility. For allocating indivisible chores, we propose a randomized mechanism that is strategyproof in expectation as well as ex-ante and ex-post (best of both worlds) fair and efficient. For allocating mixed items, where an item can be a good (i.e., with a positive utility) for one agent but a chore (i.e., a with negative utility) for another, we propose a randomized mechanism that is strategyproof in expectation with best of both worlds fairness and efficiency when there are two agents.
{"title":"Randomized Strategyproof Mechanisms with Best of Both Worlds Fairness and Efficiency","authors":"Ankang Sun, Bo Chen","doi":"arxiv-2408.01027","DOIUrl":"https://doi.org/arxiv-2408.01027","url":null,"abstract":"We study the problem of mechanism design for allocating a set of indivisible\u0000items among agents with private preferences on items. We are interested in such\u0000a mechanism that is strategyproof (where agents' best strategy is to report\u0000their true preferences) and is expected to ensure fairness and efficiency to a\u0000certain degree. We first present an impossibility result that a deterministic\u0000mechanism does not exist that is strategyproof, fair and efficient for\u0000allocating indivisible chores. We then utilize randomness to overcome the\u0000strong impossibility. For allocating indivisible chores, we propose a\u0000randomized mechanism that is strategyproof in expectation as well as ex-ante\u0000and ex-post (best of both worlds) fair and efficient. For allocating mixed\u0000items, where an item can be a good (i.e., with a positive utility) for one\u0000agent but a chore (i.e., a with negative utility) for another, we propose a\u0000randomized mechanism that is strategyproof in expectation with best of both\u0000worlds fairness and efficiency when there are two agents.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934938","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}
Examining the mechanisms underlying the formation and evolution of opinions within real-world social systems, which consist of numerous individuals, can provide valuable insights for effective social functioning and informed business decision making. The focus of our study is on the dynamics of opinions inside a networked multi-agent system. We provide a novel approach called the Game Theory Based Community-Aware Opinion Formation Process (GCAOFP) to accurately represent the co-evolutionary dynamics of communities and opinions in real-world social systems. The GCAOFP algorithm comprises two distinct steps in each iteration. 1) The Community Dynamics Process conceptualizes the process of community formation as a non-cooperative game involving a finite number of agents. Each individual agent aims to maximize their own utility by adopting a response that leads to the most favorable update of the community label. 2) The Opinion Formation Process involves the updating of an individual agent's opinion within a community-aware framework that incorporates bounded confidence. This process takes into account the updated matrix of community members and ensures that an agent's opinion aligns with the opinions of others within their community, within certain defined limits. The present study provides a theoretical proof that under any initial conditions, the aforementioned co-evolutionary dynamics process will ultimately reach an equilibrium state. In this state, both the opinion vector and community member matrix will stabilize after a finite number of iterations. In contrast to conventional opinion dynamics models, the guaranteed convergence of agent opinion within the same community ensures that the convergence of opinions takes place exclusively inside a given community.
{"title":"Game Theory Based Community-Aware Opinion Dynamics","authors":"Shanfan Zhang, Xiaoting Shen, Zhan Bu","doi":"arxiv-2408.01196","DOIUrl":"https://doi.org/arxiv-2408.01196","url":null,"abstract":"Examining the mechanisms underlying the formation and evolution of opinions\u0000within real-world social systems, which consist of numerous individuals, can\u0000provide valuable insights for effective social functioning and informed\u0000business decision making. The focus of our study is on the dynamics of opinions\u0000inside a networked multi-agent system. We provide a novel approach called the\u0000Game Theory Based Community-Aware Opinion Formation Process (GCAOFP) to\u0000accurately represent the co-evolutionary dynamics of communities and opinions\u0000in real-world social systems. The GCAOFP algorithm comprises two distinct steps\u0000in each iteration. 1) The Community Dynamics Process conceptualizes the process\u0000of community formation as a non-cooperative game involving a finite number of\u0000agents. Each individual agent aims to maximize their own utility by adopting a\u0000response that leads to the most favorable update of the community label. 2) The\u0000Opinion Formation Process involves the updating of an individual agent's\u0000opinion within a community-aware framework that incorporates bounded\u0000confidence. This process takes into account the updated matrix of community\u0000members and ensures that an agent's opinion aligns with the opinions of others\u0000within their community, within certain defined limits. The present study\u0000provides a theoretical proof that under any initial conditions, the\u0000aforementioned co-evolutionary dynamics process will ultimately reach an\u0000equilibrium state. In this state, both the opinion vector and community member\u0000matrix will stabilize after a finite number of iterations. In contrast to\u0000conventional opinion dynamics models, the guaranteed convergence of agent\u0000opinion within the same community ensures that the convergence of opinions\u0000takes place exclusively inside a given community.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"29 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141968999","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}
In this article, we study the optimal design of High Occupancy Toll (HOT) lanes. The traffic authority determines the road capacity allocation between HOT lanes and ordinary lanes, as well as the toll price charged for travelers using HOT lanes who do not meet the high-occupancy eligibility criteria. We develop a game-theoretic model to analyze the decisions of travelers with heterogeneous preference parameters in values of time and carpool disutilities. These travelers choose between paying or forming carpools to use the HOT lanes, or taking the ordinary lanes. Travelers' welfare depends on the congestion cost of the lane they use, the toll payment, and the carpool disutilities. For highways with a single entrance and exit node, we provide a complete characterization of equilibrium strategies and a comparative statics analysis of how the equilibrium vehicle flow and travel time change with HOT capacity and toll price. We then extend the single segment model to highways with multiple entrance and exit nodes. We extend the equilibrium concept and propose various design objectives considering traffic congestion, toll revenue, and social welfare. Using the data collected from the HOT lane of the California Interstate Highway 880 (I-880), we formulate a convex program to estimate the travel demand and approximate the distribution of travelers' preference parameters. We then compute the optimal toll design of five segments for I-880 for achieve each one of the four objectives, and compare the optimal solution with the current toll pricing.
本文研究了高占用率收费（HOT）车道的优化设计。交通管理部门决定 HOT 车道和普通车道之间的道路容量分配，以及对使用 HOT 车道但不符合高占有率资格标准的旅客收取的通行费价格。我们建立了一个博弈论模型，分析在时间和拼车效用值方面具有异质偏好参数的旅行者的决策。这些旅行者会选择付费或拼车使用 HOT 车道，还是使用普通车道。旅行者的福利取决于他们所使用车道的拥堵成本、通行费支付和拼车效用。对于只有一个入口和出口节点的高速公路，我们对均衡策略进行了完整描述，并对均衡车辆流量和旅行时间如何随 HOT 容量和收费价格变化进行了比较静态分析。然后，我们将单一路段模型扩展到具有多个出入口节点的高速公路。我们扩展了平衡概念，并提出了考虑交通拥堵、通行费收入和社会福利的各种设计目标。利用从加利福尼亚州 880 号州际公路（I-880）的 HOT 车道收集到的数据，我们制定了一个凸程序来估计旅行需求并近似计算旅行者偏好参数的分布。然后，我们计算了 I-880 五个路段的最优收费设计，以实现四个目标中的每个目标，并将最优方案与当前的收费定价进行比较。
{"title":"A Game Theoretic Analysis of High Occupancy Toll Lane Design","authors":"Zhanhao Zhang, Ruifan Yang, Manxi Wu","doi":"arxiv-2408.01413","DOIUrl":"https://doi.org/arxiv-2408.01413","url":null,"abstract":"In this article, we study the optimal design of High Occupancy Toll (HOT)\u0000lanes. The traffic authority determines the road capacity allocation between\u0000HOT lanes and ordinary lanes, as well as the toll price charged for travelers\u0000using HOT lanes who do not meet the high-occupancy eligibility criteria. We\u0000develop a game-theoretic model to analyze the decisions of travelers with\u0000heterogeneous preference parameters in values of time and carpool disutilities.\u0000These travelers choose between paying or forming carpools to use the HOT lanes,\u0000or taking the ordinary lanes. Travelers' welfare depends on the congestion cost\u0000of the lane they use, the toll payment, and the carpool disutilities. For\u0000highways with a single entrance and exit node, we provide a complete\u0000characterization of equilibrium strategies and a comparative statics analysis\u0000of how the equilibrium vehicle flow and travel time change with HOT capacity\u0000and toll price. We then extend the single segment model to highways with\u0000multiple entrance and exit nodes. We extend the equilibrium concept and propose\u0000various design objectives considering traffic congestion, toll revenue, and\u0000social welfare. Using the data collected from the HOT lane of the California\u0000Interstate Highway 880 (I-880), we formulate a convex program to estimate the\u0000travel demand and approximate the distribution of travelers' preference\u0000parameters. We then compute the optimal toll design of five segments for I-880\u0000for achieve each one of the four objectives, and compare the optimal solution\u0000with the current toll pricing.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934944","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}
Machine learning has witnessed remarkable breakthroughs in recent years. As machine learning permeates various aspects of daily life, individuals and organizations increasingly interact with these systems, exhibiting a wide range of social and adversarial behaviors. These behaviors may have a notable impact on the behavior and performance of machine learning systems. Specifically, during these interactions, data may be generated by strategic individuals, collected by self-interested data collectors, possibly poisoned by adversarial attackers, and used to create predictors, models, and policies satisfying multiple objectives. As a result, the machine learning systems' outputs might degrade, such as the susceptibility of deep neural networks to adversarial examples (Shafahi et al., 2018; Szegedy et al., 2013) and the diminished performance of classic algorithms in the presence of strategic individuals (Ahmadi et al., 2021). Addressing these challenges is imperative for the success of machine learning in societal settings.
{"title":"Trustworthy Machine Learning under Social and Adversarial Data Sources","authors":"Han Shao","doi":"arxiv-2408.01596","DOIUrl":"https://doi.org/arxiv-2408.01596","url":null,"abstract":"Machine learning has witnessed remarkable breakthroughs in recent years. As\u0000machine learning permeates various aspects of daily life, individuals and\u0000organizations increasingly interact with these systems, exhibiting a wide range\u0000of social and adversarial behaviors. These behaviors may have a notable impact\u0000on the behavior and performance of machine learning systems. Specifically,\u0000during these interactions, data may be generated by strategic individuals,\u0000collected by self-interested data collectors, possibly poisoned by adversarial\u0000attackers, and used to create predictors, models, and policies satisfying\u0000multiple objectives. As a result, the machine learning systems' outputs might\u0000degrade, such as the susceptibility of deep neural networks to adversarial\u0000examples (Shafahi et al., 2018; Szegedy et al., 2013) and the diminished\u0000performance of classic algorithms in the presence of strategic individuals\u0000(Ahmadi et al., 2021). Addressing these challenges is imperative for the\u0000success of machine learning in societal settings.","PeriodicalId":501316,"journal":{"name":"arXiv - CS - Computer Science and Game Theory","volume":"57 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141934846","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}
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