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

{"title":"Alone, Together: A Model of Social (Mis)Learning from Consumer Reviews","authors":"Tommaso Bondi","doi":"10.1145/3580507.3597768","DOIUrl":"https://doi.org/10.1145/3580507.3597768","url":null,"abstract":"The Internet has had a significant impact on how consumers discover and evaluate products, particularly by means of consumer reviews. Learning from reviews, however, is made difficult by the fact that, to some extent, reviews measure idiosyncratic consumer-product fit, and not just objective quality. In this context, how and what can consumers learn?","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139361784","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":"I'll Tell You Tomorrow: Committing to Future Commitments","authors":"Andrew Choi","doi":"10.2139/ssrn.4453920","DOIUrl":"https://doi.org/10.2139/ssrn.4453920","url":null,"abstract":"This paper studies a principal who commits to inform an agent about what the principal will eventually do. The principal wishes to promote the agent if and only if the state is good, and he gradually receives private information about the state. The agent always wishes to be promoted, but faces a decreasing outside option and would rather leave if she expects not to be promoted. The principal optimally induces the agent to stay by committing to commit, that is, by committing today to tell the agent tomorrow about her chances of promotion the day after. When the agent has a high initial outside option, with some probability, the principal promotes the agent regardless of his information---even if he realizes early that the state will turn out to be bad. The principal may ask the agent to stay until he fully observes the state, only to deny her promotion; this does not necessarily mean that the principal leads the agent on. We apply our results to worker retention, relationship-specific investment, and forward guidance.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"86 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":"115815412","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":"Managed Campaigns and Data-Augmented Auctions for Digital Advertising","authors":"D. Bergemann, A. Bonatti, Nicholas Wu","doi":"10.1145/3580507.3597807","DOIUrl":"https://doi.org/10.1145/3580507.3597807","url":null,"abstract":"Digital advertising facilitates the matching of consumers and advertisers online. Large platforms leverage their extensive consumer data to offer access to qualified online shoppers, helping them find their preferred brands. In turn, advertisers join these platforms to target a wider range of potential consumers beyond their existing customer base.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"98 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":"124918358","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":"Signaling Competition in Two-Sided Markets","authors":"Omar Besbes, Yuri R. Fonseca, I. Lobel, Fanyin Zheng","doi":"10.2139/ssrn.4451693","DOIUrl":"https://doi.org/10.2139/ssrn.4451693","url":null,"abstract":"Platforms facilitating many-to-many matches in two-sided markets have become ubiquitous across industries ranging from professional services to dating. Differently from standard (one-sided) markets where consumers choose goods or services, in two-sided markets, both sides have preferences. Since these preferences can often be hard to describe, centralized matching is difficult to implement. The alternative option is for the platform to operate in a decentralized fashion, leaving the agents from both sides \"free to find each other\". While easier to implement, the downside of decentralized systems is that inefficiencies driven by congestion are likely to arise. In the present paper, we are primarily interested in understanding the power of \"detail-free\" levers that decentralized platforms can leverage to improve market outcomes. In particular, we focus on the lever of information design through competition signaling, where the platform discloses how much competition currently exists for a given supply unit. Signaling that there is competition for a supply unit may reduce the value of that unit but may also redirect the demand's attention to alternative supply units, potentially increasing the value for the platform. To quantify the trade-off at play and tackle the question above, we focus on a specific labor platform and the submarket of cleaning services to answer this question empirically. We partnered with the largest service labor marketplace in Latin America, which operates as follows. Service providers (agents) join the platform to purchase nonexclusive leads for jobs posted by supply-side customers. When they purchase a lead, they are not guaranteed to get the job, but simply purchase the contact information of the customer in order to apply for the job. A key characteristic of this market is the possible congestion on the lead side. In the context of such a platform, to understand the impact of any lever on market outcomes, it is fundamental to first understand how agents make their lead purchasing decisions and, in particular, how they take competition into account when making such decisions. We propose a structural model in which agents use a prediction function to forecast how much competition they may face. We show that if agents are strategic, a natural concept of equilibrium arises. By leveraging the platforms' data and an quasi-experiment, we estimate the structural parameters in the model. We find that agents react strongly to observed competition and predictions of future competition. We then conduct counterfactual analysis to study the impact of signaling competition. Our findings show that it is a powerful lever to improve market outcomes in this market. Signaling competition improves (decreases) congestion, and it also improves (increases) the probability that a lead will receive at least one applicant. Furthermore, displaying competition leads to an increase in overall leads purchased.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"77 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":"124954813","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":"Which wage distributions are consistent with statistical discrimination?","authors":"R. Deb, L. Renou","doi":"10.1145/3580507.3597667","DOIUrl":"https://doi.org/10.1145/3580507.3597667","url":null,"abstract":"In this paper, we propose a general non-parametric model of statistical discrimination in the labor market, and derive a test for statistical discrimination that only requires cross-sectional data on wages. There are two groups whose productivity distributions have identical means, but can otherwise be different. The group identity is observable to employers, but productivities are not. Instead, there are group-dependent statistical experiments that generate signals about the underlying productivity. Signals induce posterior productivity distributions (via Bayes' rule) and, in particular, these can be used to compute posterior estimates (the mean of the productivity conditional on the signal) of the unobserved productivity. Therefore, each group's statistical experiment generates a distribution over posterior productivity estimates. Wages are then determined via a strictly increasing, continuous function of the posterior productivity estimate that, importantly, does not depend on the group. We say that two wage distributions - one for each of the two groups - are consistent with statistical discrimination if they can be rationalized by this model.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"34 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":"122613443","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":"Multi-Item Order Fulfillment Revisited: LP Formulation and Prophet Inequality","authors":"Ayoub Amil, A. Makhdoumi, Y. Wei","doi":"10.1145/3580507.3597700","DOIUrl":"https://doi.org/10.1145/3580507.3597700","url":null,"abstract":"In this work, we revisit the multi-item order fulfillment model introduced by [Jasin and Sinha 2015]. Specifically, we study a dynamic setting in which an e-commerce platform (or online retailer) with multiple warehouses and finite inventory is faced with the problem of fulfilling orders that may contain multiple items. The platform's goal is to minimize the expected cost incurred from the fulfillment process, subject to warehouses' inventory constraints. Unlike the classical literature on multi-item fulfillment, we propose an alternative offline formulation of the problem. In particular, in our model, the platform sequentially selects methods to fulfill the arriving orders. A method consists of a set of facilities that will determine which warehouses the items will ship from and, more importantly, whether multi-item orders will be split. Under this formulation, we design a class of dynamic policies that combine ideas from randomized fulfillment, prophet inequalities and subgradient methods for the general multi-item fulfillment model. Specifically, by establishing connections between the fulfillment and prophet inequality literature, we prove that our algorithm is both asymptotically optimal and has strong approximation guarantees in non-asymptotic settings. Our result shows that there is a simple and near-optimal procedure for solving multi-item fulfillment problems once the online retailer has enough inventory, independently of other problem parameters. To the best of our knowledge, this is the first result of this type in the context of multi-item order fulfillment. In addition, and of independent interest, our analysis also leads to new asymptotically optimal bounds for network revenue management problems.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"63 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":"128871987","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":"Blockchain Mediated Persuasion","authors":"K. Drakopoulos, I-Chieh Lo, Justin A. Mulvany","doi":"10.1145/3580507.3597769","DOIUrl":"https://doi.org/10.1145/3580507.3597769","url":null,"abstract":"In the classic Bayesian Persuasion model studied by [Kamenica and Gentzkow, 2011], there are two players: the first, called Sender, wishes to persuade the second, called Receiver, to take a desired action. Provided that Sender is ex-post better informed about the underlying state of the world, Sender can leverage their informational advantage by communicating with Receiver via a signal mechanism. However, Sender's ability to effectively manage Receiver's beliefs largely hinges on the assumption that Sender can credibly commit to a signal mechanism. Usually, it is not ex-post optimal for Sender to follow the mechanism, but instead to deviate and send the message that generates the highest payoff. Consequently, Receiver may not have faith in Sender's ability to commit. In this case, all bets are off: persuasion devolves into cheap talk.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"15 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":"124082546","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":"Smoothed Analysis of Online Non-parametric Auctions","authors":"Naveen Durvasula, Nika Haghtalab, M. Zampetakis","doi":"10.1145/3580507.3597787","DOIUrl":"https://doi.org/10.1145/3580507.3597787","url":null,"abstract":"Online learning of revenue-optimal auctions is a fundamental problem in mechanism design without priors. Nevertheless, all the existing positive results assume that the auctioneer optimizes over a parameterized class of auctions, such as pricings and auctions with reserves. This is perhaps not surprising given that natural correlations that occur in online sequences pose a challenge to characterizing a succinct class of revenue-optimal auctions. This has left behind a significant gap in our understanding of online-learnability of general classes of non-parametric auctions. We provide the first positive results for online learnability of a non-parametric auction class, for smooth adversaries and the class of smooth auctions. In a nutshell, an online adversary is smooth (in the style of Smoothed analysis [Spielman and Teng, 2004] in online learning [Haghtalab et al., 2021]) if the bid distribution has bounded density at every time step, and an auction is smooth if the level sets of its revenue function have small boundaries. We prove the following fundamental guarantees: (1) Revenue maximization in the class of smooth auctions is online-learnable, against smooth adversaries. (2) It is impossible to construct a no-regret algorithm even for the class of smooth auctions against worst-case adversaries. (3) It is impossible to construct a no-regret algorithm for the class of all incentive-compatible auctions even against smooth adversaries. This gives a strong characterization of when and which class of non-parametric auctions are online-learnable. To illustrate the generality of the class of smooth auctions we show that it contains the class of all monotone-revenue auctions, as well as, the class of all competition-monotone auctions. This brings up an interesting observation: while independence across bids leads to the optimal auctions being monotone, significantly weaker assumptions, compared to monotonicity of revenue, are sufficient for learnability.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"7 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":"127909188","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":"Help and Haggle: Social Commerce Through Randomized, All-or-Nothing Discounts","authors":"Luyi Yang, Chen Jin, Zhen Shao","doi":"10.1145/3580507.3597790","DOIUrl":"https://doi.org/10.1145/3580507.3597790","url":null,"abstract":"This paper studies a novel social commerce practice known as \"help-and-haggle,\" whereby an online consumer can ask friends to help her \"haggle\" over the price of a product. Each time a friend agrees to help, the price is cut by a random amount, and if the consumer cuts the product price down to zero within a time limit, she will get the product for free; otherwise, the product reverts to the original price. Help-and-haggle enables the firm to promote its product and boost its social reach as consumers effectively refer their friends to the firm. We model the consumer's dynamic referral behavior in help-and-haggle and solve for the optimal price-cut distribution for the firm that trades off social reach, promotion expense, and product sales. Our results are as follows.","PeriodicalId":210555,"journal":{"name":"Proceedings of the 24th ACM Conference on Economics and Computation","volume":"36 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":"127518276","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":"Comparison of Screening Devices","authors":"M. Akbarpour, Piotr Dworczak, Frank Yang","doi":"10.2139/ssrn.4456198","DOIUrl":"https://doi.org/10.2139/ssrn.4456198","url":null,"abstract":"Public agencies are often tasked with allocating scarce resources (such as public housing or financial aid) to a target population. In many such cases, the goal is to maximize social welfare, which requires identifying agents who have the highest social value for the resource. The challenge is that while public agencies may be able to access some data about potential beneficiaries (for example, through means testing), they generally lack information necessary to achieve perfect targeting. When monetary transfers are unavailable or ineffective in targeting, public agencies often rely on \"ordeals\" instead. Natural examples include standing in line, filing out complicated forms, dealing with \"red tape,\" waiting, visiting an office at an inconvenient time, or traveling to a registration site. But what makes one costly screening device better than another?","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":"117310067","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}