Proceedings of the 2019 ACM Conference on Economics and Computation最新文献_第7页

Imitative Follower Deception in Stackelberg Games Stackelberg游戏中的模仿追随者欺骗

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-03-07 DOI: 10.1145/3328526.3329629

Jiarui Gan, Haifeng Xu, Qingyu Guo, Long Tran-Thanh, Zinovi Rabinovich, M. Wooldridge

{"title":"Imitative Follower Deception in Stackelberg Games","authors":"Jiarui Gan, Haifeng Xu, Qingyu Guo, Long Tran-Thanh, Zinovi Rabinovich, M. Wooldridge","doi":"10.1145/3328526.3329629","DOIUrl":"https://doi.org/10.1145/3328526.3329629","url":null,"abstract":"Information uncertainty is one of the major challenges facing applications of game theory. In the context of Stackelberg games, various approaches have been proposed to deal with the leader's incomplete knowledge about the follower's payoffs, typically by gathering information from the leader's interaction with the follower. Unfortunately, these approaches rely crucially on the assumption that the follower will not strategically exploit this information asymmetry, i.e., the follower behaves truthfully during the interaction according to their actual payoffs. As we show in this paper, the follower may have strong incentives to deceitfully imitate the behavior of a different follower type and, in doing this, benefit significantly from inducing the leader into choosing a highly suboptimal strategy. This raises a fundamental question: how to design a leader strategy in the presence of a deceitful follower? To answer this question, we put forward a basic model of Stackelberg games with (imitative) follower deception and show that the leader is indeed able to reduce the loss due to follower deception with carefully designed policies. We then provide a systematic study of the problem of computing the optimal leader policy and draw a relatively complete picture of the complexity landscape; essentially matching positive and negative complexity results are provided for natural variants of the model. Our intractability results are in sharp contrast to the situation with no deception, where the leader's optimal strategy can be computed in polynomial time, and thus illustrate the intrinsic difficulty of handling follower deception. Through simulations we also examine the benefit of considering follower deception in randomly generated games.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127334278","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: 23

Identifying Bid Leakage in Procurement Auctions: Machine Learning Approach 识别采购拍卖中的投标泄漏:机器学习方法

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-03-01 DOI: 10.1145/3328526.3329642

Dmitry Ivanov, Alexander S. Nesterov

引用次数: 7

Buy-Many Mechanisms are Not Much Better than Item Pricing 多买机制并不比道具定价好多少

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-27 DOI: 10.1145/3328526.3329583

Shuchi Chawla, Yifeng Teng, Christos Tzamos

{"title":"Buy-Many Mechanisms are Not Much Better than Item Pricing","authors":"Shuchi Chawla, Yifeng Teng, Christos Tzamos","doi":"10.1145/3328526.3329583","DOIUrl":"https://doi.org/10.1145/3328526.3329583","url":null,"abstract":"Multi-item revenue optimal mechanisms can be very complex offering many different bundles to the buyer that could even be randomized. Such complexity is thought to be necessary as the revenue gaps between randomized and deterministic mechanisms, or deterministic and simple mechanisms are huge even for additive valuations. We challenge this conventional belief by showing that these large gaps can only happen in unrealistic situations. These are situations where the mechanism overcharges a buyer for a bundle while selling individual items at much lower prices. Arguably this is impractical as the buyer can break his order into smaller pieces paying a much lower price overall. Our main result is that if the buyer is allowed to purchase as many (randomized) bundles as he pleases, the revenue of any multi-item mechanism is at most O(łog n) times the revenue achievable by item pricing, where n is the number of items. This holds in the most general setting possible, with an arbitrarily correlated distribution of buyer types and arbitrary valuations. We also show that this result is tight in a very strong sense. Any family of mechanisms of subexponential description complexity cannot achieve better than logarithmic approximation even against the best deterministic mechanism and even for additive valuations. In contrast, item pricing that has linear description complexity matches this bound against randomized mechanisms.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"1984 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128224343","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: 9

Estimating Approximate Incentive Compatibility 估计近似激励相容性

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-25 DOI: 10.1145/3328526.3329628

Maria-Florina Balcan, T. Sandholm, Ellen Vitercik

{"title":"Estimating Approximate Incentive Compatibility","authors":"Maria-Florina Balcan, T. Sandholm, Ellen Vitercik","doi":"10.1145/3328526.3329628","DOIUrl":"https://doi.org/10.1145/3328526.3329628","url":null,"abstract":"In practice, most mechanisms for selling, buying, matching, voting, and so on are not incentive compatible. We present techniques for estimating how far a mechanism is from incentive compatible. Given samples from the agents' type distribution, we show how to estimate the extent to which an agent can improve his utility by misreporting his type. We do so by first measuring the maximum utility an agent can gain by misreporting his type on average over the samples, assuming his true and reported types are from a finite subset---which our technique constructs---of the type space. The challenge is that by measuring utility gains over a finite subset of the type space, we might miss pairs of types t and t' where an agent with type t can greatly improve his utility by reporting the type t'. Our technique discretizes the type space by constructing a learning-theoretic cover in a higher-dimensional space. The key technical contribution is proving that the maximum utility gain over this finite subset nearly matches the maximum utility gain overall, despite the volatility of the utility functions we study. We apply our tools to the single-item and combinatorial first-price auctions, generalized second-price auction, discriminatory auction, uniform-price auction, and second-price auction with spiteful bidders. To our knowledge, these are the first guarantees for estimating approximate incentive compatibility from the mechanism designer's perspective.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128249607","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: 23

Sample-Based Optimal Pricing 基于样本的最优定价

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-25 DOI: 10.2139/ssrn.3334650

Amine Allouah, Omar Besbes

引用次数: 8

Naive Learning Through Probability Matching

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-19 DOI: 10.2139/ssrn.3338015

Itai Arieli, Y. Babichenko, Manuel Mueller-Frank

引用次数: 8

Stable Fractional Matchings 稳定分数匹配

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-18 DOI: 10.1145/3328526.3329637

I. Caragiannis, Aris Filos-Ratsikas, Panagiotis A. Kanellopoulos, Rohit Vaish

引用次数: 10

Prophet Inequality for Bipartite Matching: Merits of Being Simple and Non Adaptive 二部匹配的先知不等式:简单和非自适应的优点

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-17 DOI: 10.1145/3328526.3329604

N. Gravin, Hongao Wang

引用次数: 39

Matchings under Preferences: Strength of Stability and Trade-Offs 偏好下的匹配:稳定性的强度和权衡

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-15 DOI: 10.1145/3328526.3329555

Jiehua Chen, P. Skowron, Manuel Sorge

{"title":"Matchings under Preferences: Strength of Stability and Trade-Offs","authors":"Jiehua Chen, P. Skowron, Manuel Sorge","doi":"10.1145/3328526.3329555","DOIUrl":"https://doi.org/10.1145/3328526.3329555","url":null,"abstract":"We propose two solution concepts for matchings under preferences: robustness and near stability. The former strengthens while the latter relaxes the classic definition of stability by Gale and Shapley (1962). Informally speaking, robustness requires that a matching must be stable in the classic sense, even if the agents slightly change their preferences. Near stability, on the other hand, imposes that a matching must become stable (again, in the classic sense) provided the agents are willing to adjust their preferences a bit. Both of our concepts are quantitative; together they provide means for a fine-grained analysis of the stability of matchings. Moreover, our concepts allow the exploration of trade-offs between stability and other criteria of social optimality, such as the egalitarian cost and the number of unmatched agents. We investigate the computational complexity of finding matchings that implement certain predefined trade-offs. We provide a polynomial-time algorithm that, given agent preferences, returns a socially optimal robust matching, and we prove that finding a socially optimal and nearly stable matching is computationally hard.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"218 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122394145","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: 20

The Perils of Exploration under Competition: A Computational Modeling Approach 竞争下探索的危险:一种计算建模方法

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2019-02-14 DOI: 10.1145/3328526.3329635

Guy Aridor, K. Liu, Aleksandrs Slivkins, Zhiwei Steven Wu

{"title":"The Perils of Exploration under Competition: A Computational Modeling Approach","authors":"Guy Aridor, K. Liu, Aleksandrs Slivkins, Zhiwei Steven Wu","doi":"10.1145/3328526.3329635","DOIUrl":"https://doi.org/10.1145/3328526.3329635","url":null,"abstract":"We empirically study the interplay between exploration and competition. Systems that learn from interactions with users often engage in exploration: making potentially suboptimal decisions in order to acquire new information for future decisions. However, when multiple systems are competing for the same market of users, exploration may hurt a system's reputation in the near term, with adverse competitive effects. In particular, a system may enter a \"death spiral\", when the short-term reputation cost decreases the number of users for the system to learn from, which degrades the system's performance relative to competition and further decreases the market share. We ask whether better exploration algorithms are incentivized under competition. We run extensive numerical experiments in a stylized duopoly model in which two firms deploy multi-armed bandit algorithms and compete for myopic users. We find that duopoly and monopoly tend to favor a primitive \"greedy algorithm\" that does not explore and leads to low consumer welfare, whereas a temporary monopoly (a duopoly with an early entrant) may incentivize better bandit algorithms and lead to higher consumer welfare. Our findings shed light on the first-mover advantage in the digital economy by exploring the role that data can play as a barrier to entry in online markets.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114174674","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: 10