Proceedings of the 22nd ACM Conference on Economics and Computation最新文献

{"title":"Quality Disclosures and Disappointment: Evidence from the Academy Awards","authors":"M. Rossi","doi":"10.1145/3465456.3467573","DOIUrl":"https://doi.org/10.1145/3465456.3467573","url":null,"abstract":"I study the impact of quality disclosures on buyers' ratings using data from an online recommender system. Disclosures may alter expectations on sellers' quality and affect buyers' rating behavior. In particular, if buyers' utility depends on their expectations, a positive disclosure of quality such as an award may lead to buyers' disappointment, negatively influencing their ratings. I identify the disappointment effect in moviegoers' ratings originated from the rise in expectations due to movies' nominations for the Academy of Motion Picture Arts and Sciences awards. I control for the selection of moviegoers who watch and rate movies before or after nominations with a non-parametric matching technique. After nominations, ratings for nominated movies significantly drop relative to ratings for movies that were not nominated. This short-term disappointment effect reduces the rating premium of nominated movies by more than five percent.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115572418","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":"Disentangling Exploration from Exploitation","authors":"Leeat Yariv","doi":"10.1145/3465456.3467524","DOIUrl":"https://doi.org/10.1145/3465456.3467524","url":null,"abstract":"A key tension in the study of experimentation revolves around the exploration of new possibilities and the exploitation of prior discoveries. Starting from Robbins (1952), a large literature in economics and statistics has married the two: Agents experiment by selecting potentially risky options and observing their resulting payoffs. This framework has been used in many applications, ranging from pricing decisions to labor market search. Nonetheless, in many applications, agents' exploration and exploitation need not be intertwined. An investor may study stocks she is not invested in, an employee may explore alternative jobs while working, etc. The current paper focuses on the consequences of disentangling exploration from exploitation. This talk will cover some insights generated from work joint with Alessandro Lizzeri (Princeton University) and Eran Shmaya (Stony Brook University). We consider the classical Poisson bandit problem that has served as the canonical model for experimentation. We fully characterize the solution when exploration and exploitation are disentangled, both for the \"good news\" and \"bad news\" settings. We illustrate the stark differences the optimal exploration policy exhibits compared to the standard setting. In particular, we show that agents optimally utilize the option to observe projects different than the ones they act on. In the good news case, the optimal policy entails the continued exploration of a singular arm-no matter how pessimistic the decision-maker becomes about that arm-until news arrives. In contrast, in the bad news, exploration can involve the use of more than a single arm, but entails at most one switch. In all settings, the separation of exploration from exploitation guarantees asymptotic efficiency. BIO: Leeat Yariv is the Uwe E. Reinhardt Professor of Economics at Princeton University. She is also the director of the Princeton Experimental Laboratory for the Social Sciences (PExL), which she opened. She is the lead editor of AEJ: Micro and has served on the editorial boards of multiple journals. She is a member of the American Academy of Arts and Sciences and a fellow of the Econometric Society and the Society for the Advancement of Economic Theory. She is also a research associate of the National Bureau of Economic Research (NBER) and a research fellow of the Center for Economic and Policy Research (CEPR). Yariv's work focuses on market design, social networks, and political economy. She uses theory, lab experiments, and field studies to understand how individuals connect to one another and how they make decisions, on their own and collectively.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128122558","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":"Processing Reserves Simultaneously","authors":"David Delacrétaz","doi":"10.1145/3465456.3467544","DOIUrl":"https://doi.org/10.1145/3465456.3467544","url":null,"abstract":"Policymakers frequently use reserve categories to combine competing objectives in allocating scarce resources based on priority. For example, schools may prioritize students from underprivileged backgrounds for some of their seats while allocating the rest of them based solely on academic merit. The order in which different categories are processed has been shown to have an important, yet subtle impact on allocative outcomes---and has led to unintended consequences in practice. I introduce a new, more transparent way of processing reserves, which handles all categories simultaneously. I provide an axiomatic characterization of my solution, showing that it satisfies basic desiderata as well as category neutrality : if an agent qualifies for n categories, she takes $1/n$ units from each of them. A practical advantage of this approach is that the relative importance of categories is entirely captured by their quotas.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123968275","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":"Revenue Maximization from Finite Samples","authors":"Amine Allouah, Achraf Bahamou, Omar Besbes","doi":"10.1145/3465456.3467572","DOIUrl":"https://doi.org/10.1145/3465456.3467572","url":null,"abstract":"In the present paper, we study the following fundamental problem: how should a decision-maker price based on a finite and limited number of samples from the distribution of values of customers. The decision-maker's objective is to select a pricing policy with maximum competitive ratio when the value distribution is only known to belong to some general non-parametric class. We study achievable performance for two central classes, regular and monotone hazard rate (mhr) distributions, through a general framework. To date, only results are available for a single sample and two samples. We improve existing results but also obtain the first results on achievable performance as the number of samples increases. At a higher level, this work also provides insights on the value of samples for pricing purposes. For example, against mhr distributions (resp. regular), two samples suffice to ensure 71% (resp. 61%) of optimal oracle performance, and ten samples guarantee $80%$ (resp. $65%$) of such performance. Our analysis relies on the introduction of a new (simple) class of policies and the derivation of tractable lower bounds on their performance through factor revealing dynamic programs.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121281067","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":"Designing a Combinatorial Financial Options Market","authors":"Xintong Wang, David M. Pennock, Nikhil R. Devanur, David M. Rothschild, Biaoshuai Tao, Michael P. Wellman","doi":"10.1145/3465456.3467634","DOIUrl":"https://doi.org/10.1145/3465456.3467634","url":null,"abstract":"Financial options are contracts that specify the right to buy or sell an underlying asset at a strike price by an expiration date. Standard exchanges offer options of predetermined strike values and trade options of different strikes independently, even for those written on the same underlying asset. Such independent market design can introduce arbitrage opportunities and lead to the thin market problem. The paper first proposes a mechanism that consolidates and matches orders on standard options related to the same underlying asset, while providing agents the flexibility to specify any custom strike value. The mechanism generalizes the classic double auction, runs in time polynomial to the number of orders, and poses no risk to the exchange, regardless of the value of the underlying asset at expiration. Empirical analysis on real-market options data shows that the mechanism can find new matches for options of different strike prices and reduce bid-ask spreads. Extending standard options written on a single asset, we propose and define a new derivative instrument ---combinatorial financial options that offer contract holders the right to buy or sell any linear combination of multiple underlying assets. We generalize our single-asset mechanism to match options written on different combinations of assets, and prove that optimal clearing of combinatorial financial options is coNP-hard. To facilitate market operations, we propose an algorithm that finds the exact optimal match through iterative constraint generation, and evaluate its performance on synthetically generated combinatorial options markets of different scales. As option prices reveal the market's collective belief of an underlying asset's future value, a combinatorial options market enables the expression of aggregate belief about future correlations among assets.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133658002","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 Repeated First Price Auctions","authors":"Shipra Agrawal, Eric Balkanski, V. Mirrokni, Balasubramanian Sivan","doi":"10.1145/3465456.3467590","DOIUrl":"https://doi.org/10.1145/3465456.3467590","url":null,"abstract":"We study dynamic mechanisms for optimizing revenue in repeated auctions, that are robust to heterogeneous forward-looking and learning behavior of the buyers. Typically it is assumed that the buyers are either all myopic or are all infinite lookahead, and that buyers understand and trust the mechanism. These assumptions raise the following question: is it possible to design approximately revenue optimal mechanisms when the buyer pool is heterogeneous? We provide this fresh perspective on the problem by considering a heterogeneous population of buyers with an unknown mixture of k-lookahead buyers, myopic buyers, no-regret-learners and no-policy-regret learners. Facing this population, we design a simple state-based mechanism that achieves a constant fraction of the optimal achievable revenue.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123356136","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-Rank Smart Reserves","authors":"H. Aziz, Zhaohong Sun","doi":"10.1145/3465456.3467619","DOIUrl":"https://doi.org/10.1145/3465456.3467619","url":null,"abstract":"We study the school choice problem where each school has flexible multi-ranked diversity goals, and each student may belong to multiple overlapping types, and consumes only one of the positions reserved for their types. We propose a novel choice function and show that it is the unique rule that satisfies three fundamental properties: maximal diversity, non-wastefulness, and justified envy-freeness. We provide a fast polynomial-time algorithm for our choice function that is based on the Dulmage Mendelsohn Decomposition Theorem as well as new insights into the combinatorial structure of constrained rank maximal matchings. Even for the case of minimum and maximum quotas for types (that capture two ranks), ours is the first known polynomial-time approach to compute an optimally diverse choice outcome. Finally, we prove that the choice function we design for schools, satisfies substitutability and hence can be directly embedded in the generalized deferred acceptance algorithm to achieve strategyproofness and stability. Our algorithms and results have immediate policy implications and directly apply to a variety of scenarios, such as where hiring positions or scarce medical resources need to be allocated while taking into account diversity concerns or ethical principles.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129540254","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 Feedback in Contests","authors":"Jeffrey C. Ely, G. Georgiadis, S. Khorasani, Luis Rayo","doi":"10.1145/3465456.3467532","DOIUrl":"https://doi.org/10.1145/3465456.3467532","url":null,"abstract":"We derive an optimal dynamic contest for environments where effort can be monitored only through a coarse, binary performance measure and the principal chooses prize-allocation and termination rules together with a real-time feedback policy for the contestants. The optimal contest takes a stark cyclical form: contestants are kept fully apprised of their own successes, and at the end of each fixed-length cycle, if at least one agent has succeeded, the contest ends and the prize is shared equally among all successful agents irrespective of when they succeeded; otherwise, the designer informs all contestants that nobody has yet succeeded and the contest resets. Applications include promotions, innovation contests, and proof-of-work protocols.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114224201","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 Role of Accuracy in Algorithmic Process Fairness Across Multiple Domains","authors":"M. Albach, J. R. Wright","doi":"10.1145/3465456.3467620","DOIUrl":"https://doi.org/10.1145/3465456.3467620","url":null,"abstract":"Machine learning is often used to aid in human decision-making, sometimes for life-altering decisions like when determining whether or not to grant bail to a defendant or a loan to an applicant. Because of their importance, it is critical to ensure that the processes used to reach these decisions are considered fair. A common approach is to enforce some fairness constraint over the outcomes of a decision maker, but there is no single, generally-accepted definition of fairness. With notable exceptions, most of the literature on algorithmic fairness takes for granted that there will be an inherent trade-off between accuracy and algorithmic fairness. Additionally, most work focuses only on one or two domains, whereas machine learning techniques are used in an increasing number of distinct decision-making contexts with differing pertinent features. In this work, we consider six different decision-making domains: bail, child protective services, hospital resources, insurance rates, loans, and unemployment aid. We focus on the fairness of the process directly, rather than the outcomes. We also take a descriptive approach, using survey data to elicit the factors that lead a decision-making process to be perceived as fair. Specifically, we ask 2157 Amazon Mechanical Turk workers to rate the features used for algorithmic decision-making in one of the six domains as either fair or unfair, as well as to rate how much they agree or disagree with the assignments of eight previously (and one newly) proposed properties to the features. For example, a worker could be asked to rate the feature of \"criminal history\" as fair or unfair to use in bail decisions, and then rate how much they agree or disagree that \"criminal history\" is a reliable feature. We show that, in every domain, disagreements in fairness judgements can be largely explained by the assignments of properties (like reliability) to features (like criminal history). We also show that fairness judgements can be well predicted across domains by training the predictor using the property assignments from one domain's data and predicting in another. These findings imply that the properties act as moral determinants for fairness judgements, and that respondents reason similarly about the implications of the properties in all the decision-making domains that we consider. Although our results are mostly consistent across domains, we find some important differences within specific demographic groups in the hospital and insurance domains, indicating that at least some differences in fairness judgements are introduced by demographic differences. However, a single property usually holds the majority of the predictive power. With some exceptions, predictors learning from only the \"increases accuracy\" property perform better (in all domains) than predictors learning from any combination of the other seven properties, implying that the primary factor affecting respondents' perceptions of the fairness of using a feat","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116819499","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":"Distribution Rules Under Dichotomous Preferences: Two Out of Three Ain't Bad","authors":"F. Brandt, Dominik Peters, C. Stricker","doi":"10.1145/3465456.3467653","DOIUrl":"https://doi.org/10.1145/3465456.3467653","url":null,"abstract":"We consider a setting in which agents contribute amounts of a divisible resource (such as money or time) to a common pool, which is used to finance projects of public interest. How the collected resources are to be distributed among the projects is decided by a distribution rule that takes as input a set of approved projects for each agent. An important application of this setting is donor coordination, which allows philanthropists to find an efficient and mutually agreeable distribution of their donations. We analyze various distribution rules (including the Nash product rule and the conditional utilitarian rule) in terms of classic as well as new axioms, and propose the first fair distribution rule that satisfies efficiency and monotonicity. Our main result settles a long-standing open question of Bogomolnaia, Moulin, and Stong (2005) by showing that no strategyproof and efficient rule can guarantee that at least one approved project of each agent receives a positive amount of the resource. The proof reasons about 386 preference profiles and was obtained using a computer-aided method involving SAT solvers.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129478452","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}