Proceedings of the 23rd ACM Conference on Economics and Computation最新文献

{"title":"Public Signals in Network Congestion Games","authors":"Svenja M. Griesbach, M. Hoefer, Max Klimm, Tim Koglin","doi":"10.1145/3490486.3538349","DOIUrl":"https://doi.org/10.1145/3490486.3538349","url":null,"abstract":"It is a well-known fact that selfish behavior degrades the performance of traffic networks. Various measures have been proposed in the literature as a remedy for the inefficiency of traffic equilibria (such as road tolls or network design techniques). However, it often seems impractical and/or politically undesirable that these measures get implemented to a substantial extent. We consider a largely untapped potential of network improvement rooted in the inherent uncertainty of travel times. Travel times are subject to stochastic uncertainty resulting from various parameters such as weather condition, occurrences of road works, or traffic accidents. Large mobility services have an informational advantage over single network users as they are able to learn traffic conditions from data. A benevolent mobility service may use this informational advantage in order to steer the traffic equilibrium into a favorable direction. The resulting optimization problem is a task commonly referred to as signaling or Bayesian persuasion. Previous work has shown that the underlying signaling problem can be NP-hard to approximate within any non-trivial bounds[1], even for affine cost functions with stochastic offsets. In contrast, we show that in this case, the signaling problem is easy for many networks. We tightly characterize the class of single-commodity networks, in which full information revelation is always an optimal signaling strategy. Moreover, we construct a reduction from optimal signaling to computing an optimal collection of support vectors for the Wardrop equilibrium. For two states, this insight can be used to compute an optimal signaling scheme. The algorithm runs in polynomial time whenever the number of different supports resulting from any signal distribution is bounded to a polynomial in the input size. Using a cell decomposition technique, we extend the approach to a polynomial-time algorithm for multi-commodity parallel link networks with a constant number of commodities, even when we have a constant number of different states of nature.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115612926","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":"Impartial Selection with Additive Guarantees via Iterated Deletion","authors":"Javier Cembrano, Felix A. Fischer, David Hannon, Max Klimm","doi":"10.1145/3490486.3538294","DOIUrl":"https://doi.org/10.1145/3490486.3538294","url":null,"abstract":"Impartial selection is the selection of an individual from a group based on nominations by other members of the group, in such a way that individuals cannot influence their own chance of selection. We give a deterministic mechanism with an additive performance guarantee of O(n(1+κ)/2) in a setting with n individuals where each individual casts O(nκ) nominations, where κ∈[0,1]. For κ=0, i.e. when each individual casts at most a constant number of nominations, this bound is O(√n). This matches the best-known guarantee for randomized mechanisms and a single nomination. For κ=1 the bound is O(n). This is trivial, as even a mechanism that never selects provides an additive guarantee of n-1. We show, however, that it is also best possible: for every deterministic impartial mechanism there exists a situation in which some individual is nominated by every other individual and the mechanism either does not select or selects an individual not nominated by anyone.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123794187","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":"Improved Online Contention Resolution for Matchings and Applications to the Gig Economy","authors":"Tristan Pollner, M. Roghani, A. Saberi, David Wajc","doi":"10.1145/3490486.3538295","DOIUrl":"https://doi.org/10.1145/3490486.3538295","url":null,"abstract":"Motivated by applications in the gig economy, we study approximation algorithms for a sequential pricing problem. The input is a bipartite graph [Formula: see text] between individuals I and jobs J. The platform has a value of vj for matching job j to an individual worker. In order to find a matching, the platform can consider the edges [Formula: see text] in any order and make a one-time take-it-or-leave-it offer of a price [Formula: see text] of its choosing to i for completing j. The worker accepts the offer with a known probability pijw; in this case, the job and the worker are irrevocably matched. What is the best way to make offers to maximize revenue and/or social welfare? The optimal algorithm is known to be NP-hard to compute (even if there is only a single job). With this in mind, we design efficient approximations to the optimal policy via a new random-order online contention resolution scheme (RO-OCRS) for matching. Our main result is a 0.456-balanced RO-OCRS in bipartite graphs and a 0.45-balanced RO-OCRS in general graphs. These algorithms improve on the recent bound of [Formula: see text] and improve on the best-known lower bounds for the correlation gap of matching, despite applying to a significantly more restrictive setting. As a consequence of our online contention resolution scheme results, we obtain a 0.456-approximate algorithm for the sequential pricing problem. We further extend our results to settings where workers can only be contacted a limited number of times and show how to achieve improved results for this problem via improved algorithms for the well-studied stochastic probing problem. Funding: This work was supported by the National Science Foundation [Grant CCF2209520] and a gift from Amazon Research.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134584025","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":"Distributional Robustness: From Pricing to Auctions","authors":"Nir Bachrach, Inbal Talgam-Cohen","doi":"10.1145/3490486.3538273","DOIUrl":"https://doi.org/10.1145/3490486.3538273","url":null,"abstract":"We study robust mechanism design for revenue maximization when selling a single item in an auction, assuming that only the mean of the value distribution and an upper bound on the bidders' valuations for the item are known. Robust mechanism design is a rising alternative to Bayesian mechanism design, which yields designs that do not rely on assumptions like full distributional knowledge, but rather only partial knowledge of the distributions. We seek a mechanism that maximizes revenue over the worst-case distribution compatible with the known parameters. Such a mechanism arises as an equilibrium of a zero-sum game between the seller and an adversary who chooses the distribution, and so can be referred to as the max-min mechanism. Carrasco et al. [2018] derive the max-min pricing when the seller faces a single bidder for the item. We go from max-min pricing to max-min auctions by studying the canonical setting of two i.i.d. bidders, and show the max-min mechanism is the second-price auction with a randomized reserve. We derive a closed-form solution for the distribution over reserve prices, as well as the worst-case value distribution, for which there is simple economic intuition. We also derive a closed-form solution for the max-min reserve price distribution for any number of bidders, and we show that unlike the case of two bidders, a second-price auction with a randomized reserve cannot be an equilibrium for more than two bidders. Our technique for solving the zero-sum game is quite different than that of Carrasco et al. -- we focus on a reduced zero-sum game, where the seller can only choose a distribution for a second-price auction with a randomized reserve price (rather than any mechanism). We then analyze a discretized version of the setting to find conditions an equilibrium would satisfy. By refining the discretization grid, we are able to achieve differential equations, and solving them yields closed-form non-discretized distributions. The resulting distributions for the seller and the adversary are later shown to be an equilibrium for the reduced zero-sum game. For the two-bidder case, we expand our result to an equilibrium of the original zero-sum game, where the seller is not limited to second price auctions with reserve. The full version of the paper is available at https://arxiv.org/abs/2205.09008.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130662178","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":"Price Interpretability of Prediction Markets: A Convergence Analysis","authors":"Dian Yu, Jianjun Gao, Weiping Wu, Zizhuo Wang","doi":"10.1145/3490486.3538347","DOIUrl":"https://doi.org/10.1145/3490486.3538347","url":null,"abstract":"Prediction markets are long known for prediction accuracy. However, there is still a lack of systematic understanding of how prediction markets aggregate information and why they work so well. This work proposes a multivariate utility (MU)-based mechanism that unifies several existing prediction market-making schemes. Based on this mechanism, we derive convergence results for markets with myopic, risk-averse traders who repeatedly interact with the market maker. We show that the resulting limiting wealth distribution lies on the Pareto efficient frontier defined by all market participants' utilities. With the help of this result, we establish both analytical and numerical results for the limiting price for different market models. We show that the limiting price converges to the geometric mean of agents' beliefs for exponential utility-based markets. For risk measure-based markets, we construct a risk measure family that meets the convergence requirements and show that the limiting price can converge to a weighted power mean of agent beliefs. For markets based on hyperbolic absolute risk aversion (HARA) utilities, we show that the limiting price is also a risk-adjusted weighted power mean of agent beliefs, even though the trading order will affect the aggregation weights. We further propose an approximation scheme for the limiting price under the HARA utility family. We show through numerical experiments that our approximation scheme works well in predicting the convergent prices.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115970647","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":"Fair Shares: Feasibility, Domination and Incentives","authors":"Moshe Babaioff, U. Feige","doi":"10.48550/arXiv.2205.07519","DOIUrl":"https://doi.org/10.48550/arXiv.2205.07519","url":null,"abstract":"We consider fair allocation of a set M of indivisible goods to n equally-entitled agents, with no monetary transfers. Every agent i has a valuation function vi from some given class of valuation functions. A share s is a function that maps a pair (vi,n) to a non-negative value, with the interpretation that if an allocation of M to n agents fails to give agent i a bundle of value at least equal to s(vi,n), this serves as evidence that the allocation is not fair towards i. For such an interpretation to make sense, we would like the share to be feasible, meaning that for any valuations in the class, there is an allocation that gives every agent at least her share. The maximin share (MMS) was a natural candidate for a feasible share for additive valuations. However, Kurokawa, Procaccia and Wang [2018] show that it is not feasible. We initiate a systematic study of the family of feasible shares. We say that a share is self maximizing if truth-telling maximizes the implied guarantee (the worse true value of any bundle that gives the share with respect to the report). We show that every feasible share is dominated by some self-maximizing and feasible share. We seek to identify those self-maximizing feasible shares that are polynomial time computable, and offer the highest share values. We show that a SM-dominating feasible share -- one that dominates every self-maximizing (SM) feasible share -- does not exist for additive valuations (and beyond). Consequently, we relax the domination property to that of domination up to a multiplicative factor of ρ (called ρ-dominating ). For additive valuations we present shares that are feasible, self-maximizing and polynomial-time computable. For n agents we present such a share that is 2n/3n-1-dominating, and is 4/5-dominating when n ≤ 4. For two agents we present such a share that is (1 - ε)-dominating. Moreover, for each of these shares we present a polynomial time algorithm that computes allocations that give every agent at least her share.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116768160","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":"Efficient Algorithms for Planning with Participation Constraints","authors":"Hanrui Zhang, Yu Cheng","doi":"10.1145/3490486.3538280","DOIUrl":"https://doi.org/10.1145/3490486.3538280","url":null,"abstract":"We consider the problem of planning with participation constraints introduced in[24]. In this problem, a principal chooses actions in a Markov decision process, resulting in separate utilities for the principal and the agent. However, the agent can and will choose to end the process whenever his expected onward utility becomes negative. The principal seeks to compute and commit to a policy that maximizes her expected utility, under the constraint that the agent should always want to continue participating. We provide the first polynomial-time exact algorithm for this problem for finite-horizon settings, where previously only an additive ε-approximation algorithm was known. Our approach can also be extended to the (discounted) infinite-horizon case, for which we give an algorithm that runs in time polynomial in the size of the input and log(1/ε), and returns a policy that is optimal up to an additive error of ε.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116962423","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":"Core-Stability in Assignment Markets with Financially Constrained Buyers","authors":"Eleni Batziou, M. Bichler, Maximilian Fichtl","doi":"10.1145/3490486.3538262","DOIUrl":"https://doi.org/10.1145/3490486.3538262","url":null,"abstract":"We study markets where a set of indivisible items is sold to bidders with unit-demand valuations, subject to a hard budget limit. Without financial constraints and pure quasilinear bidders, this assignment model allows for a simple ascending auction format that maximizes welfare and is incentive-compatible and core-stable. Introducing budget constraints, the ascending auction requires strong additional conditions on the unit-demand preferences to maintain its properties. We show that, without these conditions, we cannot hope for an incentive-compatible and core-stable mechanism. We design an iterative algorithm that depends solely on a trivially verifiable ex-post condition and demand queries, and with appropriate decisions made by an auctioneer, always yields a welfare-maximizing and core-stable outcome. If these conditions do not hold, we cannot hope for incentive-compatibility and computing welfare-maximizing assignments and core-stable prices is hard: Even in the presence of value queries, where bidders reveal their valuations and budgets truthfully, we prove that the problem becomes NP-complete for the assignment market model. The analysis complements complexity results for markets with more complex valuations and shows that even with simple unit-demand bidders the problem becomes intractable. This raises doubts on the efficiency of simple auction designs as they are used in high-stakes markets, where budget constraints typically play a role.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126809794","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":"Optimal Price Discrimination for Randomized Mechanisms","authors":"Shao-Heng Ko, Kamesh Munagala","doi":"10.1145/3490486.3538335","DOIUrl":"https://doi.org/10.1145/3490486.3538335","url":null,"abstract":"We study the power of price discrimination via an intermediary in bilateral trade, when there is a revenue-maximizing seller selling an item to a buyer with a private value drawn from a prior. Between the seller and the buyer, there is an intermediary that can segment the market by releasing information about the true values to the seller. This is termed signaling, and enables the seller to price discriminate. In this setting, Bergemann et al. showed the existence of a signaling scheme that simultaneously raises the optimal consumer surplus, guarantees the item always sells, and ensures the seller's revenue does not increase. Our work extends the positive result of Bergemann et al. to settings where the type space is larger, and where optimal auction is randomized, possibly over a menu that can be exponentially large. In particular, we consider two settings motivated by budgets: The first is when there is a publicly known budget constraint on the price the seller can charge and the second is the FedEx problem where the buyer has a private deadline or service level (equivalently, a private budget that is guaranteed to never bind). For both settings, we present a novel signaling scheme and its analysis via a continuous construction process that recreates the optimal consumer surplus guarantee of Bergemann et al. and further subsumes their signaling scheme as a special case. In effect, our results show settings where even though the optimal auction is randomized over a possibly large menu, there is a market segmentation such that for each segment, the optimal auction is a simple posted price scheme where the item is always sold. The settings we consider are special cases of the more general problem where the buyer has a private budget constraint in addition to a private value. We finally show that our positive results do not extend to this more general setting, particularly when the budget can bind in the optimal auction, and when the seller's mechanism allows for all-pay auctions. Here, we show that any efficient signaling scheme necessarily transfers almost all the surplus to the seller instead of the buyer.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116682866","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":"Improved Price of Anarchy via Predictions","authors":"Vasilis Gkatzelis, Kostas Kollias, A. Sgouritsa, Xizhi Tan","doi":"10.1145/3490486.3538296","DOIUrl":"https://doi.org/10.1145/3490486.3538296","url":null,"abstract":"A central goal in algorithmic game theory is to analyze the performance of decentralized multiagent systems, like communication and information networks. In the absence of a central planner who can enforce how these systems are utilized, the users can strategically interact with the system, aiming to maximize their own utility, possibly leading to very inefficient outcomes, and thus a high price of anarchy. To alleviate this issue, the system designer can use decentralized mechanisms that regulate the use of each resource (e.g., using local queuing protocols or scheduling mechanisms), but with only limited information regarding the state of the system. These information limitations have a severe impact on what such decentralized mechanisms can achieve, so most of the success stories in this literature have had to make restrictive assumptions (e.g., by either restricting the structure of the networks or the types of cost functions). In this paper, we overcome some of the obstacles that the literature has imposed on decentralized mechanisms, by designing mechanisms that are enhanced with predictions regarding the missing information. Specifically, inspired by the big success of the literature on \"algorithms with predictions\", we design decentralized mechanisms with predictions and evaluate their price of anarchy as a function of the prediction error, focusing on two very well-studied classes of games: scheduling games and multicast network formation games.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116715115","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}