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

{"title":"Data Tracking under Competition","authors":"K. Bimpikis, Ilan Morgenstern, D. Sabán","doi":"10.1145/3465456.3467582","DOIUrl":"https://doi.org/10.1145/3465456.3467582","url":null,"abstract":"We explore the welfare implications of data tracking technologies that enable firms to collect consumer data and potentially use it for price discrimination. The model we develop centers around two features: first, competition between firms and, second, consumers' level of sophistication. Our baseline environment features a firm that can collect information about the consumers it transacts with in a duopoly market, which it can then use in a second monopoly market. We characterize and compare the equilibrium outcomes in three settings of interest: (i) an economy with myopic consumers, who, when making purchase decisions, do not internalize the fact that firms have the ability to track their behavior and use this information in future transactions, (ii) an economy with forward-looking consumers, who take into account the implications of data tracking when determining their actions, and (iii) an economy where no data tracking technologies are used either due to technological or regulatory constraints. We find that the absence of data tracking may lead to a decrease in consumer surplus, even when consumers are myopic. Importantly, this result relies critically on competition: consumer surplus may be higher when data tracking technologies are used in the marketplace only when multiple firms offer substitutable products to consumers. Our results contribute to the debate of whether to regulate firms' use of data tracking technologies by illustrating that their effect on consumers depends not only on their level of sophistication, i.e., the extent to which they internalize how their data may be used, but also on the degree of competition in the market. Finally, in contrast to earlier work, we show that firms may have no incentive to self-regulate their use of consumer data even when consumers fully internalize and anticipate how their data may be used. The full paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3808228","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134062428","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":"Online Stochastic Max-Weight Bipartite Matching: Beyond Prophet Inequalities","authors":"C. Papadimitriou, Tristan Pollner, A. Saberi, David Wajc","doi":"10.1145/3465456.3467613","DOIUrl":"https://doi.org/10.1145/3465456.3467613","url":null,"abstract":"The rich literature on online Bayesian selection problems has long focused on so-called prophet inequalities, which compare the gain of an online algorithm to that of a \"prophet\" who knows the future. An equally-natural, though significantly less well-studied benchmark is the optimum online algorithm, which may be omnipotent (i.e., computationally-unbounded), but not omniscient. What is the computational complexity of the optimum online? How well can a polynomial-time algorithm approximate it? Motivated by applications in ride hailing, we study the above questions for the online stochastic maximum-weight matching problem under vertex arrivals. This problem was recently introduced by Ezra, Feldman, Gravin and Tang (EC'20), who gave a 1/2-competitive algorithm for it. This is the best possible ratio, as this problem is a generalization of the original single-item prophet inequality. We present a polynomial-time algorithm which approximates optimal online within a factor of 0.51---beating the best-possible prophet inequality. At the core of our result are a new linear program formulation, an algorithm that tries to match the arriving vertices in two attempts, and an analysis that bounds the correlation resulting from the second attempts. In contrast, we show that it is PSPACE-hard to approximate this problem within some constant α < 1.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134149639","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":"Learning to Persuade on the Fly: Robustness Against Ignorance","authors":"You Zu, Krishnamurthy Iyer, Haifeng Xu","doi":"10.1145/3465456.3467593","DOIUrl":"https://doi.org/10.1145/3465456.3467593","url":null,"abstract":"We study a repeated persuasion setting between a sender and a receiver, where at each time t, the sender shares information about a payoff-relevant state with the receiver. The state at each time t is drawn independently and identically from an unknown distribution, and subsequent to receiving information about it, the receiver (myopically) chooses an action from a finite set. The sender seeks to persuade the receiver into choosing actions that are aligned with her preference by selectively sharing information about the state. In contrast to the standard persuasion setting, we focus on the case where neither the sender nor the receiver knows the distribution of the payoff relevant state. Instead, the sender learns this distribution over time by observing the state realizations. We adopt the assumption common in the literature on Bayesian persuasion that at each time period, prior to observing the realized state in that period, the sender commits to a signaling mechanism that maps each state to a possibly random action recommendation. Subsequent to the state observation, the sender recommends an action as per the chosen signaling mechanism.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125370594","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 Approximately Optimal Search on Matching Platforms","authors":"Nicole Immorlica, Brendan Lucier, V. Manshadi, Alexander Wei","doi":"10.2139/ssrn.3850164","DOIUrl":"https://doi.org/10.2139/ssrn.3850164","url":null,"abstract":"We study the design of a decentralized two-sided matching market in which agents' search is guided by the platform. Each agent is of one of finitely many types and has (potentially random) preferences drawn from known type-specific distributions. Equipped with such distributional knowledge, the platform guides the search process by determining the meeting rate between each pair of types from the two sides. Meanwhile, agents strategically accept or reject the potential partners whom they meet. Focusing on when agents have symmetric pairwise preferences in a continuum model, we first characterize the unique stationary equilibrium that arises given a feasible set of meeting rates. We then introduce the platform's optimal directed search problem, which involves optimizing meeting rates to maximize equilibrium social welfare. We show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate. Nonetheless, we develop an efficiently computable solution whose corresponding equilibrium achieves at least 1/4 of the optimal social welfare. Our directed search design is simple and easy-to-implement, as its corresponding bipartite graph consists of disjoint stars. Furthermore, our solution implies that, with careful search design, the platform can substantially limit choice and yet induce an equilibrium with approximately optimal welfare. Finally, we show that approximation is likely the best we can hope for by establishing that the problem of designing optimal directed search is NP-hard to approximate beyond a certain constant factor.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132976926","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":"A Regret Analysis of Bilateral Trade","authors":"N. Cesa-Bianchi, T. Cesari, Roberto Colomboni, Federico Fusco, S. Leonardi","doi":"10.1145/3465456.3467645","DOIUrl":"https://doi.org/10.1145/3465456.3467645","url":null,"abstract":"Bilateral trade, a fundamental topic in economics, models the problem of intermediating between two strategic agents, a seller and a buyer, willing to trade a good for which they hold private valuations. Despite the simplicity of this problem, a classical result by Myerson and Satterthwaite (1983) affirms the impossibility of designing a mechanism that is simultaneously efficient, incentive compatible, individually rational, and budget balanced. This impossibility result fostered an intense investigation of meaningful trade-offs between these desired properties. Much work has focused on approximately efficient fixed-price mechanisms, e.g., Blumrosen and Dobzinski (2014, 2016), Colini-Baldeschi et al. (2016), which have been shown to fully characterize strong budget balanced and ex-post individually rational direct revelation mechanisms. All these results, however, either assume some knowledge on the priors of the seller/buyer valuations, or black-box access to some samples of the distributions, as in Dütting et al. (2021). In this paper, we cast for the first time the bilateral trade problem in a regret minimization framework over T rounds of seller/buyer interactions, with no prior knowledge on their private valuations. Our main contribution is a complete characterization of the regret regimes for fixed-price mechanisms with different feedback models and private valuations, using as a benchmark the best fixed-price in hindsight. More precisely, we prove the following bounds on the regret ~Θ (√T) for full-feedback (i.e., direct revelation mechanisms); ~Θ(T2/3) for realistic feedback (i.e., posted-price mechanisms) and independent seller/buyer valuations with bounded densities; Θ(T) for realistic feedback and seller/buyer valuations with bounded densities; Θ(T) for realistic feedback and independent seller/buyer valuations; Θ(T) for the adversarial setting.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122976786","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":"Efficient Competitions and Online Learning with Strategic Forecasters","authors":"Rafael M. Frongillo, Robert Gomez, Anish Thilagar, Bo Waggoner","doi":"10.1145/3465456.3467635","DOIUrl":"https://doi.org/10.1145/3465456.3467635","url":null,"abstract":"Winner-take-all competitions in forecasting and machine-learning suffer from distorted incentives. [23] identified this problem and proposed ELF, a truthful mechanism to select a winner. We show that, from a pool of n forecasters, ELF requires Θ(nłog n) events or test data points to select a near-optimal forecaster with high probability. We then show that standard online learning algorithms select an ε-optimal forecaster using only O(łog(n) / ε2) events, by way of a strong approximate-truthfulness guarantee. This bound matches the best possible even in the nonstrategic setting. We then apply these mechanisms to obtain the first no-regret guarantee for non-myopic strategic experts.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129507208","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":"On Interim Envy-Free Allocation Lotteries","authors":"I. Caragiannis, P. Kanellopoulos, M. Kyropoulou","doi":"10.1145/3465456.3467648","DOIUrl":"https://doi.org/10.1145/3465456.3467648","url":null,"abstract":"With very few exceptions, recent research in fair division has mostly focused on deterministic allocations. Deviating from this trend, we study the fairness notion of interim envy-freeness (iEF) for lotteries over allocations, which serves as a sweet spot between the too stringent notion of ex-post envy-freeness and the very weak notion of ex-ante envy-freeness. iEF is a natural generalization of envy-freeness to random allocations in the sense that a deterministic envy-free allocation is iEF (when viewed as a degenerate lottery). It is also certainly meaningful as it allows for a richer solution space, which includes solutions that are provably better than envy-freeness according to several criteria. Our analysis relates iEF to other fairness notions as well, and reveals tradeoffs between iEF and efficiency. Even though several of our results apply to general fair division problems, we are particularly interested in instances with equal numbers of agents and items where allocations are perfect matchings of the items to the agents. Envy-freeness can be trivially decided and (when it can be achieved, it) implies full efficiency in this setting. Although computing iEF allocations in matching allocation instances is considerably more challenging, we show how to compute them in polynomial time, while also maximizing several efficiency objectives. Our algorithms use the ellipsoid method for linear programming and efficient solutions to a novel variant of the bipartite matching problem as a separation oracle. We also study the extension of interim envy-freeness notion when payments to or from the agents are allowed. We present a series of results on two optimization problems, including a generalization of the classical rent division problem to random allocations using interim envy-freeness as the solution concept.","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128375780","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":"A Theory of Choice Bracketing under Risk","authors":"Mu Zhang","doi":"10.1145/3465456.3467603","DOIUrl":"https://doi.org/10.1145/3465456.3467603","url":null,"abstract":"Decision makers in the real world usually face multiple risky choice problems. For instance, an investor might need to take care of her investment accounts simultaneously in different financial markets, including stocks, bonds, and cryptocurrencies. One implicit assumption of the long-standing focus on single choice problems in economics is that agents can rationally aggregate and assess risks and consequences in multiple sources. However, aggregating risks from multiple sources can be complex and demanding, and decision makers usually adopt heuristics, such as narrow bracketing and correlation neglect, to simplify the decision process. Narrow bracketing describes the situation where a decision maker faced with multiple choice problems tends to choose an option in each decision without full regard to other decisions. Correlation neglect means that the decision maker might ignore the interdependence of risks from different sources. Both heuristics are well-documented in experiments and behavioral economics, but have received inadequate attention in the choice-theoretic literature. One possible reason is that they are typically interpreted as exotic behavioral or \"irrational\" biases and supposed to deviate drastically from the standard framework. This paper axiomatically characterizes the two heuristics by introducing an intuitive relaxation of the independence axiom in the standard von Neumann-Morgenstern expected utility framework. The representation theorem allows for either narrow bracketing, or correlation neglect, or both of them, or none of them. This behavioral foundation suggests that the two heuristics are no more \"irrational'' than other commonly accepted non-EU theories like certainty effect and reference dependence, and hence deserve more attention in the future. The flexibility of our framework allows for applications in various choice domains. First, my model can accommodate the experimental evidence on narrow bracketing, where subjects tend to violate first-order stochastic dominance when making choices in simultaneous and independent monetary gambles. Second, in environments with background risk, I show how the model can explain non-trivial risk aversion over small gambles. Finally, in intertemporal choices, I show that my framework unifies three commonly used and seemingly distinct models of time preferences in the literature. This is ex-ante surprising since the analysis is based solely on simplifying heuristics to deal with multi-source risk and contains no normative properties required in each of the three models. Then, I propose a novel class of time preferences that can simultaneously satisfy indifference to time resolution of uncertainty, dynamic consistency and separation of time and risk preferences. This provides an alternative solution to the impossibility result in [3]. One specification of my new model shares the same predictions and explanatory power as [1] in macroeconomics and finance applications, without being sub","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127332517","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":"Tight Revenue Gaps among Multi-Unit Mechanisms","authors":"Yaonan Jin, Shunhua Jiang, P. Lu, Hengjie Zhang","doi":"10.1145/3465456.3467621","DOIUrl":"https://doi.org/10.1145/3465456.3467621","url":null,"abstract":"This paper considers Bayesian revenue maximization in the k-unit setting, where a monopolist seller has k copies of an indivisible item and faces n unit-demand buyers (whose value distributions can be non-identical). Four basic mechanisms among others have been widely employed in practice and widely studied in the literature: Myerson Auction, Sequential Posted-Pricing, (k + 1)-th Price Auction with Anonymous Reserve, and Anonymous Pricing. Regarding a pair of mechanisms, we investigate the largest possible ratio between the two revenues (a.k.a. the revenue gap), over all possible value distributions of the buyers. Divide these four mechanisms into two groups: (i) the discriminating mechanism group, Myerson Auction and Sequential Posted-Pricing, and (ii) the anonymous mechanism group, Anonymous Reserve and Anonymous Pricing. Within one group, the involved two mechanisms have an asymptotically tight revenue gap of 1 + Θ(1 / √k). In contrast, any two mechanisms from the different groups have an asymptotically tight revenue gap of Θ(łog k).","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114475642","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":"From Proper Scoring Rules to Max-Min Optimal Forecast Aggregation","authors":"Eric Neyman, T. Roughgarden","doi":"10.1287/opre.2022.2414","DOIUrl":"https://doi.org/10.1287/opre.2022.2414","url":null,"abstract":"This paper forges a strong connection between two seemingly unrelated forecasting problems: incentive-compatible forecast elicitation and forecast aggregation. Proper scoring rules are the well-known solution to the former problem. To each such rule s we associate a corresponding method of aggregation, mapping expert forecasts and expert weights to a \"consensus forecast,\" which we call quasi-arithmetic (QA) pooling with respect to s. We justify this correspondence in several ways: QA pooling with respect to the two most well-studied scoring rules (quadratic and logarithmic) corresponds to the two most well-studied forecast aggregation methods (linear and logarithmic). Given a scoring rule s used for payment, a forecaster agent who sub-contracts several experts, paying them in proportion to their weights, is best off aggregating the experts' reports using QA pooling with respect to s, meaning this strategy maximizes its worst-case profit (over the possible outcomes). The score of an aggregator who uses QA pooling is concave in the experts' weights. As a consequence, online gradient descent can be used to learn appropriate expert weights from repeated experiments with low regret. The class of all QA pooling methods is characterized by a natural set of axioms (generalizing classical work by Kolmogorov on quasi-arithmetic means).","PeriodicalId":395676,"journal":{"name":"Proceedings of the 22nd ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127458207","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}