Proceedings of the Sixteenth ACM Conference on Economics and Computation最新文献_第3页

Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization 传统营销与病毒营销相结合，实现两栖影响力最大化

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764480

Wei Chen, Fu Li, Tian Lin, A. Rubinstein

{"title":"Combining Traditional Marketing and Viral Marketing with Amphibious Influence Maximization","authors":"Wei Chen, Fu Li, Tian Lin, A. Rubinstein","doi":"10.1145/2764468.2764480","DOIUrl":"https://doi.org/10.1145/2764468.2764480","url":null,"abstract":"In this paper, we propose the amphibious influence maximization (AIM) model that combines traditional marketing via content providers and viral marketing to consumers in social networks in a single framework. In AIM, a set of content providers and consumers form a bipartite network while consumers also form their social network, and influence propagates from the content providers to consumers and among consumers in the social network following the independent cascade model. An advertiser needs to select a subset of seed content providers and a subset of seed consumers, such that the influence from the seed providers passing through the seed consumers could reach a large number of consumers in the social network in expectation. We prove that the AIM problem is NP-hard to approximate to within any constant factor via a reduction from Feige's k-prover proof system for 3-SAT5. We also give evidence that even when the social network graph is trivial (i.e. has no edges), a polynomial time constant factor approximation for AIM is unlikely. However, when we assume that the weighted bi-adjacency matrix that describes the influence of content providers on consumers is of constant rank, a common assumption often used in recommender systems, we provide a polynomial-time algorithm that achieves approximation ratio of (1-1/e-ε)3 for any (polynomially small) ε > 0. Our algorithmic results still hold for a more general model where cascades in social network follow a general monotone and submodular function.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"07 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128958115","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: 21

A Non-asymptotic Approach to Analyzing Kidney Exchange Graphs 分析肾脏交换图的非渐近方法

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764494

Yichuan Ding, Dongdong Ge, Simai He, C. Ryan

引用次数: 21

Truthful Online Scheduling with Commitments 真实的在线日程安排与承诺

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764535

Y. Azar, Inna Kalp-Shaltiel, Brendan Lucier, Ishai Menache, J. Naor, Jonathan Yaniv

引用次数: 62

Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb 在线评论中的偏见和互惠:来自Airbnb现场实验的证据

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764528

Andrey Fradkin, Elena Grewal, David Holtz, Matthew Pearson

{"title":"Bias and Reciprocity in Online Reviews: Evidence From Field Experiments on Airbnb","authors":"Andrey Fradkin, Elena Grewal, David Holtz, Matthew Pearson","doi":"10.1145/2764468.2764528","DOIUrl":"https://doi.org/10.1145/2764468.2764528","url":null,"abstract":"Reviews and other evaluations are used by consumers to decide what goods to buy and by firms to choose whom to trade with, hire, or promote. However, because potential reviewers are not compensated for submitting reviews and may have reasons to omit relevant information in their reviews, reviews may be biased. We use the setting of Airbnb to study the determinants of reviewing behavior, the extent to which reviews are biased, and whether changes in the design of reputation systems can reduce that bias. We find that reviews on Airbnb are generally informative and 97% of guests privately report having positive experiences. Using two field experiments intended to reduce bias, we show that non-reviewers tend to have worse experiences than reviewers and that strategic reviewing behavior occurred on the site, although the aggregate effect of the strategic behavior was relatively small. We use a quantitative exercise to show that the mechanisms for bias that we document decrease the rate of reviews with negative text and a non-recommendation by just .86 percentage points. Lastly, we discuss how online marketplaces can design more informative review systems.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115356699","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: 197

Revenue Maximization and Ex-Post Budget Constraints 收益最大化和事后预算约束

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764521

C. Daskalakis, Nikhil R. Devanur, S. M. Weinberg

{"title":"Revenue Maximization and Ex-Post Budget Constraints","authors":"C. Daskalakis, Nikhil R. Devanur, S. M. Weinberg","doi":"10.1145/2764468.2764521","DOIUrl":"https://doi.org/10.1145/2764468.2764521","url":null,"abstract":"We consider the problem of a revenue-maximizing seller with $m$ items for sale to $n$ additive bidders with hard budget constraints, assuming that the seller has some prior distribution over bidder values and budgets. The prior may be correlated across items and budgets of the same bidder, but is assumed independent across bidders. We target mechanisms that are Bayesian Incentive Compatible, but that are ex-post Individually Rational and ex-post budget respecting. Virtually no such mechanisms are known that satisfy all these conditions and guarantee any revenue approximation, even with just a single item. We provide a computationally efficient mechanism that is a 3-approximation with respect to all BIC, ex-post IR, and ex-post budget respecting mechanisms. Note that the problem is NP-hard to approximate better than a factor of 16/15, even in the case where the prior is a point mass [Chakrabarty and Goel 2010]. We further characterize the optimal mechanism in this setting, showing that it can be interpreted as a distribution over virtual welfare maximizers. We prove our results by making use of a black-box reduction from mechanism to algorithm design developed by [Cai et al. 2013]. Our main technical contribution is a computationally efficient 3-approximation algorithm for the algorithmic problem that results by an application of their framework to this problem. The algorithmic problem has a mixed-sign objective and is NP-hard to optimize exactly, so it is surprising that a computationally efficient approximation is possible at all. In the case of a single item (m=1), the algorithmic problem can be solved exactly via exhaustive search, leading to a computationally efficient exact algorithm and a stronger characterization of the optimal mechanism as a distribution over virtual value maximizers.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130436354","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: 13

Dynamic Fair Division with Minimal Disruptions 动态公平分工与最小的干扰

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764495

E. Friedman, Alexandros Psomas, Shai Vardi

引用次数: 33

Online Allocation with Traffic Spikes: Mixing Adversarial and Stochastic Models 具有流量峰值的在线分配:混合对抗和随机模型

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764536

Hossein Esfandiari, Nitish Korula, V. Mirrokni

{"title":"Online Allocation with Traffic Spikes: Mixing Adversarial and Stochastic Models","authors":"Hossein Esfandiari, Nitish Korula, V. Mirrokni","doi":"10.1145/2764468.2764536","DOIUrl":"https://doi.org/10.1145/2764468.2764536","url":null,"abstract":"Motivated by Internet advertising applications, online allocation problems have been studied extensively in various adversarial and stochastic models. While the adversarial arrival models are too pessimistic, many of the stochastic (such as i.i.d or random-order) arrival models do not realistically capture uncertainty in predictions. A significant cause for such uncertainty is the presence of unpredictable traffic spikes, often due to breaking news or similar events. To address this issue, a simultaneous approximation framework has been proposed to develop algorithms that work well both in the adversarial and stochastic models; however, this framework does not enable algorithms that make good use of partially accurate forecasts when making online decisions. In this paper, we propose a robust online stochastic model that captures the nature of traffic spikes in online advertising. In our model, in addition to the stochastic input for which we have good forecasting, an unknown number of impressions arrive that are adversarially chosen.We design algorithms that combine an stochastic algorithm with an online algorithm that adaptively reacts to inaccurate predictions. We provide provable bounds for our new algorithms in this framework. We accompany our positive results with a set of hardness results showing that that our algorithms are not far from optimal in this framework. As a byproduct of our results, we also present improved online algorithms for a slight variant of the simultaneous approximation framework.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134508147","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: 56

The Impact of the Sharing Economy on the Hotel Industry: Evidence from Airbnb's Entry Into the Texas Market 共享经济对酒店业的影响:来自Airbnb进入德克萨斯州市场的证据

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764524

G. Zervas, Davide Proserpio, J. Byers

{"title":"The Impact of the Sharing Economy on the Hotel Industry: Evidence from Airbnb's Entry Into the Texas Market","authors":"G. Zervas, Davide Proserpio, J. Byers","doi":"10.1145/2764468.2764524","DOIUrl":"https://doi.org/10.1145/2764468.2764524","url":null,"abstract":"Spurred by technological advancement, a number of decentralized peer-to-peer markets, now colloquially known as the sharing economy, have emerged as alternative suppliers of goods and services traditionally provided by long-established industries. A central question surrounding the sharing economy regards its long-term impact: will peer-to-peer platforms materialize as viable mainstream alternatives to traditional providers, or will they languish as niche markets? In this paper, we study Airbnb, a sharing economy pioneer offering short-term accommodation. Combining data from Airbnb and the Texas hotel industry, we estimate the impact of Airbnb's entry into the Texas market on hotel room revenue, and study the market response of hotels. To identify Airbnb's causal impact on hotel room revenue, we use a difference-in-differences empirical strategy that exploits the significant spatiotemporal variation in the patterns of Airbnb adoption across citylevel markets. We estimate that each 10% increase in Airbnb supply results in a 0:37% decrease in monthly hotel room revenue. In Austin, where Airbnb supply is highest, the impact on hotel revenue exceeds 10%. We find that Airbnb's impact is non-uniformly distributed, with lower-priced hotels, and hotels not catering to business travel being the most affected segments. Finally, we find that affected hotels have responded by reducing prices, an impact that benefits all consumers, not just participants in the sharing economy. Our work provides empirical evidence that the sharing economy is making inroads by successfully competing with, and acquiring market share from, incumbent firms.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132137931","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: 33

Faster First-Order Methods for Extensive-Form Game Solving 广义博弈求解的快速一阶方法

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764476

Christian Kroer, K. Waugh, F. Kılınç-Karzan, T. Sandholm

{"title":"Faster First-Order Methods for Extensive-Form Game Solving","authors":"Christian Kroer, K. Waugh, F. Kılınç-Karzan, T. Sandholm","doi":"10.1145/2764468.2764476","DOIUrl":"https://doi.org/10.1145/2764468.2764476","url":null,"abstract":"We study the problem of computing a Nash equilibrium in large-scale two-player zero-sum extensive-form games. While this problem can be solved in polynomial time, first-order or regret-based methods are usually preferred for large games. Regret-based methods have largely been favored in practice, in spite of their theoretically inferior convergence rates. In this paper we investigate the acceleration of first-order methods both theoretically and experimentally. An important component of many first-order methods is a distance-generating function. Motivated by this, we investigate a specific distance-generating function, namely the dilated entropy function, over treeplexes, which are convex polytopes that encompass the strategy spaces of perfect-recall extensive-form games. We develop significantly stronger bounds on the associated strong convexity parameter. In terms of extensive-form game solving, this improves the convergence rate of several first-order methods by a factor of O((#information sets ⋅ depth ⋅ M)/(2depth)) where M is the maximum value of the l1 norm over the treeplex encoding the strategy spaces. Experimentally, we investigate the performance of three first-order methods (the excessive gap technique, mirror prox, and stochastic mirror prox) and compare their performance to the regret-based algorithms. In order to instantiate stochastic mirror prox, we develop a class of gradient sampling schemes for game trees. Equipped with our distance-generating function and sampling scheme, we find that mirror prox and the excessive gap technique outperform the prior regret-based methods for finding medium accuracy solutions","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122728927","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: 49

Algorithms against Anarchy: Understanding Non-Truthful Mechanisms 对抗无政府状态的算法:理解非真实机制

Proceedings of the Sixteenth ACM Conference on Economics and Computation Pub Date : 2015-06-15 DOI: 10.1145/2764468.2764507

Paul Dütting, Thomas Kesselheim

{"title":"Algorithms against Anarchy: Understanding Non-Truthful Mechanisms","authors":"Paul Dütting, Thomas Kesselheim","doi":"10.1145/2764468.2764507","DOIUrl":"https://doi.org/10.1145/2764468.2764507","url":null,"abstract":"The algorithmic requirements for dominant strategy incentive compatibility, or truthfulness, are well understood. Is there a similar characterization of algorithms that when combined with a suitable payment rule yield near-optimal welfare in all equilibria? We address this question by providing a tight characterization of a (possibly randomized) mechanism's Price of Anarchy provable via smoothness, for single-parameter settings. The characterization assigns a unique value to each allocation algorithm; this value provides an upper and a matching lower bound on the Price of Anarchy of a derived mechanism provable via smoothness. The characterization also applies to the sequential or simultaneous composition of single-parameter mechanisms. Importantly, the factor that we identify is typically not in one-to-one correspondence to the approximation guarantee of the algorithm. Rather, it is usually the product of the approximation guarantee and the degree to which the mechanism is loser independent. We apply our characterization to show the optimality of greedy mechanisms for single-minded combinatorial auctions, whether these mechanisms are polynomial-time computable or not. We also use it to establish the optimality of a non-greedy, randomized mechanism for independent set in interval graphs and show that it is strictly better than any other deterministic mechanism.","PeriodicalId":376992,"journal":{"name":"Proceedings of the Sixteenth ACM Conference on Economics and Computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125159354","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: 16