We consider a new model of many-to-one matching markets in which agents with multi-unit demand aim to maximize a cardinal linear objective subject to multidimensional knapsack constraints. The choice functions of agents with multi-unit demand are therefore not substitutable. As a result, pairwise stable matchings may not exist and, even when they do, may be highly inefficient. We provide an algorithm that finds a group-stable matching that approximately satisfies all the multidimensional knapsack constraints. The degree of the constraint violation is proportional to the sparsity of the constraint matrix. The algorithm therefore provides practical error bounds for applications in several contexts, such as refugee resettlement, matching of children to daycare centers, and meeting diversity requirements in colleges. A novel ingredient in our algorithm is a combination of matching with contracts and Scarf's Lemma.
{"title":"Stability in Matching Markets with Complex Constraints","authors":"Thành Nguyen, Hai Nguyen, A. Teytelboym","doi":"10.1145/3328526.3329639","DOIUrl":"https://doi.org/10.1145/3328526.3329639","url":null,"abstract":"We consider a new model of many-to-one matching markets in which agents with multi-unit demand aim to maximize a cardinal linear objective subject to multidimensional knapsack constraints. The choice functions of agents with multi-unit demand are therefore not substitutable. As a result, pairwise stable matchings may not exist and, even when they do, may be highly inefficient. We provide an algorithm that finds a group-stable matching that approximately satisfies all the multidimensional knapsack constraints. The degree of the constraint violation is proportional to the sparsity of the constraint matrix. The algorithm therefore provides practical error bounds for applications in several contexts, such as refugee resettlement, matching of children to daycare centers, and meeting diversity requirements in colleges. A novel ingredient in our algorithm is a combination of matching with contracts and Scarf's Lemma.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126223780","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}
Sameer Mehta, Milind Dawande, G. Janakiraman, V. Mookerjee
The wide variety of pricing policies used in practice by data-sellers suggests that there are significant challenges in pricing datasets. The selling of a dataset -- arranged in a row-column format, where rows represent records and columns represent attributes of the records -- is more nuanced than that of information goods like telephone minutes and bandwidth, in the sense that, for a buyer, it is not only the amount of data that matters but also the type of the data. We develop a utility framework that is appropriate for data-buyers and the corresponding pricing of the data by the data-seller. A buyer interested in purchasing a dataset has private valuations in two aspects -- her ideal record that she values the most, and the rate at which her valuation for the records in the dataset decays as they differ from her ideal record. The seller allows individual (and heterogeneous) buyers to filter the dataset and select the records that are of interest to them. The multi-dimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the dataset a challenging one. We formulate a tractable model and successfully exploit its special structure to examine it both analytically and numerically. A key result we establish is that, under reasonable assumptions, a price-quantity schedule is an optimal data-selling mechanism. Such a schedule has a nuanced interpretation in the data-selling context in that buyers buy different sets of records but the price for a given number of records does not depend on the identity of the records chosen by the buyer. Even when the assumptions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case performance guarantee relative to an optimal mechanism. Further, we numerically solve for the optimal mechanism and show that the actual performance of two simple and well-known price-quantity schedules -- two-part pricing and two-block pricing -- is near-optimal. We also quantify the value to the seller from allowing buyers to filter the dataset.
{"title":"How to Sell a Dataset? Pricing Policies for Data Monetization","authors":"Sameer Mehta, Milind Dawande, G. Janakiraman, V. Mookerjee","doi":"10.2139/ssrn.3333296","DOIUrl":"https://doi.org/10.2139/ssrn.3333296","url":null,"abstract":"The wide variety of pricing policies used in practice by data-sellers suggests that there are significant challenges in pricing datasets. The selling of a dataset -- arranged in a row-column format, where rows represent records and columns represent attributes of the records -- is more nuanced than that of information goods like telephone minutes and bandwidth, in the sense that, for a buyer, it is not only the amount of data that matters but also the type of the data. We develop a utility framework that is appropriate for data-buyers and the corresponding pricing of the data by the data-seller. A buyer interested in purchasing a dataset has private valuations in two aspects -- her ideal record that she values the most, and the rate at which her valuation for the records in the dataset decays as they differ from her ideal record. The seller allows individual (and heterogeneous) buyers to filter the dataset and select the records that are of interest to them. The multi-dimensional private information of the buyers coupled with the endogenous selection of records makes the seller's problem of optimally pricing the dataset a challenging one. We formulate a tractable model and successfully exploit its special structure to examine it both analytically and numerically. A key result we establish is that, under reasonable assumptions, a price-quantity schedule is an optimal data-selling mechanism. Such a schedule has a nuanced interpretation in the data-selling context in that buyers buy different sets of records but the price for a given number of records does not depend on the identity of the records chosen by the buyer. Even when the assumptions leading to the optimality of a price-quantity schedule do not hold, we show that the optimal price-quantity schedule offers an attractive worst-case performance guarantee relative to an optimal mechanism. Further, we numerically solve for the optimal mechanism and show that the actual performance of two simple and well-known price-quantity schedules -- two-part pricing and two-block pricing -- is near-optimal. We also quantify the value to the seller from allowing buyers to filter the dataset.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114780480","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}
Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance, or for users with long-term contracts). A natural idea to increase the provider's profit is to sell these idle instances on a spot market where users can be preempted. However, this ignores the possible "market cannibalization'' that may occur in equilibrium. In particular, users who would generate more profit in the provider's existing fixed-price market might move to the spot market and generate less profit. In this paper, we model the provider's profit optimization problem using queuing theory and game theory and analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which offering a market consisting of fixed-price instances as well as some spot instances (using idle resources) increases the provider's profit over offering only fixed-price instances. Furthermore, we show that under our condition, such a profit increase can always be combined with a Pareto improvement for the users. Finally, we illustrate our results numerically to demonstrate the effects the provider's costs and her strategy have on her profit. Full paper: https://ssrn.com/abstract=3383420
{"title":"Cloud Pricing: The Spot Market Strikes Back","authors":"Ludwig Dierks, Sven Seuken","doi":"10.2139/ssrn.3383420","DOIUrl":"https://doi.org/10.2139/ssrn.3383420","url":null,"abstract":"Cloud computing providers must constantly hold many idle compute instances available (e.g., for maintenance, or for users with long-term contracts). A natural idea to increase the provider's profit is to sell these idle instances on a spot market where users can be preempted. However, this ignores the possible \"market cannibalization'' that may occur in equilibrium. In particular, users who would generate more profit in the provider's existing fixed-price market might move to the spot market and generate less profit. In this paper, we model the provider's profit optimization problem using queuing theory and game theory and analyze the equilibria of the resulting queuing system. Our main result is an easy-to-check condition under which offering a market consisting of fixed-price instances as well as some spot instances (using idle resources) increases the provider's profit over offering only fixed-price instances. Furthermore, we show that under our condition, such a profit increase can always be combined with a Pareto improvement for the users. Finally, we illustrate our results numerically to demonstrate the effects the provider's costs and her strategy have on her profit. Full paper: https://ssrn.com/abstract=3383420","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"191 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123049173","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}
Apostolos Filippas, Srikanth Jagabathula, A. Sundararajan
We report on a randomized trial conducted during a market design transition on a sharing economy platform, where providers who formerly set rental prices for their assets were randomly assigned to groups with varying levels of pricing control. Even when faced with the prospect of significantly higher revenues, providers retaliate against the centralization of pricing by exiting the platform, reducing asset availability and cancelling transactions. Allowing providers to retain partial control lowers retaliation substantially even though providers do not frequently utilize this additional flexibility. We discuss information asymmetry, divergent incentives, and psychological contract violation as alternative explanations for our results.
{"title":"Managing Market Mechanism Transitions: A Randomized Trial of Decentralized Pricing Versus Platform Control","authors":"Apostolos Filippas, Srikanth Jagabathula, A. Sundararajan","doi":"10.1145/3328526.3329654","DOIUrl":"https://doi.org/10.1145/3328526.3329654","url":null,"abstract":"We report on a randomized trial conducted during a market design transition on a sharing economy platform, where providers who formerly set rental prices for their assets were randomly assigned to groups with varying levels of pricing control. Even when faced with the prospect of significantly higher revenues, providers retaliate against the centralization of pricing by exiting the platform, reducing asset availability and cancelling transactions. Allowing providers to retain partial control lowers retaliation substantially even though providers do not frequently utilize this additional flexibility. We discuss information asymmetry, divergent incentives, and psychological contract violation as alternative explanations for our results.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129474970","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 black-box reductions from mechanism design to algorithm design for welfare maximization in settings of incomplete information. Given oracle access to an algorithm for an underlying optimization problem, the goal is to simulate an incentive compatible mechanism. The mechanism will be evaluated on its expected welfare, relative to the algorithm provided, and its complexity is measured by the time (and queries) needed to simulate the mechanism on any input. While it is known that black-box reductions are not possible in many prior-free settings, settings with priors appear more promising: there are known reductions for Bayesian incentive compatible (BIC) mechanism design for general classes of welfare maximization problems. This dichotomy begs the question: which mechanism design problems admit black-box reductions, and which do not? Our main result is that black-box mechanism design is impossible under two of the simplest settings not captured by known positive results. First, for the problem of allocating n goods to a single buyer whose valuation is additive and independent across the goods, subject to a downward-closed constraint on feasible allocations, we show that there is no polytime (in n) BIC black-box reduction for expected welfare maximization. Second, for the setting of multiple single-parameter agents---where polytime BIC reductions are known---we show that no polytime reductions exist when the incentive requirement is tightened to Max-In-Distributional-Range. In each case, we show that achieving a sub-polynomial approximation to the expected welfare requires exponentially many queries, even when the set of feasible allocations is known to be downward-closed.
{"title":"The Complexity of Black-Box Mechanism Design with Priors","authors":"Evangelia Gergatsouli, Brendan Lucier, Christos Tzamos","doi":"10.1145/3328526.3329648","DOIUrl":"https://doi.org/10.1145/3328526.3329648","url":null,"abstract":"We study black-box reductions from mechanism design to algorithm design for welfare maximization in settings of incomplete information. Given oracle access to an algorithm for an underlying optimization problem, the goal is to simulate an incentive compatible mechanism. The mechanism will be evaluated on its expected welfare, relative to the algorithm provided, and its complexity is measured by the time (and queries) needed to simulate the mechanism on any input. While it is known that black-box reductions are not possible in many prior-free settings, settings with priors appear more promising: there are known reductions for Bayesian incentive compatible (BIC) mechanism design for general classes of welfare maximization problems. This dichotomy begs the question: which mechanism design problems admit black-box reductions, and which do not? Our main result is that black-box mechanism design is impossible under two of the simplest settings not captured by known positive results. First, for the problem of allocating n goods to a single buyer whose valuation is additive and independent across the goods, subject to a downward-closed constraint on feasible allocations, we show that there is no polytime (in n) BIC black-box reduction for expected welfare maximization. Second, for the setting of multiple single-parameter agents---where polytime BIC reductions are known---we show that no polytime reductions exist when the incentive requirement is tightened to Max-In-Distributional-Range. In each case, we show that achieving a sub-polynomial approximation to the expected welfare requires exponentially many queries, even when the set of feasible allocations is known to be downward-closed.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"362 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133133303","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}
Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.
{"title":"The Supply and Demand Effects of Review Platforms","authors":"Gregory Lewis, G. Zervas","doi":"10.2139/ssrn.3468278","DOIUrl":"https://doi.org/10.2139/ssrn.3468278","url":null,"abstract":"Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126977275","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}
J. Correa, Paul Dütting, Felix A. Fischer, Kevin Schewior
A central object in optimal stopping theory is the single-choice prophet inequality for independent, identically distributed random variables: given a sequence of random variables X1, ..., Xn drawn independently from a distribution F, the goal is to choose a stopping time τ so as to maximize α such that for all distributions F we have E[Xτ]≥α•E[maxt Xt]. What makes this problem challenging is that the decision whether τ=t may only depend on the values of the random variables X1, ..., Xt and on the distribution F. For a long time the best known bound for the problem had been α≥1-1/e≅0.632, but quite recently a tight bound of α≅0.745 was obtained. The case where F is unknown, such that the decision whether τ=t may depend only on the values of the random variables X1, ..., Xt, is equally well motivated but has received much less attention. A straightforward guarantee for this case of α≥1-1/e≅0.368 can be derived from the solution to the secretary problem, where an arbitrary set of values arrive in random order and the goal is to maximize the probability of selecting the largest value. We show that this bound is in fact tight. We then investigate the case where the stopping time may additionally depend on a limited number of samples from~F, and show that even with o(n) samples α≥1/e. On the other hand, n samples allow for a significant improvement, while O(n2) samples are equivalent to knowledge of the distribution: specifically, with n samples α≥1-1/e≅0.632 and α≥ln(2)≅0.693, and with O(n2) samples α≥0.745-ε for any ε>0.
{"title":"Prophet Inequalities for I.I.D. Random Variables from an Unknown Distribution","authors":"J. Correa, Paul Dütting, Felix A. Fischer, Kevin Schewior","doi":"10.1145/3328526.3329627","DOIUrl":"https://doi.org/10.1145/3328526.3329627","url":null,"abstract":"A central object in optimal stopping theory is the single-choice prophet inequality for independent, identically distributed random variables: given a sequence of random variables X1, ..., Xn drawn independently from a distribution F, the goal is to choose a stopping time τ so as to maximize α such that for all distributions F we have E[Xτ]≥α•E[maxt Xt]. What makes this problem challenging is that the decision whether τ=t may only depend on the values of the random variables X1, ..., Xt and on the distribution F. For a long time the best known bound for the problem had been α≥1-1/e≅0.632, but quite recently a tight bound of α≅0.745 was obtained. The case where F is unknown, such that the decision whether τ=t may depend only on the values of the random variables X1, ..., Xt, is equally well motivated but has received much less attention. A straightforward guarantee for this case of α≥1-1/e≅0.368 can be derived from the solution to the secretary problem, where an arbitrary set of values arrive in random order and the goal is to maximize the probability of selecting the largest value. We show that this bound is in fact tight. We then investigate the case where the stopping time may additionally depend on a limited number of samples from~F, and show that even with o(n) samples α≥1/e. On the other hand, n samples allow for a significant improvement, while O(n2) samples are equivalent to knowledge of the distribution: specifically, with n samples α≥1-1/e≅0.632 and α≥ln(2)≅0.693, and with O(n2) samples α≥0.745-ε for any ε>0.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116562201","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 proliferation of rich consumer-level datasets has led to the rise of the "algorithmic modeling culture" [2] wherein analysts treat the statistical model as a "black box" and predict choices using algorithms trained on existing datasets. In most cases, these evaluations of algorithmic prediction have focused on settings where individuals face the same choices over time. However, evaluating policy questions often involves modeling a substantial shift in the choice environment. For example, a health insurance reform may change the set of insurance products that consumers can buy, or a merger may alter the products available in the marketplace. For such questions, it is less obvious whether machine learning methods can usefully be applied. As Athey [1] remarks: [M]uch less attention has been paid to the limitations of pure prediction methods. When SML [supervised machine learning] applications are used "off the shelf" without understanding the underlying assumptions or ensuring that conditions like stability [of the environment] are met, then the validity and usefulness of the conclusions can be compromised.
{"title":"How Do Machine Learning Algorithms Perform in Predicting Hospital Choices?: Evidence from Changing Environments","authors":"D. Raval, Ted Rosenbaum, N. Wilson","doi":"10.1145/3328526.3329602","DOIUrl":"https://doi.org/10.1145/3328526.3329602","url":null,"abstract":"The proliferation of rich consumer-level datasets has led to the rise of the \"algorithmic modeling culture\" [2] wherein analysts treat the statistical model as a \"black box\" and predict choices using algorithms trained on existing datasets. In most cases, these evaluations of algorithmic prediction have focused on settings where individuals face the same choices over time. However, evaluating policy questions often involves modeling a substantial shift in the choice environment. For example, a health insurance reform may change the set of insurance products that consumers can buy, or a merger may alter the products available in the marketplace. For such questions, it is less obvious whether machine learning methods can usefully be applied. As Athey [1] remarks: [M]uch less attention has been paid to the limitations of pure prediction methods. When SML [supervised machine learning] applications are used \"off the shelf\" without understanding the underlying assumptions or ensuring that conditions like stability [of the environment] are met, then the validity and usefulness of the conclusions can be compromised.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130085686","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 value of price discrimination in large random networks. Recent trends in industry suggest that increasingly firms are using information about social network to offer personalized prices to individuals based upon their positions in the social network. In the presence of positive network externalities, firms aim to increase their profits by offering discounts to influential individuals that can stimulate consumption by other individuals at a higher price. However, the lack of transparency in discriminative pricing can reduce consumer satisfaction and create mistrust. Recent research has focused on the computation of optimal prices in deterministic networks under positive externalities. We would like to answer the question: how valuable is such discriminative pricing? We find, surprisingly, that the value of such pricing policies (increase in profits due to price discrimination) in very large random networks are often not significant. We provide the exact rates at which this value grows in the size of the random networks for different ranges of network densities.
{"title":"The Value of Price Discrimination in Large Random Networks","authors":"Jiali Huang, Ankur Mani, Zizhuo Wang","doi":"10.2139/ssrn.3368458","DOIUrl":"https://doi.org/10.2139/ssrn.3368458","url":null,"abstract":"We study the value of price discrimination in large random networks. Recent trends in industry suggest that increasingly firms are using information about social network to offer personalized prices to individuals based upon their positions in the social network. In the presence of positive network externalities, firms aim to increase their profits by offering discounts to influential individuals that can stimulate consumption by other individuals at a higher price. However, the lack of transparency in discriminative pricing can reduce consumer satisfaction and create mistrust. Recent research has focused on the computation of optimal prices in deterministic networks under positive externalities. We would like to answer the question: how valuable is such discriminative pricing? We find, surprisingly, that the value of such pricing policies (increase in profits due to price discrimination) in very large random networks are often not significant. We provide the exact rates at which this value grows in the size of the random networks for different ranges of network densities.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125033014","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}
Taylor Lundy, Alexander Wei, Hu Fu, S. Kominers, Kevin Leyton-Brown
We consider the problem of a nonprofit organization ("center") that must divide resources among subsidiaries ("agents"), based on agents' reported demand forecasts, with the aim of maximizing social good (agents' valuations for the allocation minus any payments that are imposed on them). We investigate the impact of a common feature of the nonprofit setting: the center's ability to audit agents who receive allocations, comparing their actual consumption with their reported forecasts. We show that auditing increases the power of mechanisms for utility maximization, both in unit-demand settings and beyond: in unit-demand settings, we consider both constraining ourselves to an allocation function studied in past work and allowing the allocation function to vary; beyond unit demand, we adopt the VCG allocation but modify the payment rule. Our ultimate goal is to show how to leverage auditing mechanisms to maximize utility in repeated allocation problems where payments are not possible; we show how any static auditing mechanism can be transformed to operate in such a setting, using the threat of reduced future allocations in place of monetary payments.
{"title":"Allocation for Social Good: Auditing Mechanisms for Utility Maximization","authors":"Taylor Lundy, Alexander Wei, Hu Fu, S. Kominers, Kevin Leyton-Brown","doi":"10.1145/3328526.3329623","DOIUrl":"https://doi.org/10.1145/3328526.3329623","url":null,"abstract":"We consider the problem of a nonprofit organization (\"center\") that must divide resources among subsidiaries (\"agents\"), based on agents' reported demand forecasts, with the aim of maximizing social good (agents' valuations for the allocation minus any payments that are imposed on them). We investigate the impact of a common feature of the nonprofit setting: the center's ability to audit agents who receive allocations, comparing their actual consumption with their reported forecasts. We show that auditing increases the power of mechanisms for utility maximization, both in unit-demand settings and beyond: in unit-demand settings, we consider both constraining ourselves to an allocation function studied in past work and allowing the allocation function to vary; beyond unit demand, we adopt the VCG allocation but modify the payment rule. Our ultimate goal is to show how to leverage auditing mechanisms to maximize utility in repeated allocation problems where payments are not possible; we show how any static auditing mechanism can be transformed to operate in such a setting, using the threat of reduced future allocations in place of monetary payments.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134025732","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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