Proceedings of the 23rd ACM Conference on Economics and Computation最新文献

{"title":"Robust Data-Driven Decisions Under Model Uncertainty","authors":"Xiaoyu Cheng","doi":"10.1145/3490486.3538356","DOIUrl":"https://doi.org/10.1145/3490486.3538356","url":null,"abstract":"In data-driven decisions, while the extrapolation from sample data is often based on observable similarities within a population, one also needs to take into account possible unobserved heterogeneity across individuals in the population. Because of the unobservability, a decision-maker (DM) can be uncertain about not only how the individuals might vary but also how different individuals are going to be sampled from the population. As a result, she might worry about the possibility that the sample data are more from one type of individual, whereas future draws that determine the payoff of decisions may be more of a different type. This paper captures this concern by considering a decision environment where the underlying data-generating process (DGP) is a sequence of independent but possibly non-identical distributions. Specifically, the DM observes sample data given by realizations of marginal distributions of a DGP and then makes a decision whose payoff depends only on future realizations of the same DGP. In addition, the DM facesmodel uncertainty in the form ofambiguity, i.e., she only knows there is a set of possible DGPs but cannot form any probabilistic assessment over them. For making decisions, I suppose the DM applies the maxmin expected-utility (MEU) criterion [1] to cope with ambiguity. That is, she makes an optimal decision under the worst possible DGP that she contemplates. The DM can either make adata-free decision based on her initial belief, or adata-driven decision based on her updated belief taking into account the sample data. Given these two types of decisions, I study updating rules in terms of how to guarantee the data-driven decisions to be better than the data-free decisions according toobjective payoff, i.e., the expected utility under thetrue DGP that governs the future uncertainty. In other words, while the DM makes decisions considering the worst case, the quality of her decisions will be evaluated against the ground truth. When an updating rule can guarantee improvement for all possible DGPs in the initial belief, the data-driven decisions are said to robustly improve upon the data-free decisions. In this paper, I formalize two achievable notions of how data-driven decisions can robustly improve upon data-free decisions across decision problems. I show that these two notions are both equivalent to the intuitive requirement that the updated set of DGPs should accommodate (i.e., contain with some technical generalization) the true DGP that generates the data (Theorem 4.2, 4.6, Corollary 4.7). Based on this equivalence, I further study updating rules in terms of this property. In Section 2 of the paper, I make a critical observation that in the presence of independent but non-identical distributions, common updating rules such as maximum likelihood and Bayesian updating, can almost surely rule out the true DGP. Thus, implied by the previous result, they can almost surely lead to strictly worse decisions than simply ignori","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117121579","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":"Credible Persuasion","authors":"Xiao Lin, Ce Liu","doi":"10.1145/3490486.3538264","DOIUrl":"https://doi.org/10.1145/3490486.3538264","url":null,"abstract":"We propose a new notion of credibility for Bayesian persuasion problems. A disclosure policy is credible if the Sender cannot profit from tampering with her messages while keeping the message distribution unchanged. Using optimal transport theory, we show that for every profile of Sender's disclosure policy and Receiver's strategy, the credibility of the profile is equivalent to a cyclical monotonicity condition on its induced distribution over states and actions. We provide conditions on when credibility considerations completely shut down informative communication, as well as settings where the Sender is guaranteed to benefit from credible persuasion. We apply our results to a classic setting of asymmetric information-the market for lemons. It is well-known that market outcome in such a setting may be inefficient due to adverse selection (Akerlof, 1970): despite common knowledge of gain from trade, some cars may not be traded. If the seller can commit to a disclosure policy to persuade the buyers, she can completely solve the market inefficiency by perfectly revealing 0 to the buyers. However, we show that if the buyers can only observe the message distribution of the seller's disclosure policy, but not exactly how these messages are generated, then the seller cannot credibly disclose any useful information to the buyer. Our paper offers foundations for studying Bayesian persuasion in a number of settings. One example is when the Sender's payoff is state-independent: in these cases, our results imply that all disclosure policies are credible, so the full-commitment assumption in the Bayesian persuasion approach is nonessential as long as the message distribution is observable. Another example is when the Sender's payoff is supermodular, in which case all monotone disclosure policies are credible.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129677278","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":"Estimation of Standard Auction Models","authors":"Yeshwanth Cherapanamjeri, C. Daskalakis, Andrew Ilyas, M. Zampetakis","doi":"10.1145/3490486.3538284","DOIUrl":"https://doi.org/10.1145/3490486.3538284","url":null,"abstract":"We provide efficient estimation methods for first- and second-price auctions under independent (asymmetric) private values and partial observability. Given a finite set of observations, each comprising the identity of the winner and the price they paid in a sequence of identical auctions, we provide algorithms for non-parametrically estimating the bid distribution of each bidder, as well as their value distributions under equilibrium assumptions. We provide finite-sample estimation bounds which are uniform in that their error rates do not depend on the bid/value distributions being estimated. Our estimation guarantees advance a body of work in Econometrics wherein only identification results have been obtained, unless the setting is symmetric, parametric, or all bids are observable. Our guarantees also provide computationally and statistically effective alternatives to classical techniques from reliability theory. Finally, our results are immediately applicable to Dutch and English auctions.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115099588","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 the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design","authors":"Jerry Anunrojwong, S. Balseiro, Omar Besbes","doi":"10.1145/3490486.3538324","DOIUrl":"https://doi.org/10.1145/3490486.3538324","url":null,"abstract":"Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: the seller is selling an indivisible item to n buyers such that the buyers' valuations are drawn from a joint distribution that is unknown to both the buyers and the seller; buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class. We measure performance through the worst-caseregret, or the difference between the expected revenue achievable with perfect knowledge of buyers' valuations and the actual mechanism revenue. We study a broad set of classes of valuation distributions that capture a wide spectrum of possible dependencies: independent and identically distributed (i.i.d.) distributions, mixtures of i.i.d. distributions, affiliated and exchangeable distributions, exchangeable distributions, and all joint distributions. We derive in quasi closed form the minimax values and the associated optimal mechanism. In particular, we show that the first three classes admit the same minimax regret value, which is decreasing with the number of competitors, while the last two have the same minimax regret equal to that of the case n = 1. Furthermore, we show that the minimax optimal mechanisms have a simple form across all settings: asecond-price auction with random reserve prices, which shows its robustness in prior-independent mechanism design. En route to our results, we also develop a principled methodology to determine the form of the optimal mechanism and worst-case distribution via first-order conditions that should be of independent interest in other minimax problems. The full paper is available at https://arxiv.org/abs/2204.10478 and https://ssrn.com/abstract=4090071.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125253388","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 Routing for Constant Function Market Makers","authors":"Guillermo Angeris, Tarun Chitra, A. Evans, Stephen P. Boyd","doi":"10.1145/3490486.3538336","DOIUrl":"https://doi.org/10.1145/3490486.3538336","url":null,"abstract":"We consider the problem of optimally executing an order involving multiple crypto-assets, sometimes called tokens, on a network of multiple constant function market makers (CFMMs). When we ignore the fixed cost associated with executing an order on a CFMM, this optimal routing problem can be cast as a convex optimization problem, which is computationally tractable. When we include the fixed costs, the optimal routing problem is a mixed-integer convex problem, which can be solved using (sometimes slow) global optimization methods, or approximately solved using various heuristics based on convex optimization. The optimal routing problem includes as a special case the problem of identifying an arbitrage present in a network of CFMMs, or certifying that none exists.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123980525","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":"Contracts with Information Acquisition, via Scoring Rules","authors":"Maneesha Papireddygari, Bo Waggoner","doi":"10.1145/3490486.3538261","DOIUrl":"https://doi.org/10.1145/3490486.3538261","url":null,"abstract":"This paper considers a principal-agent problem of delegation that features two types of information asymmetry. A principal delegates a task to the agent; the agent can first choose to acquire a costly signal, then takes an action. The signal is relevant to the final outcome and the best course of action. Both of these decisions are hidden from the principal, who only observes a final outcome -- a noisy function of both information and action. We call this problem Contracts with Information Acquisition. To incent the agent, the principal offers a menu of contracts, each a function mapping the observed outcome to the agent's payment. The agent selects a contract after the information acquisition phase. The principal designs the menu in order to incentivize this hidden acquisition as well as to incentivize the desired choice of hidden action conditioned on the information acquired. We impose the important assumption of limited liability from economics literature, which states that the agent must never be required to make a payment to the principal under any state of the world. Limited liability plays the role of risk aversion, without which the problem becomes trivial. A plan consists of 1) a decision to acquire information or not and 2) a mapping that specifies, for each signal realization, the action that the principal wants the agent to choose. Given that after the information acquisition phase, the agent has better understanding of the outcome, the principal potentially wants a different action performed for each signal realization. This can be encapsulated by the plan that the principal specifies. Hence we consider the minimum payment problem, which asks what the minimal expected payment is to incent the agent to follow a specific plan. As a motivating example, consider a company (principal) designing a contract for a marketing firm (agent). The marketing firm can choose to conduct a costly survey to gain more information regarding customer preferences. Then, regardless of whether they acquired survey information or not, it will run a marketing campaign. The choice of campaign design and effort level may be influenced by the survey results. The principal cannot directly observe whether the agent acquired survey information nor how much effort they expended, but can observe the final outcome, e.g. sales numbers. We show that our general problem reduces without loss of generality to the design of a proper scoring rule: a function s(p, ω) that assigns a score to prediction p when the true outcome turns out to be ω. It is interesting that although proper scoring rules are designed for settings in which agents acquire no new information and take no action, they are \"complete\" for a problem that involves both. Similar observations have been previously noted, especially in the information acquisition literature (see below). We use this observation to frame our approach in the following results. We first consider two subcases of our general setting bef","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128320541","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-Augmented Mechanism Design: Leveraging Predictions for Facility Location","authors":"Priyank Agrawal, Eric Balkanski, Vasilis Gkatzelis, Ting-Chieh Ou, Xizhi Tan","doi":"10.1145/3490486.3538306","DOIUrl":"https://doi.org/10.1145/3490486.3538306","url":null,"abstract":"In this work we introduce an alternative model for the design and analysis of strategyproof mechanisms that is motivated by the recent surge of work in \"learning-augmented algorithms\". Aiming to complement the traditional approach in computer science, which analyzes the performance of algorithms based on worst-case instances, this line of work has focused on the design and analysis of algorithms that are enhanced with machine-learned predictions regarding the optimal solution. The algorithms can use the predictions as a guide to inform their decisions, and the goal is to achieve much stronger performance guarantees when these predictions are accurate (consistency), while also maintaining near-optimal worst-case guarantees, even if these predictions are very inaccurate (robustness). So far, these results have been limited to algorithms, but in this work we argue that another fertile ground for this framework is in mechanism design. We initiate the design and analysis of strategyproof mechanisms that are augmented with predictions regarding the private information of the participating agents. To exhibit the important benefits of this approach, we revisit the canonical problem of facility location with strategic agents in the two-dimensional Euclidean space. We study both the egalitarian and utilitarian social cost functions, and we propose new strategyproof mechanisms that leverage predictions to guarantee an optimal trade-off between consistency and robustness guarantees. This provides the designer with a menu of mechanism options to choose from, depending on her confidence regarding the prediction accuracy. Furthermore, we also prove parameterized approximation results as a function of the prediction error, showing that our mechanisms perform well even when the predictions are not fully accurate.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114844577","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 Algorithms for Matching Platforms with Multi-Channel Traffic","authors":"Vahideh H. Manshadi, Scott Rodilitz, D. Sabán, Akshaya Suresh","doi":"10.1145/3490486.3538326","DOIUrl":"https://doi.org/10.1145/3490486.3538326","url":null,"abstract":"Two-sided platforms rely on their recommendation algorithms to help their visitors successfully find a match. However, on platforms such as VolunteerMatch - which has facilitated tens of millions of connections between volunteers and nonprofits - a sizable fraction of website traffic arrives directly to a nonprofit's volunteering page via an external link, thus bypassing the platform's recommendation algorithm. We study how such platforms should account for this external traffic in the design of their recommendation engines, given the goal of maximizing the total number of successful matches. We model the platform's problem as a special case of online matching with stochastic rewards, where (using VolunteerMatch as a motivating example) volunteers arrive sequentially and (probabilistically) match with one opportunity, each of which has finite need for volunteers. In our framework, external traffic is interested only in their targeted opportunity; in contrast, internal traffic may be interested in many opportunities, and the platform's online algorithm selects which opportunity to recommend. In evaluating the performance of different algorithms, we take a worst-case analysis approach, yet we refine the notion of the competitive ratio by parameterizing it based on the amount of external traffic. After demonstrating the shortcomings of a commonly-used algorithm which is optimal in the absence of external traffic, we introduce a new algorithm - Adaptive Capacity (AC) - which accounts for matches differently based on whether they originate from internal or external traffic. We establish a lower bound on AC's competitive ratio that is increasing in the amount of external traffic, and we compare our lower bound to a parameterized upper bound on the competitive ratio of any online algorithm. We find that (in certain parameter regimes) AC is near-optimal regardless of the amount of external traffic, even though it does not know this amount a priori. Our analysis utilizes a path-based, pseudo-rewards approach, which we further generalize to settings where the platform can recommend a ranked set of opportunities. Beyond our theoretical results, we demonstrate the strong performance of AC in a case study motivated by VolunteerMatch data.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123290739","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 Correlated Equilibria in General-Sum Extensive-Form Games: Fixed-Parameter Algorithms, Hardness, and Two-Sided Column-Generation","authors":"B. Zhang, Gabriele Farina, A. Celli, T. Sandholm","doi":"10.1145/3490486.3538330","DOIUrl":"https://doi.org/10.1145/3490486.3538330","url":null,"abstract":"We study the problem of finding optimal correlated equilibria of various sorts: normal-form coarse correlated equilibrium (NFCCE), extensive-form coarse correlated equilibrium (EFCCE), and extensive-form correlated equilibrium (EFCE). This is NP-hard in the general case and has been studied in special cases, most notably triangle-free games[2], which include all two-player games with public chance moves. However, the general case is not well understood, and algorithms usually scale poorly. In this paper, we make two primary contributions. First, we introduce the correlation DAG, a representation of the space of correlated strategies whose structure and size are dependent on the specific solution concept desired. It extends the team belief DAG of Zhang et al. [3] to general-sum games. For each of the three solution concepts, its size depends exponentially only on a parameter related to the information structure of the game. We also prove a fundamental complexity gap: while our size bounds for NFCCE are similar to those achieved in the case of team games by Zhang et al. [3], this is impossible to achieve for the other two concepts under standard complexity assumptions. Second, we propose a two-sided column generation approach to compute optimal correlated strategies in extensive-form games. Our algorithm improves upon the one-sided approach of Farina et al. [1] by means of a new decomposition of correlated strategies which allows players to re-optimize their sequence-form strategies with respect to correlation plans which were previously added to the support. Experiments show that our techniques outperform the prior state of the art for computing optimal general-sum correlated equilibria, and that our two families of approaches have complementary strengths: the correlation DAG is fast when the parameter is small and the two-sided column generation approach is superior when the parameter is large. For team games, we show that the two-sided column generation approach vastly outperforms standard column generation approaches, making it the state of the art algorithm when the parameter is large. Along the way, we also introduce two new benchmark games: a trick-taking game that emulates the endgame phase of the card game bridge, and a ride-sharing game, where two drivers traversing a graph are competing to reach specific nodes and serve requests. The full version is available at: https://arxiv.org/abs/2203.07181","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115427187","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":"Descending Price Auctions with Bounded Number of Price Levels and Batched Prophet Inequality","authors":"S. Alaei, A. Makhdoumi, Azarakhsh Malekian, Rad Niazadeh","doi":"10.48550/arXiv.2203.01384","DOIUrl":"https://doi.org/10.48550/arXiv.2203.01384","url":null,"abstract":"We consider descending price auctions for selling m units of a good to unit demand i.i.d. buyers where there is an exogenous bound of k on the number of price levels the auction clock can take. The auctioneer's problem is to choose price levels p1 > p2 > ․․․ > pk for the auction clock such that auction expected revenue is maximized. The price levels are announced prior to the auction. We reduce this problem to a new variant of prophet inequality, which we call batched prophet inequality, where a decision-maker chooses k (decreasing) thresholds and then sequentially collects rewards (up to m) that are above the thresholds with ties broken uniformly at random. For the special case of m=1 (i.e., selling a single item), we show that the resulting descending auction with k price levels achieves 1- 1/ek of the unrestricted (without the bound of k) optimal revenue. That means a descending auction with just 4 price levels can achieve more than 98% of the optimal revenue. We then extend our results for m>1 and provide a closed-form bound on the competitive ratio of our auction as a function of the number of units m and the number of price levels k. The full paper is available at: https://arxiv.org/abs/2203.01384","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130754280","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}