Proceedings of the 21st ACM Conference on Economics and Computation最新文献

{"title":"Prizes on Crowdsourcing Platforms: An Equilibrium Analysis of Competing Contests","authors":"Konstantinos I. Stouras, Sanjiv Erat, K. C. Lichtendahl","doi":"10.1145/3391403.3399549","DOIUrl":"https://doi.org/10.1145/3391403.3399549","url":null,"abstract":"On a typical crowdsourcing platform solvers can self-select which (if any) of the concurrently running contests to participate in. Thus, firms which offer prizes and organize contests on these platforms are competing among themselves (for solver participation and effort). We formalize and model this competition among contests and examine the equilibrium outcomes. Our analysis reveals that, in general, there is a unique dominant strategy for each firm to offer multiple identical prizes. Moreover, when the quality of submitted solutions is sufficiently noise-driven (as opposed to effort-driven), we find that a single winner-take-all reward is the unique equilibrium allocation. Our analytical framework integrates and extends prior results of the monopolistic contest.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121978909","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":"Variance-Weighted Estimators to Improve Sensitivity in Online Experiments","authors":"Kevin Liou, Sean J. Taylor","doi":"10.1145/3391403.3399542","DOIUrl":"https://doi.org/10.1145/3391403.3399542","url":null,"abstract":"As companies increasingly rely on experiments to make product decisions, precisely measuring changes in key metrics is important. Various methods to increase sensitivity in experiments have been proposed, including methods that use pre-experiment data, machine learning, and more advanced experimental designs. However, prior work has not explored modeling heterogeneity in the variance of individual experimental users. We propose a more sensitive treatment effect estimator that relies on estimating the individual variances of experimental users using pre-experiment data. We show that that weighted estimators using individual-level variance estimates can reduce the variance of treatment effect estimates, and prove that the coefficient of variation of the sample population variance is a sufficient statistic for determining the scale of possible variance reduction. We provide empirical results from case studies at Facebook demonstrating the effectiveness of this approach, where the average experiment achieved a 17% reduction in variance with minimal impact on bias.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127557775","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":"The Effects of Influencer Advertising Disclosure Regulations: Evidence From Instagram","authors":"D. Ershov, M. Mitchell","doi":"10.1145/3391403.3399477","DOIUrl":"https://doi.org/10.1145/3391403.3399477","url":null,"abstract":"We collect data from fifty top Instagram influencers in Germany and Spain from 2014 to 2019. Germany experienced changes in disclosure regulation for social media sponsorship during the sample period. Using a difference-in-difference approach, we study the impact of the the rules on the content of posts and the nature of interaction of followers with the posts. On the content side, we measure whether posts include suggested disclosure terms and show variable but substantial adoption of disclosure. We use an approach based on a fixed list of words associated with sponsorship (i.e. links, mentions of brands, use of words like \"sale\") as well as natural language processing to assess the likelihood that a post is sponsored. We show that sponsored content use may have increased after changes in disclosure and that followers may have been negatively affected. On the other hand, there is evidence that consumers' reaction to sponsored posts, measured by likes, may be quite different under stricter disclosure rules, suggesting that the rules could have a substantial impact on information transmission.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122273146","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":"Finite State Machines Play Extensive-Form Games","authors":"Jakub Černý, B. Bosanský, Bo An","doi":"10.1145/3391403.3399517","DOIUrl":"https://doi.org/10.1145/3391403.3399517","url":null,"abstract":"Finite state machines are a well-known representation of strategies in (in)finitely repeated or stochastic games. Actions of players correspond to states in the machine and the transition between machine-states are caused by observations in the game. For extensive-form games (EFGs), machines can act as a formal grounding for abstraction methods used for solving large EFGs and as a domain-independent approach for generating sufficiently compact abstractions. We show that using machines of a restricted size in EFGs can both (i) reduce the theoretical complexity of computing some solution concepts, including Strong Stackelberg Equilibrium (SSE), (ii) as well as bring new practical algorithms that compute near-optimal equilibria considering only a fraction of strategy space. Our contributions include (1) formal definition and theoretical characterization of machine strategies in EFGs, (2) formal definitions and complexity analysis for solution concepts and their computation when restricted to small classes of machines, (3) new algorithms for computing SSE in general-sum games and Nash Equilibrium in zero-sum games that both directly use the concept of machines. Experimental results on two different domains show that the algorithms compute near-optimal strategies and achieve significantly better scalability compared to previous state-of-the-art algorithms.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"79 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129802708","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":"Combinatorial Ski Rental and Online Bipartite Matching","authors":"Hanrui Zhang, Vincent Conitzer","doi":"10.1145/3391403.3399470","DOIUrl":"https://doi.org/10.1145/3391403.3399470","url":null,"abstract":"We consider a combinatorial variant of the classical ski rental problem --- which we call combinatorial ski rental --- where multiple resources are available to purchase and to rent, and are demanded online. Moreover, the costs of purchasing and renting are potentially combinatorial. The dual problem of combinatorial ski rental, which we call combinatorial online bipartite matching, generalizes the classical online bipartite matching problem into a form where constraints, induced by both offline and online vertices, can be combinatorial. We give a 2-competitive (resp. e / (e - 1)-competitive) deterministic (resp. randomized) algorithm for combinatorial ski rental, and an e / (e - 1)-competitive algorithm for combinatorial online bipartite matching. All these ratios are optimal given simple lower bounds inherited from the respective well-studied special cases. We also prove information-theoretic impossibility of constant-factor algorithms when any part of our assumptions is considerably relaxed.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131669190","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 Communication-Distortion Tradeoff in Voting","authors":"Debmalya Mandal, Nisarg Shah, David P. Woodruff","doi":"10.1145/3391403.3399510","DOIUrl":"https://doi.org/10.1145/3391403.3399510","url":null,"abstract":"In recent work, Mandal et al. [2019] study a novel framework for the winner selection problem in voting, in which a voting rule is seen as a combination of an elicitation rule and an aggregation rule. The elicitation rule asks voters to respond to a query based on their preferences over a set of alternatives, and the aggregation rule aggregates voter responses to return a winning alternative. They study the tradeoff between the communication complexity of a voting rule, which measures the number of bits of information each voter must send in response to its query, and its distortion, which measures the quality of the winning alternative in terms of utilitarian social welfare. They prove upper and lower bounds on the communication complexity required to achieve a desired level of distortion, but their bounds are not tight. Importantly, they also leave open the question whether the best randomized rule can significantly outperform the best deterministic rule. We settle this question in the affirmative. For a winner selection rule to achieve distortion d with m alternatives, we show that the communication complexity required is ~Θ (m/d) when using deterministic elicitation, and ~Θ (m/d3) when using randomized elicitation; both bounds are tight up to logarithmic factors. Our upper bound leverages recent advances in streaming algorithms. To establish our lower bound, we derive a new lower bound on a multi-party communication complexity problem. We then study the k-selection problem in voting, where the goal is to select a set of k alternatives. For a k-selection rule that achieves distortion d with m alternatives, we show that the best communication complexity is ~Θ (m/(kd)) when the rule uses deterministic elicitation and ~Θ (m/(kd3)) when the rule uses randomized elicitation. Our optimal bounds yield the non-trivial implication that the k-selection problem becomes strictly easier as k increases.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129006444","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":"Queue Lengths as Constantly Adapting Prices: Allocative Efficiency Under Random Dynamics","authors":"I. Ashlagi, Jacob D. Leshno, Pengyu Qian, A. Saberi","doi":"10.1145/3391403.3399539","DOIUrl":"https://doi.org/10.1145/3391403.3399539","url":null,"abstract":"Waiting lists are common mechanisms for allocating scarce items without monetary transfers. Examples include the allocation of cadaver organs to patients in need of a transplant, public housing apartments to applicants, health care services to patients, and even spots at childcare centers to parents. In all these markets waiting times play the role of prices in guiding the allocation and rationing items. But while prices are set by the designer, waiting times are endogenously determined by the number of agents waiting. Moreover, waiting times are not fixed, and continuously adjust as items arrive or agents join. When agents and items arrive stochastically over time, waiting times stochastically adjust over time. The stochastic adaptation of waiting times adversely impacts the allocative efficiency. If utility is quasi-linear in waiting time, standard competitive equilibrium (CE) arguments show that fixed waiting times can serve as market clearing prices and yield the optimal allocative efficiency. But even if one may expect the endogenously generated waiting times to tend towards market clearing prices, the waiting times keep fluctuating and never converge. Agents may arrive when waiting times are far from the market clearing prices, and their assignment can be inefficient. This paper evaluates the allocative efficiency loss due to the random fluctuations. We consider a standard waiting list mechanism, which holds a separate First Come First Served (FCFS) queue for each of finitely many items. Items arrive over time according to a Poisson process, and are assigned to the first agent in the respective queue. Agents arrive over time according to a Poisson process, observe the length of each queue, and then choose a queue to join or leave the system. An agent who joins a queue must wait there until he receives the item. Agents have heterogeneous private values over the items, and their utility is quasi-linear in waiting costs. That is, all agents have the same waiting costs. We interpret the expected waiting costs at each queue as prices that stochastically adjust as items arrive or agents join the queues. Our key technical observation is that the waiting list's random price adaptation process is equivalent to that of a stochastic gradient descent algorithm (SGD). While each arrival randomly adjusts prices, the expected price adjustment from each arrival moves waiting times towards market clearing prices. However, waiting times never converge. Standard usage of SGD optimization algorithms requires reducing the step size to zero as the algorithm gets closer to the optimal solution. In contrast, the step-size for the waiting list mechanism is determined by the granularity of waiting costs C>0, which is the maximal price impact (i.e., increase in expected waiting costs) of adding one agent to a queue. Our first result states that the allocative efficiency loss from the random price fluctuations is O(C), and this bound is tight. We further show that, if","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116535995","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":"More Revenue from Two Samples via Factor Revealing SDPs","authors":"C. Daskalakis, M. Zampetakis","doi":"10.1145/3391403.3399543","DOIUrl":"https://doi.org/10.1145/3391403.3399543","url":null,"abstract":"We consider the classical problem of selling a single item to a single bidder whose value for the item is drawn from a regular distribution F, in a \"data-poor'' regime where Fis not known to the seller, and very few samples from Fare available. Prior work [Dhangwatnotai et al '10] has shown that one sample from Fcan be used to attain a 1/2-factor approximation to the optimal revenue, but it has been challenging to improve this guarantee when more samples from Fare provided, even when two samples from Fare provided. In this case, the best approximation known to date is 0.509, achieved by the Empirical Revenue Maximizing (ERM) mechanism Babaioff et al. '18]. We improve this guarantee to 0.558, and provide a lower bound of 0.65. Our results are based on a general framework, based on factor-revealing Semidefinite Programming relaxations aiming to capture as tight as possible a superset of product measures of regular distributions, the challenge being that neither regularity constraints nor product measures are convex constraints. The framework is general and can be applied in more abstract settings to evaluate the performance of a policy chosen using independent samples from a distribution and applied on a fresh sample from that same distribution.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":" 48","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060610","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":"Ordered Search with Boundedly Rational Consumers","authors":"Mauro Papi","doi":"10.1145/3391403.3399461","DOIUrl":"https://doi.org/10.1145/3391403.3399461","url":null,"abstract":"The literature on ordered search has assumed consumers to search optimally. In contrast, I investigate a price-competition model in which consumers are boundedly rational and firms use persuasive advertising to influence the consumers' aspiration price, i.e., the price regarded as 'satisfactory'. I consider various variants of the model capturing different consumers' second-best strategies if they do not find any satisfactory price. I derive a number of results including (i) predictions about the correlation between firm prominence and important market indicators, such as profits and conversion rates, that can help explain empirical evidence, (ii) extensions of the basic model, such as an analysis of the relationship between the consumer satisfaction rate and firms profits, and (iii) policy implications of the model.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811157","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":"Information Choice in Auctions","authors":"Nina Bobkova","doi":"10.1145/3391403.3399530","DOIUrl":"https://doi.org/10.1145/3391403.3399530","url":null,"abstract":"Bidders are uncertain about their valuation for an object and choose about which component to learn. Their valuation consists of a common value component (which matters to all bidders) and a private value component (which is relevant only to individual bidders). Learning about a private component yields independent estimates, whereas learning about a common component leads to correlated information between bidders. I analyze the incentives of bidders to choose information about components in the second-price, the first-price and the all-pay auction. I identify conditions for the second-price auction, such that bidders only learn about their private component: an independent private value framework and an efficient outcome arise endogenously. In a first-price auction, every robust equilibrium is inefficient under certain conditions.","PeriodicalId":148025,"journal":{"name":"Proceedings of the 21st ACM Conference on Economics and Computation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121499064","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}