Proceedings of the 2019 ACM Conference on Economics and Computation最新文献_第10页

Simple versus Optimal Contracts 简单契约与最优契约

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-08-10 DOI: 10.1145/3328526.3329591

Paul Dütting, T. Roughgarden, Inbal Talgam-Cohen

{"title":"Simple versus Optimal Contracts","authors":"Paul Dütting, T. Roughgarden, Inbal Talgam-Cohen","doi":"10.1145/3328526.3329591","DOIUrl":"https://doi.org/10.1145/3328526.3329591","url":null,"abstract":"We consider the classic principal-agent model of contract theory, in which a principal designs an outcome-dependent compensation scheme to incentivize an agent to take a costly and unobservable action. When all of the model parameters---including the full distribution over principal rewards resulting from each agent action---are known to the designer, an optimal contract can in principle be computed by linear programming. In addition to their demanding informational requirements, however, such optimal contracts are often complex and unintuitive, and do not resemble contracts used in practice. This paper examines contract theory through the theoretical computer science lens, with the goal of developing novel theory to explain and justify the prevalence of relatively simple contracts, such as linear (pure commission) contracts. First, we consider the case where the principal knows only the first moment of each action's reward distribution, and we prove that linear contracts are guaranteed to be worst-case optimal, ranging over all reward distributions consistent with the given moments. Second, we study linear contracts from a worst-case approximation perspective, and prove several tight parameterized approximation bounds.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127759298","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}

引用次数: 56

Nearly Optimal Pricing Algorithms for Production Constrained and Laminar Bayesian Selection 生产约束与层流贝叶斯选择的近最优定价算法

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-07-15 DOI: 10.1145/3328526.3329652

Nima Anari, Rad Niazadeh, A. Saberi, A. Shameli

{"title":"Nearly Optimal Pricing Algorithms for Production Constrained and Laminar Bayesian Selection","authors":"Nima Anari, Rad Niazadeh, A. Saberi, A. Shameli","doi":"10.1145/3328526.3329652","DOIUrl":"https://doi.org/10.1145/3328526.3329652","url":null,"abstract":"In the Bayesian online selection problem, the goal is to find a pricing algorithm for serving a sequence of arriving buyers that maximizes the expected social-welfare (or revenue) subject to different types of structural constraints. The focus of this paper is on the case where the allowable subsets of served customers are characterized by a laminar matroid with constant depth. This problem is a special case of the well-known matroid Bayesian online selection problem studied in [Kleinberg & Weinberg, 2012], when the underlying matroid is laminar. We give the first Polynomial-Time Approximation Scheme (PTAS) for the above problem. Our approach is based on rounding the solution of a hierarchy of linear programming relaxations that can approximate the optimum online solution with any degree of accuracy as well as a concentration argument that shows our rounding does not have a considerable loss in the expected social welfare. We also introduce the production constrained problem, for which the allowable subsets of served customers are characterized by joint production/shipping constraints that can be modeled by a special case of laminar matroids. We show that by leveraging the special structure of this problem, and using a similar approach as before, we can design a PTAS for this problem too even in the case where the depth of the laminar matroid is not constant. To achieve our result we exploit the negative dependence property of the selection rule in the lower-levels of the laminar family.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131971654","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}

引用次数: 20

How Do Classifiers Induce Agents to Invest Effort Strategically? 分类器如何诱导智能体策略性地投入精力?

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-07-13 DOI: 10.1145/3328526.3329584

J. Kleinberg, Manish Raghavan

引用次数: 119

Power of Dynamic Pricing in Revenue Management with Strategic (Forward-looking) Customers 动态定价在策略性(前瞻性)客户收益管理中的作用

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-06-17 DOI: 10.2139/ssrn.3197959

Yiwei Chen, Stefanus Jasin

{"title":"Power of Dynamic Pricing in Revenue Management with Strategic (Forward-looking) Customers","authors":"Yiwei Chen, Stefanus Jasin","doi":"10.2139/ssrn.3197959","DOIUrl":"https://doi.org/10.2139/ssrn.3197959","url":null,"abstract":"The present paper considers a canonical revenue management problem wherein a monopolist seller seeks to maximize revenue from selling a fixed inventory of a product to customers who arrive over time. We assume that customers are forward-looking and rationally strategize the timing of their purchases, an empirically confirmed aspect of modern customer behavior. We consider a broad class of customer utility models that allow customer disutility from waiting to be heterogeneous and correlated with product valuations. Chen et al. [1] show that the so-called fixed price policy is asymptotically optimal in the high-volume regime where both the seller's initial inventory and the length of the selling horizon are proportionally scaled. Specifically, the revenue loss of the fixed price policy is O( k1/2), where k is the system's scaling parameter. In the present paper, we present a novel real-time pricing policy. This policy repeatedly updates the fixed price policy in Chen et al. [1] by taking into account the volatility of the historic sales. We force the price process under this policy to be non-decreasing over time. Therefore, our policy incentivizes strategic customers to behave myopically. We show that if the seller updates the price for only a single time, then the revenue loss of our policy can be arbitrarily close to O(k1/3 ln k). If the seller updates the prices with a frequency O(lnk/ln ln k), then the revenue loss of our policy can be arbitrarily close to O((ln k)3). These results are novel and show the power of dynamic pricing in the presence of forward-looking customers, at least for the problem setting considered in this paper.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"28 31","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132271807","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}

引用次数: 2

A Marketplace for Data: An Algorithmic Solution 数据市场:一个算法解决方案

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-05-21 DOI: 10.1145/3328526.3329589

Anish Agarwal, M. Dahleh, Tuhin Sarkar

{"title":"A Marketplace for Data: An Algorithmic Solution","authors":"Anish Agarwal, M. Dahleh, Tuhin Sarkar","doi":"10.1145/3328526.3329589","DOIUrl":"https://doi.org/10.1145/3328526.3329589","url":null,"abstract":"In this work, we aim to design a data marketplace; a robust real-time matching mechanism to efficiently buy and sell training data for Machine Learning tasks. While the monetization of data and pre-trained models is an essential focus of industry today, there does not exist a market mechanism to price training data and match buyers to sellers while still addressing the associated (computational and other) complexity. The challenge in creating such a market stems from the very nature of data as an asset: (i) it is freely replicable; (ii) its value is inherently combinatorial due to correlation with signal in other data; (iii) prediction tasks and the value of accuracy vary widely; (iv) usefulness of training data is difficult to verify a priori without first applying it to a prediction task. As our main contributions we: (i) propose a mathematical model for a two-sided data market and formally define the key associated challenges; (ii) construct algorithms for such a market to function and analyze how they meet the challenges defined. We highlight two technical contributions: (i) a new notion of \"fairness\" required for cooperative games with freely replicable goods; (ii) a truthful, zero regret mechanism to auction a class of combinatorial goods based on utilizing Myerson's payment function and the Multiplicative Weights algorithm. These might be of independent interest.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125133587","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}

引用次数: 151

Adaptive-Price Combinatorial Auctions 自适应价格组合拍卖

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-05-14 DOI: 10.2139/ssrn.3195827

Sébastien Lahaie, Benjamin Lubin

引用次数: 5

Optimal Pricing in Markets with Non-Convex Costs 非凸成本市场的最优定价

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-04-01 DOI: 10.2139/ssrn.3365416

Navid Azizan, Yu Su, Krishnamurthy Dvijotham, A. Wierman

{"title":"Optimal Pricing in Markets with Non-Convex Costs","authors":"Navid Azizan, Yu Su, Krishnamurthy Dvijotham, A. Wierman","doi":"10.2139/ssrn.3365416","DOIUrl":"https://doi.org/10.2139/ssrn.3365416","url":null,"abstract":"We consider a market run by an operator who seeks to satisfy a given consumer demand for a commodity by purchasing the needed amount from a group of competing suppliers with non-convex cost functions. The operator knows the suppliers' cost functions and announces a price/payment function for each supplier, which determines the payment to that supplier for producing different quantities. Each supplier then makes an individual decision about how much to produce (and whether to participate at all), in order to maximize its own profit. The key question is how to design the price functions. This problem is relevant for many applications, including electricity markets. The main contribution of this paper is the introduction of a new pricing scheme, name (acr ) pricing, which is applicable to general non-convex costs, allows using general parametric price functions, and guarantees market clearing, revenue adequacy, and ecomonic efficiency while supporting comptitive euqilibrium. The name of this scheme stems from the fact that we directly impose all the equilibrium conditions as constraints in the optimization problem for finding the best allocations, as opposed to adjusting the prices later to make the allocations an equilibrium. While the optimization problem is, of course, non-convex, and non-convex problems are intractable in general, we present a tractable approximation algorithm for solving the proposed optimization problem. Our framework extends to the case of networked markets, which, to the best of our knowledge, has not been considered in previous work.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116206368","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}

引用次数: 24

Carpooling and the Economics of Self-Driving Cars 拼车和自动驾驶汽车的经济学

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-02-01 DOI: 10.1145/3328526.3329625

M. Ostrovsky, M. Schwarz

引用次数: 53

Graphon Games Graphon游戏

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2018-01-31 DOI: 10.1145/3328526.3329638

F. Parise, A. Ozdaglar

引用次数: 45

Fair Mixing: the Case of Dichotomous Preferences 公平混合:二元偏好的案例

Proceedings of the 2019 ACM Conference on Economics and Computation Pub Date : 2017-12-07 DOI: 10.1145/3328526.3329552

H. Aziz, Anna Bogomolnaia, H. Moulin

引用次数: 61