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

{"title":"Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data","authors":"Tesary Lin, Avner Strulov-Shlain","doi":"10.1145/3580507.3597674","DOIUrl":"https://doi.org/10.1145/3580507.3597674","url":null,"abstract":"Companies often deploy some form of \"choice architecture\" when collecting consumer data, designed to nudge consumers towards sharing more private information. This study examines when an emphasis on maximizing the volume of data shared when deploying choice architecture can alter the composition of the collected data, hence creating a trade-off between the quantity and representativeness of data collected. To this end, we ran a large-scale choice experiment to elicit consumers' incentive-compatible valuation for their private Facebook data while randomizing the choice frames they encountered. Within participants, we elicited WTA using a multiple-price list, followed by a free-text entry. Across participants, we randomized the choice default and the price anchor. The default varied between opt-in, opt-out, and active choice. Price anchor was the range of prices in the multiple price list, which was either $0--$50 (low) or $50--$100 (high).","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128317423","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":"Adaptive Algorithms and Collusion via Coupling","authors":"M. Banchio, Giacomo Mantegazza","doi":"10.1145/3580507.3597726","DOIUrl":"https://doi.org/10.1145/3580507.3597726","url":null,"abstract":"Learning algorithms are proliferating in a variety of business contexts, ranging from automated bidding in online auctions to pricing on shopping platforms and setting rents. This diffusion has been accompanied by fears that such automation could facilitate collusion. A number of recent papers on algorithmic pricing show in simulations that learning algorithms coordinate on less-than-competitive outcomes.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125805981","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 Design of Online Platforms","authors":"T. Ke, Song Lin, Michelle Y. Lu","doi":"10.1145/3580507.3597770","DOIUrl":"https://doi.org/10.1145/3580507.3597770","url":null,"abstract":"We consider the strategic use of information by an online platform to both guide consumers' search through product recommendations and influence sellers' targeted advertising decision. Drawing on Bayesian persuasion, we posit that the platform can design a public signal that influences the beliefs of both consumers and sellers. Upon observing the signal, a consumer can conduct a sequential search with perfect recall among the sellers. After visiting a seller, the consumer observes the product price and whether the product is a match or not. Sellers set prices and decide how much to bid in an ad auction for each consumer, where the winner is granted a prominent position. The consumer can obtain the price and match information of the seller in the prominent position at no cost, but incur search cost to visit additional sellers.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126422567","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":"Pollution Permits: Efficiency by Design","authors":"M. Pycia, Kyle Woodward","doi":"10.1145/3580507.3597784","DOIUrl":"https://doi.org/10.1145/3580507.3597784","url":null,"abstract":"The annual adverse effects of pollution are on the order of 10% of world GDP. Many approaches are used or have been proposed to control the growing pollution problem, but none of them allows for efficient pollution control in settings in which the marginal cost of pollution is increasing and polluters are better informed than regulators about the costs of abatement. In particular, taxes, quantity restrictions, uniform-price auctions, and the usual implementations of discriminatory auctions (e.g., by the U.S. Environmental Protection Agency) in general lead to inefficient allocations. We propose a simple primary market mechanism, True-Cost Pay as Bid (TCPAB), that implements efficient pollution control and does not depend on how much information regulators have about firms' abatement costs. When polluters have symmetric information and the marginal cost of pollution is known, TCPAB implements an efficient primary market allocation. When the marginal cost of pollution is uncertain, TCPAB implements the most efficient allocation possible without further information on the true marginal cost. When polluters have asymmetric information about their opponents' costs of abatement, the inefficiency of TCPAB is small provided the informational asymmetry is not too large. TCPAB's favorable properties extend to dynamic environments provided limits are placed on the trading of permits across time. We also discuss how an adoption of TCPAB may facilitate international bargaining over emissions abatement.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125525709","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":"Fairness and Incentive Compatibility via Percentage Fees","authors":"Shahar Dobzinski, Sigal Oren, J. Vondrák","doi":"10.1145/3580507.3597810","DOIUrl":"https://doi.org/10.1145/3580507.3597810","url":null,"abstract":"We study incentive-compatible mechanisms that maximize the Nash Social Welfare. Since traditional incentive-compatible mechanisms cannot maximize the Nash Social Welfare even approximately, we propose changing the traditional model. Inspired by a widely used charging method (e.g., royalties, a lawyer that charges some percentage of possible future compensation), we suggest charging the players some percentage of their value of the outcome. We call this model the percentage fee model. We show that there is a mechanism that maximizes exactly the Nash Social Welfare in every setting with non-negative valuations. Moreover, we prove an analog of Roberts theorem that essentially says that if the valuations are non-negative, then the only implementable social choice functions are those that maximize weighted variants of the Nash Social Welfare. We develop polynomial time incentive compatible approximation algorithms for the Nash Social Welfare with subadditive valuations and prove some hardness results.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"10 17","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133651369","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":"Correlated Cluster-Based Randomized Experiments: Robust Variance Minimization","authors":"Ozan Candogan, Chen Chen, Rad Niazadeh","doi":"10.1145/3580507.3597820","DOIUrl":"https://doi.org/10.1145/3580507.3597820","url":null,"abstract":"Experimentation is prevalent in online marketplaces and social networks to assess the effectiveness of new market intervention. In an experiment, the platform exposes a (randomized) group of targeted users to the new feature or, equivalently, assigns each user to either the treatment or the control group. The platform then uses the resulting outcomes to estimate the new feature's total market effect, i.e., the difference in total user outcomes if the feature is introduced to the entire market.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134067768","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 Contracts: A Revealed Preference Approach","authors":"N. Antic, G. Georgiadis","doi":"10.1145/3580507.3597696","DOIUrl":"https://doi.org/10.1145/3580507.3597696","url":null,"abstract":"We study an agency model in which the principal has outcome data under different incentive schemes and aims to design an optimal contract under minimal assumptions about the way the agent responds to incentives. Events unfold as follows: (1) the principal offers a contract---a mapping from output to nonnegative payments; (2) the agent chooses costly action---a probability distribution over output; and (3) output and payoffs are realized. The principal has outcome data under K different contracts which, sidestepping estimation error, enables her to recover the action corresponding to each of these contracts. We assume that the agent best-responds to the offered contract and has quasi-linear preferences over money and actions, but we make no further assumptions about the production environment. The principal does not have prior beliefs about any of the unknown aspects of the environment. Instead, she seeks a contract that maximizes worst-case profit.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127374671","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":"Communication via hard and soft information","authors":"Sulagna Dasgupta","doi":"10.1145/3580507.3597779","DOIUrl":"https://doi.org/10.1145/3580507.3597779","url":null,"abstract":"In this paper I study the interaction of cheap talk and verifiable disclosure. I use a canonical model of a sender-receiver game of verifiable disclosure a la Milgrom (1981), and augment it by allowing the use of cheap talk in addition. Specifically, there is a single dimensional state privately observed by the sender. The sender's preferences are state-independent. In particular, they depend only on the receiver's posterior mean state. The sender can verifiably reveal the state exactly, and/or use cheap talk to communicate with the receiver.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123024400","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":"Location Choice, Commuting, and School Choice","authors":"Minseon Park, Dongchul Hahm","doi":"10.1145/3580507.3597689","DOIUrl":"https://doi.org/10.1145/3580507.3597689","url":null,"abstract":"We explore the impact of public school assignment reforms by building a households' school choice model that considers two important margins through which households may respond: (1) residential location choice and (2) opt-out to outside schooling options.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115994849","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":"Temporal Fairness in Learning and Earning: Price Protection Guarantee and Phase Transitions","authors":"Qing Feng, Ruihao Zhu, Stefanus Jasin","doi":"10.1145/3580507.3597668","DOIUrl":"https://doi.org/10.1145/3580507.3597668","url":null,"abstract":"Motivated by the prevalence of \"price protection guarantee\", which helps to promote temporal fairness in dynamic pricing, we study the impact of such policy on the design of online learning algorithm for data-driven dynamic pricing with initially unknown customer demand. Under the price protection guarantee, a customer who purchased a product in the past can receive a refund from the seller during the so-called price protection period (typically defined as a certain time window after the purchase date) in case the seller decides to lower the price. We consider a setting where a firm sells a product over a horizon of T time steps. For this setting, we characterize how the value of M, the length of price protection period, can affect the optimal regret of the learning process. Our contributions can be summarized as follows: • Inadequacy of Existing Algoirthms: We demonstrate that directly applying conventional dynamic pricing algorithms, such Upper Confidence Bound (UCB) algorithm and Thompson Sampling (TS) algorithm, may incur linear regret in the presence of price protection. We use both theoretical and numerical evidences to support this claim; • Regret Lower and Upper Bounds: We show that the optimal regret is [EQUATION] by first establishing a fundamental impossible regime with the novel refund-aware regret lower bound analysis. Then, we propose LEAP, a phased exploration type algorithm for Learning and EArning under Price Protection to match this lower bound up to logarithmic factors or even doubly logarithmic factors (when there are only two prices available to the seller); • Phase Transitions of Optimal Regret: Our results reveal the surprising phase transitions of the optimal regret with respect to M. Specifically, when M is not too large, the optimal regret has no major difference when compared to that of the classic setting with no price protection guarantee. We also show that there exists an upper limit on how much the optimal regret can deteriorate when M grows large; • Numerical Simulations: Finally, we conduct extensive numerical experiments to show the benefit of LEAP over other heuristic methods for this problem.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"129 8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124241187","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}