Proceedings of the 24th ACM Conference on Economics and Computation最新文献

{"title":"Fair Multiwinner Elections with Allocation Constraints","authors":"Ivan-Aleksandar Mavrov, Kamesh Munagala, Yiheng Shen","doi":"10.1145/3580507.3597685","DOIUrl":"https://doi.org/10.1145/3580507.3597685","url":null,"abstract":"We consider the classical multiwinner election problem where the goal is to choose a subset of k unit-sized candidates (called committee) given utility functions of the voters. We allow arbitrary additional constraints on the chosen committee, and the utilities of voters to belong to a very general class of set functions called β-self bounding. When β = 1, this class includes XOS (and hence, submodular and additive) utilities as special cases. We define a novel generalization of core stability called restrained core to handle constraints on the committee, and consider multiplicative approximations on the utility under this notion. Our main result is the following: If a smooth version of Nash Welfare is globally optimized over committees that respect the constraints, then the resulting optimal committee lies in the eβ-approximate restrained core for β-self bounding utilities and arbitrary constraints. As a result we obtain the first constant approximation for stability with arbitrary additional constraints even for additive utilities (factor of e), as well as the first analysis of the stability of Nash Welfare with XOS functions even in the absence of constraints. We complement this positive result by showing that the c-approximate restrained core can be empty for c < 16/15 even for additive utilities and one additional constraint. Furthermore, the exponential dependence on β in the approximation is unavoidable for β-self bounding functions even in the absence of any constraints. We next present improved and tight approximation results for multiwinner elections with simpler classes of utility functions and simpler types of constraints. We also present an extension of restrained core to extended justified representation with constraints, and show an existence result for the special case of matroid constraints. We finally generalize our results to the setting when candidates have arbitrary sizes (Participatory Budgeting) and there are no additional constraints. Our proof techniques are different from previous analyses of Nash Welfare and are of independent interest.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129463912","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}

{"title":"Guaranteeing Envy-Freeness under Generalized Assignment Constraints","authors":"Siddharth Barman, Arindam Khan, Sudarshan Shyam, K. V. N. Sreenivas","doi":"10.1145/3580507.3597698","DOIUrl":"https://doi.org/10.1145/3580507.3597698","url":null,"abstract":"We study fair division of goods under the broad class of generalized assignment constraints. In this constraint framework, the sizes and values of the goods are agent-specific, and one needs to allocate the goods among the agents fairly while further ensuring that each agent receives a bundle of total size at most the corresponding budget of the agent. Since, in such a constraint setting, it may not always be feasible to partition all the goods among the agents, we conform---as in recent works---to the construct of charity to designate the set of unassigned goods. For this allocation framework, we obtain existential and computational guarantees for envy-free (appropriately defined) allocation of divisible and indivisible goods, respectively, among agents with individual, additive valuations for the goods. We deem allocations to be fair by evaluating envy only with respect to feasible subsets. In particular, an allocation is said to be feasibly envy-free (FEF) iff each agent prefers its bundle over every (budget) feasible subset within any other agent's bundle (and within the charity). The current work establishes that, for divisible goods, FEF allocations are guaranteed to exist and can be computed efficiently under generalized assignment constraints. Note that, in the presence of generalized assignment constraints, even the existence of such fair allocations of divisible goods is nonobvious, a priori. Our existential and computational guarantee for FEF allocations is built upon an incongruity property satisfied across a family of linear programs. This novel proof template is interesting in its own right. In the context of indivisible goods, FEF allocations do not necessarily exist, and hence, we consider the fairness notion of feasible envy-freeness up to any good (FEFx). Under this notion, an allocation of indivisible goods is declared to be fair iff for each pair of agents, a and b, envy-freeness holds for agent a against every feasible and strict subset of b's bundle; a similar guarantee is required with respect to the charity. We show that, under generalized assignment constraints, an FEFx allocation of indivisible goods always exists. In fact, our FEFx result resolves open problems posed in prior works, which provide existence guarantees under weaker fairness notions and more specialized constraints. Further, for indivisible goods and under generalized assignment constraints, we provide a pseudo-polynomial time algorithm for computing FEFx allocations, and a fully polynomial-time approximation scheme (FPTAS) for computing approximate FEFx allocations.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"252 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132744321","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}

{"title":"Robust Stackelberg Equilibria","authors":"Jiarui Gan, Minbiao Han, Jibang Wu, Haifeng Xu","doi":"10.1145/3580507.3597680","DOIUrl":"https://doi.org/10.1145/3580507.3597680","url":null,"abstract":"This paper provides a systematic study of the robust Stackelberg equilibrium (RSE), which naturally generalizes the widely adopted solution concept of the strong Stackelberg equilibrium (SSE). The RSE accounts for any possible up-to-δ suboptimal follower responses in Stackelberg games and is adopted to improve the robustness of the leader's strategy. While a few variants of robust Stackelberg equilibrium have been considered in previous literature, the RSE solution concept we consider is importantly different --- in some sense, it relaxes previously studied robust Stackelberg strategies and is applicable to much broader sources of uncertainties.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"239 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127536251","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}

{"title":"Should the Timing of Inspections be Predictable?","authors":"Ian Ball, Jan Knoepfle","doi":"10.1145/3580507.3597806","DOIUrl":"https://doi.org/10.1145/3580507.3597806","url":null,"abstract":"Inspections are frequently conducted to reveal information about agents' otherwise unobserved actions. Some inspections occur at pre-announced times; others are surprises. We show how the productive role of the inspected agent determines whether predictable or random inspections are optimal. If the agent's main task is achieving a breakthrough---think of an entrepreneur investing in an innovative industry---then predictable inspections are optimal. If the main task is avoiding a breakdown---think of a financial institution managing its risk in order to avoid default---then random inspections are optimal.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117157705","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}

{"title":"Pandora's Problem with Combinatorial Cost","authors":"Ben Berger, Tomer Ezra, M. Feldman, Federico Fusco","doi":"10.1145/3580507.3597699","DOIUrl":"https://doi.org/10.1145/3580507.3597699","url":null,"abstract":"Pandora's problem is a fundamental model in economics that studies optimal search strategies under costly inspection. In this paper we initiate the study of Pandora's problem with combinatorial costs, capturing many real-life scenarios where search cost is non-additive. Weitzman's celebrated algorithm [1979] establishes the remarkable result that, for additive costs, the optimal search strategy is non-adaptive and computationally feasible. We inquire to which extent this structural and computational simplicity extends beyond additive cost functions. Our main result is that the class of submodular cost functions admits an optimal strategy that follows a fixed, non-adaptive order, thus preserving the structural simplicity of additive cost functions. In contrast, for the more general class of subadditive (or even XOS) cost functions the optimal strategy may already need to determine the search order adaptively. On the computational side, obtaining any approximation to the optimal utility requires super polynomially many queries to the cost function, even for a strict subclass of submodular cost functions. The full version of the paper is available at https://arxiv.org/abs/2303.01078.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"2015 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114447411","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}

{"title":"On the Connection between Greedy Algorithms and Imperfect Rationality","authors":"Diodato Ferraioli, Carmine Ventre","doi":"10.1145/3580507.3597755","DOIUrl":"https://doi.org/10.1145/3580507.3597755","url":null,"abstract":"The design of algorithms or protocols that are able to align the goals of the planner with the selfish interests of the agents involved in these protocols is of paramount importance in almost every decentralized setting (such as, computer networks, markets, etc.) as shown by the rich literature in Mechanism Design. Recently, huge interest has been devoted to the design of mechanisms for imperfectly rational agents, i.e., mechanisms for which agents are able to easily grasp that there is no action different from following the protocol that would satisfy their interests better. This work has culminated in the definition of Obviously Strategyproof (OSP) Mechanisms, that have been shown to capture the incentives of agents without contingent reasoning skills. Without an understanding of the algorithmic nature of OSP mechanisms, it is hard to assess how well these mechanisms can satisfy the goals of the planner. For the case of binary allocation problems and agents whose private type is a single number, recent work has shown that a generalization of greedy completely characterizes OSP. In this work, we strengthen the connection between greedy and OSP by providing a characterization of OSP mechanisms for all optimization problems involving these single-parameter agents. Specifically, we prove that OSP mechanisms must essentially work as follows: they either greedily look for agents with \"better\" types and allocate them larger outcomes; or reverse greedily look for agents with \"worse\" types and allocate them smaller outcomes; or, finally, split the domain of agents in \"good\" and \"bad\" types, and subsequently proceed in a reverse greedy fashion for the former and greedily for the latter. We further demonstrate how to use this characterization to give bounds on the approximation guarantee of OSP mechanisms for the well known scheduling related machines problem.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116218251","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}

{"title":"Fundamental Bounds on Online Strategic Classification","authors":"Saba Ahmadi, Avrim Blum, Kunhe Yang","doi":"10.1145/3580507.3597818","DOIUrl":"https://doi.org/10.1145/3580507.3597818","url":null,"abstract":"We study the problem of online binary classification where strategic agents can manipulate their observable features in predefined ways, modeled by a manipulation graph, in order to receive a positive classification. We show this setting differs in fundamental ways from classic (non-strategic) online classification. For instance, whereas in the non-strategic case, a mistake bound of ln |H| is achievable via the halving algorithm when the target function belongs to a known class H, we show that no deterministic algorithm can achieve a mistake bound o(Δ) in the strategic setting, where Δ is the maximum degree of the manipulation graph (even when |H| = O(Δ)). We complement this with a general algorithm achieving mistake bound O(Δ ln |H|). We also extend this to the agnostic setting, and show that this algorithm achieves a Δ multiplicative regret (mistake bound of O(Δ · OPT + Δ · ln |H|)), and that no deterministic algorithm can achieve o(Δ) multiplicative regret. Next, we study two randomized models based on whether the random choices are made before or after agents respond, and show they exhibit fundamental differences. In the first, fractional model, at each round the learner deterministically chooses a probability distribution over classifiers inducing expected values on each vertex (probabilities of being classified as positive), which the strategic agents respond to. We show that any learner in this model has to suffer linear regret. On the other hand, in the second randomized algorithms model, while the adversary who selects the next agent must respond to the learner's probability distribution over classifiers, the agent then responds to the actual hypothesis classifier drawn from this distribution. Surprisingly, we show this model is more advantageous to the learner, and we design randomized algorithms that achieve sublinear regret bounds against both oblivious and adaptive adversaries.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117131059","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}

{"title":"Leveraging Reviews: Learning to Price with Buyer and Seller Uncertainty","authors":"Wenshuo Guo, Nika Haghtalab, Kirthevasan Kandasamy, Ellen Vitercik","doi":"10.48550/arXiv.2302.09700","DOIUrl":"https://doi.org/10.48550/arXiv.2302.09700","url":null,"abstract":"In online marketplaces, customers have access to hundreds of reviews for a single product. Buyers often use reviews from other customers that share their type---such as height for clothing, skin type for skincare products, and location for outdoor furniture---to estimate their values, which they may not know a priori. Customers with few relevant reviews may hesitate to make a purchase except at a low price, so for the seller, there is a tension between setting high prices and ensuring that there are enough reviews so that buyers can confidently estimate their values. Simultaneously, sellers may use reviews to gauge the demand for items they wish to sell.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131502373","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}

{"title":"Robust Pseudo-Markets for Reusable Public Resources","authors":"Siddhartha Banerjee, Giannis Fikioris, É. Tardos","doi":"10.1145/3580507.3597723","DOIUrl":"https://doi.org/10.1145/3580507.3597723","url":null,"abstract":"We study non-monetary mechanisms for the fair and efficient allocation of reusable public resources. We consider settings where a limited resource is repeatedly shared among a set of agents, each of whom may request to use the resource over multiple consecutive rounds, receiving some utility only if they get to use the resource for the full duration of their request. Such settings are of particular significance in scientific research where large-scale instruments such as electron microscopes, particle colliders, or telescopes are shared between multiple research groups; this model also subsumes and extends existing models of repeated non-monetary allocation where the resource is demanded only for a single round.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203531","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}

{"title":"Welfare Distribution in Two-sided Random Matching Markets","authors":"I. Ashlagi, M. Braverman, Geng Zhao","doi":"10.1145/3580507.3597730","DOIUrl":"https://doi.org/10.1145/3580507.3597730","url":null,"abstract":"We study the welfare structure in two-sided matching markets when agents have latent preferences generated according to observed characteristics. Specifically, we are interested in the empirical welfare distribution of agents on each side of the market under stable outcomes as well as the relation between the outcomes of each side of the market.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"140 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125169217","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}