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.
{"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}
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.
{"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}
Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcomes they receive, and we give a tight characterization of when such agents can be incentivized to invest specified forms of effort into improving their outcomes as opposed to "gaming" the classifier. We show that whenever any "reasonable" mechanism can do so, a simple linear mechanism suffices.
{"title":"How Do Classifiers Induce Agents to Invest Effort Strategically?","authors":"J. Kleinberg, Manish Raghavan","doi":"10.1145/3328526.3329584","DOIUrl":"https://doi.org/10.1145/3328526.3329584","url":null,"abstract":"Algorithms are often used to produce decision-making rules that classify or evaluate individuals. When these individuals have incentives to be classified a certain way, they may behave strategically to influence their outcomes. We develop a model for how strategic agents can invest effort in order to change the outcomes they receive, and we give a tight characterization of when such agents can be incentivized to invest specified forms of effort into improving their outcomes as opposed to \"gaming\" the classifier. We show that whenever any \"reasonable\" mechanism can do so, a simple linear mechanism suffices.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129832389","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}
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.
{"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}
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.
{"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}
This work introduces a novel iterative combinatorial auction that aims to achieve both high efficiency and fast convergence across a wide range of valuation domains. We design the first fully adaptive-price combinatorial auction that gradually extends price expressivity as the rounds progress. We implement our auction design using polynomial prices and show how to detect when the current price structure is insufficient to clear the market, and how to expand the polynomial structure to guarantee progress. An experimental evaluation confirms that our auction is competitive with bundle-price auctions in regimes where these excel, namely multi-minded valuations, but also performs well in regimes favorable to linear prices, such as valuations with pairwise synergy.
{"title":"Adaptive-Price Combinatorial Auctions","authors":"Sébastien Lahaie, Benjamin Lubin","doi":"10.2139/ssrn.3195827","DOIUrl":"https://doi.org/10.2139/ssrn.3195827","url":null,"abstract":"This work introduces a novel iterative combinatorial auction that aims to achieve both high efficiency and fast convergence across a wide range of valuation domains. We design the first fully adaptive-price combinatorial auction that gradually extends price expressivity as the rounds progress. We implement our auction design using polynomial prices and show how to detect when the current price structure is insufficient to clear the market, and how to expand the polynomial structure to guarantee progress. An experimental evaluation confirms that our auction is competitive with bundle-price auctions in regimes where these excel, namely multi-minded valuations, but also performs well in regimes favorable to linear prices, such as valuations with pairwise synergy.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"626 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133846862","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}
Navid Azizan, Yu Su, Krishnamurthy Dvijotham, A. Wierman
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.
{"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}
We study the interplay between autonomous transportation, carpooling, and road pricing. We discuss how improvements in these technologies, and interactions among them, will affect transportation markets. Our main results show how to achieve socially efficient outcomes in such markets, taking into account the costs of driving, road capacity, and commuter preferences. An important component of the efficient outcome is the socially optimal matching of carpooling riders. Our approach shows how to set road prices and how to share the costs of driving and tolls among carpooling riders in a way that implements the efficient outcome.
{"title":"Carpooling and the Economics of Self-Driving Cars","authors":"M. Ostrovsky, M. Schwarz","doi":"10.1145/3328526.3329625","DOIUrl":"https://doi.org/10.1145/3328526.3329625","url":null,"abstract":"We study the interplay between autonomous transportation, carpooling, and road pricing. We discuss how improvements in these technologies, and interactions among them, will affect transportation markets. Our main results show how to achieve socially efficient outcomes in such markets, taking into account the costs of driving, road capacity, and commuter preferences. An important component of the efficient outcome is the socially optimal matching of carpooling riders. Our approach shows how to set road prices and how to share the costs of driving and tolls among carpooling riders in a way that implements the efficient outcome.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124307930","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}
We propose a way to approximate games played over networks of increasing size by using the graph limiting concept of graphon. To this end, we introduce the new class of graphon games for populations of infinite size. We investigate existence and uniqueness properties of the Nash equilibrium of graphon games and we derive upper bounds for the distance between the Nash equilibria of the infinite population graphon game and of finite population sampled network games. We then show that it is possible to design almost optimal interventions for sampled network games by relying on the graphon model.
{"title":"Graphon Games","authors":"F. Parise, A. Ozdaglar","doi":"10.1145/3328526.3329638","DOIUrl":"https://doi.org/10.1145/3328526.3329638","url":null,"abstract":"We propose a way to approximate games played over networks of increasing size by using the graph limiting concept of graphon. To this end, we introduce the new class of graphon games for populations of infinite size. We investigate existence and uniqueness properties of the Nash equilibrium of graphon games and we derive upper bounds for the distance between the Nash equilibria of the infinite population graphon game and of finite population sampled network games. We then show that it is possible to design almost optimal interventions for sampled network games by relying on the graphon model.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122294797","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}
We consider a setting in which agents vote to choose a fair mixture of public outcomes. The agents have dichotomous preferences: each outcome is liked or disliked by an agent. We discuss three outstanding voting rules. The Conditional Utilitarian rule, a variant of the random dictator, is strategyproof and guarantees to any group of like-minded agents an influence proportional to its size. It is easier to compute and more efficient than the familiar Random Priority rule. Its worst case (resp. average) inefficiency is provably (resp. in numerical experiments) low if the number of agents is low. The efficient Egalitarian rule protects individual agents but not coalitions. It is excludable strategyproof: I do not want to lie if I cannot consume outcomes I claim to dislike. The efficient Nash Max Product rule offers the strongest welfare guarantees to coalitions, who can force any outcome with a probability proportional to their size. But it even fails the excludable form of strategyproofness.
{"title":"Fair Mixing: the Case of Dichotomous Preferences","authors":"H. Aziz, Anna Bogomolnaia, H. Moulin","doi":"10.1145/3328526.3329552","DOIUrl":"https://doi.org/10.1145/3328526.3329552","url":null,"abstract":"We consider a setting in which agents vote to choose a fair mixture of public outcomes. The agents have dichotomous preferences: each outcome is liked or disliked by an agent. We discuss three outstanding voting rules. The Conditional Utilitarian rule, a variant of the random dictator, is strategyproof and guarantees to any group of like-minded agents an influence proportional to its size. It is easier to compute and more efficient than the familiar Random Priority rule. Its worst case (resp. average) inefficiency is provably (resp. in numerical experiments) low if the number of agents is low. The efficient Egalitarian rule protects individual agents but not coalitions. It is excludable strategyproof: I do not want to lie if I cannot consume outcomes I claim to dislike. The efficient Nash Max Product rule offers the strongest welfare guarantees to coalitions, who can force any outcome with a probability proportional to their size. But it even fails the excludable form of strategyproofness.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114977252","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}
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