Peer-to-peer rental markets for short-term accommodation enable "hosts" to rent out properties to "guests." A common critique of such markets is that hosts benefit at the expense of their unwitting neighbors on whom they impose costs, potentially creating a market failure that justifies policy intervention. In this paper we examine four policy regimes that differ in the allocation of the decision right to become a host. We consider the market outcomes when hosting decisions are made by (1) individual tenants that maximize their revenue, (2) building owners that consider only rents from long-term rentals, (3) cities that consider the surplus of all city residents, and (4) a social planner that takes into account the surplus of both city residents and guests. We find that the equilibrium where tenants are allowed to individually decide has too much hosting, whereas city-specific policies result in too little hosting. The efficient social planner's solution is equivalent to the equilibrium where building owners decide. We conclude with a discussion of our results and lay out directions for future research.
{"title":"The Tragedy of your Upstairs Neighbors: Is the Negative Externality of Airbnb Internalized?","authors":"Apostolos Filippas, J. Horton","doi":"10.1145/3033274.3085097","DOIUrl":"https://doi.org/10.1145/3033274.3085097","url":null,"abstract":"Peer-to-peer rental markets for short-term accommodation enable \"hosts\" to rent out properties to \"guests.\" A common critique of such markets is that hosts benefit at the expense of their unwitting neighbors on whom they impose costs, potentially creating a market failure that justifies policy intervention. In this paper we examine four policy regimes that differ in the allocation of the decision right to become a host. We consider the market outcomes when hosting decisions are made by (1) individual tenants that maximize their revenue, (2) building owners that consider only rents from long-term rentals, (3) cities that consider the surplus of all city residents, and (4) a social planner that takes into account the surplus of both city residents and guests. We find that the equilibrium where tenants are allowed to individually decide has too much hosting, whereas city-specific policies result in too little hosting. The efficient social planner's solution is equivalent to the equilibrium where building owners decide. We conclude with a discussion of our results and lay out directions for future research.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121099597","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}
Shrabastee Banerjee, Chrysanthos Dellarocas, G. Zervas
In this paper, we study the question and answer (Q&A) feature of electronic commerce platforms, an increasingly common form of user-generated content (UGC) that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, we show that Q&As complement reviews and ratings: unlike reviews, Q&As primarily happen pre-purchase, focus on clarification of product attributes (rather than discussion of quality), and convey fit-specific information in a sentiment-free way. Our main hypothesis is that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. We show that when low-rated products start receiving Q&As, their subsequent ratings improve by approximately 0.5 stars. We further show that the extent of the rating increase due to Q&As is moderated by the degree of ex-ante fit uncertainty. Overall, our findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings and sales of low-rated products, particularly those products that have incurred low ratings due to customer-product fit mismatch.
{"title":"Interacting User Generated Content Technologies: How Q&As Affect Ratings & Reviews","authors":"Shrabastee Banerjee, Chrysanthos Dellarocas, G. Zervas","doi":"10.1145/3033274.3084087","DOIUrl":"https://doi.org/10.1145/3033274.3084087","url":null,"abstract":"In this paper, we study the question and answer (Q&A) feature of electronic commerce platforms, an increasingly common form of user-generated content (UGC) that allows consumers to publicly ask product-specific questions and receive responses, either from the platform or from other customers. Using data from a major online retailer, we show that Q&As complement reviews and ratings: unlike reviews, Q&As primarily happen pre-purchase, focus on clarification of product attributes (rather than discussion of quality), and convey fit-specific information in a sentiment-free way. Our main hypothesis is that Q&As mitigate product fit uncertainty, leading to better matches between products and consumers, and therefore improved product ratings. We show that when low-rated products start receiving Q&As, their subsequent ratings improve by approximately 0.5 stars. We further show that the extent of the rating increase due to Q&As is moderated by the degree of ex-ante fit uncertainty. Overall, our findings suggest that, by resolving product fit uncertainty in an e-commerce setting, the addition of Q&As can be a viable way for retailers to improve ratings and sales of low-rated products, particularly those products that have incurred low ratings due to customer-product fit mismatch.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121100417","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}
Rupert Freeman, David M. Pennock, Jennifer Wortman Vaughan
We develop the first incentive compatible and near-Pareto-optimal wagering mechanism. Wagering mechanisms can be used to elicit predictions from agents who reveal their beliefs by placing bets. Lambert et al. [20, 21] introduced weighted score wagering mechanisms, a class of budget-balanced wagering mechanisms under which agents with immutable beliefs truthfully report their predictions. However, we demonstrate that these and other existing incentive compatible wagering mechanisms are not Pareto optimal: agents have significant budget left over even when additional trade would be mutually beneficial. Motivated by this observation, we design a new wagering mechanism, the double clinching auction, a two-sided version of the adaptive clinching auction [9]. We show that no wagering mechanism can simultaneously satisfy weak budget balance, individual rationality, weak incentive compatibility, and Pareto optimality. However, we prove that the double clinching auction attains the first three and show in a series of simulations using real contest data that it comes much closer to Pareto optimality than previously known incentive compatible wagering mechanisms, in some cases almost matching the efficiency of the Pareto optimal (but not incentive compatible) parimutuel consensus mechanism. When the goal of wagering is to crowdsource probabilities, Pareto optimality drives participation and incentive compatibility drives accuracy, making the double clinching auction an attractive and practical choice. Our mechanism may be of independent interest as the first two-sided version of the adaptive clinching auction.
{"title":"The Double Clinching Auction for Wagering","authors":"Rupert Freeman, David M. Pennock, Jennifer Wortman Vaughan","doi":"10.1145/3033274.3085118","DOIUrl":"https://doi.org/10.1145/3033274.3085118","url":null,"abstract":"We develop the first incentive compatible and near-Pareto-optimal wagering mechanism. Wagering mechanisms can be used to elicit predictions from agents who reveal their beliefs by placing bets. Lambert et al. [20, 21] introduced weighted score wagering mechanisms, a class of budget-balanced wagering mechanisms under which agents with immutable beliefs truthfully report their predictions. However, we demonstrate that these and other existing incentive compatible wagering mechanisms are not Pareto optimal: agents have significant budget left over even when additional trade would be mutually beneficial. Motivated by this observation, we design a new wagering mechanism, the double clinching auction, a two-sided version of the adaptive clinching auction [9]. We show that no wagering mechanism can simultaneously satisfy weak budget balance, individual rationality, weak incentive compatibility, and Pareto optimality. However, we prove that the double clinching auction attains the first three and show in a series of simulations using real contest data that it comes much closer to Pareto optimality than previously known incentive compatible wagering mechanisms, in some cases almost matching the efficiency of the Pareto optimal (but not incentive compatible) parimutuel consensus mechanism. When the goal of wagering is to crowdsource probabilities, Pareto optimality drives participation and incentive compatibility drives accuracy, making the double clinching auction an attractive and practical choice. Our mechanism may be of independent interest as the first two-sided version of the adaptive clinching auction.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655280","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}
Several recent authors have advocated for financial markets to move from continuous clearing to discrete or batched clearing, as a way to defeat the latency arms race: the never-ending quest for small advantages in time to access markets. How frequently should such a modern batch auction clear? We conduct a systematic simulation-based investigation on the relationship between clearing frequency and metrics of market quality, such as allocative efficiency, comparing the performance of discrete and continuous auction mechanisms under empirical equilibrium behavior of all participating traders. In effect we perform empirical mechanism design on frequent batch auctions. We find that in a wide array of environments, equilibrium efficiency is improved for small positive intervals but falls off dramatically when there are too few opportunities to trade. The result is a large range of batch frequencies that are near optimally efficient; this range is wider in thick markets.
{"title":"Empirical Mechanism Design for Optimizing Clearing Interval in Frequent Call Markets","authors":"Erik Brinkman, Michael P. Wellman","doi":"10.1145/3033274.3085153","DOIUrl":"https://doi.org/10.1145/3033274.3085153","url":null,"abstract":"Several recent authors have advocated for financial markets to move from continuous clearing to discrete or batched clearing, as a way to defeat the latency arms race: the never-ending quest for small advantages in time to access markets. How frequently should such a modern batch auction clear? We conduct a systematic simulation-based investigation on the relationship between clearing frequency and metrics of market quality, such as allocative efficiency, comparing the performance of discrete and continuous auction mechanisms under empirical equilibrium behavior of all participating traders. In effect we perform empirical mechanism design on frequent batch auctions. We find that in a wide array of environments, equilibrium efficiency is improved for small positive intervals but falls off dramatically when there are too few opportunities to trade. The result is a large range of batch frequencies that are near optimally efficient; this range is wider in thick markets.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"119 14","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120824208","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 problem of optimal information sharing in the context of a service system. In particular, we consider an unobservable single server queue offering a service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider can observe the queue, and may share information about the state of the queue with each arriving customer. The customers are Bayesian and strategic, and incorporate any information provided by the service provider into their prior beliefs about the queue length before making the decision whether to join the queue or leave without obtaining service. We pose the following question: which signaling mechanism and what price should the service provider select to maximize her revenue? We formulate this problem as an instance of Bayesian persuasion in dynamic settings. The underlying dynamics make the problem more difficult because, in contrast to static settings, the signaling mechanism adopted by the service provider affects the customers' prior beliefs about the queue (given by the steady state distribution of the queue length in equilibrium). The core contribution of this work is in characterizing the structure of the optimal signaling mechanism. We summarize our main results as follows. (1) Structural characterization: Using a revelation-principle style argument, we find that it suffices to consider signaling mechanisms where the service provider sends a binary signal of "join" or "leave", and under which the equilibrium strategy of a customer is to follow the service provider's recommended action. (2) Optimality of threshold policies: For a given fixed price for service, we use the structural characterization to show that the optimal signaling mechanism can be obtained as a solution to a linear program with a countable number of variables and constraints. Under some mild technical conditions on the waiting costs, we establish that there exists an optimal signaling mechanism with a threshold structure, where service provider sends the "join" signal if the queue length is below a threshold, and "leave" otherwise. (In addition, at the threshold, the service provider randomizes.) For the special case of linear waiting costs, we derive an analytical expression for the optimal threshold i terms of the two branches of the Lambert-W function. (3) Revenue comparison: Finally, we show that with the optimal choice of the fixed price and using the corresponding optimal signaling mechanism, the service provider can achieve the same revenue as with the optimal state-dependent pricing mechanism in a fully-observable queue. This implies that in settings where state-dependent pricing is not feasible, the service provider can effectively use optimal signaling (with the optimal fixed price) to achieve the same revenue.
{"title":"Optimal Signaling Mechanisms in Unobservable Queues with Strategic Customers","authors":"David Lingenbrink, Krishnamurthy Iyer","doi":"10.1145/3033274.3085135","DOIUrl":"https://doi.org/10.1145/3033274.3085135","url":null,"abstract":"We study the problem of optimal information sharing in the context of a service system. In particular, we consider an unobservable single server queue offering a service at a fixed price to a Poisson arrival of delay-sensitive customers. The service provider can observe the queue, and may share information about the state of the queue with each arriving customer. The customers are Bayesian and strategic, and incorporate any information provided by the service provider into their prior beliefs about the queue length before making the decision whether to join the queue or leave without obtaining service. We pose the following question: which signaling mechanism and what price should the service provider select to maximize her revenue? We formulate this problem as an instance of Bayesian persuasion in dynamic settings. The underlying dynamics make the problem more difficult because, in contrast to static settings, the signaling mechanism adopted by the service provider affects the customers' prior beliefs about the queue (given by the steady state distribution of the queue length in equilibrium). The core contribution of this work is in characterizing the structure of the optimal signaling mechanism. We summarize our main results as follows. (1) Structural characterization: Using a revelation-principle style argument, we find that it suffices to consider signaling mechanisms where the service provider sends a binary signal of \"join\" or \"leave\", and under which the equilibrium strategy of a customer is to follow the service provider's recommended action. (2) Optimality of threshold policies: For a given fixed price for service, we use the structural characterization to show that the optimal signaling mechanism can be obtained as a solution to a linear program with a countable number of variables and constraints. Under some mild technical conditions on the waiting costs, we establish that there exists an optimal signaling mechanism with a threshold structure, where service provider sends the \"join\" signal if the queue length is below a threshold, and \"leave\" otherwise. (In addition, at the threshold, the service provider randomizes.) For the special case of linear waiting costs, we derive an analytical expression for the optimal threshold i terms of the two branches of the Lambert-W function. (3) Revenue comparison: Finally, we show that with the optimal choice of the fixed price and using the corresponding optimal signaling mechanism, the service provider can achieve the same revenue as with the optimal state-dependent pricing mechanism in a fully-observable queue. This implies that in settings where state-dependent pricing is not feasible, the service provider can effectively use optimal signaling (with the optimal fixed price) to achieve the same revenue.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125037328","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}
Ride-hailing applications (apps) like Uber and Lyft introduced a matching technology and market design that recent research has found is more efficient than traditional taxi systems [2]. However, unlike traditional street-hailing taxi systems, they are prone to a failure mode first anticipated by [1]. In this paper we model and empirically establish the existence of these dynamics. We then show how surge pricing and, to a lesser extent, other market design interventions can prevent this problem from crippling a ride-hailing market. An over-burdened dispatch system results in available idle drivers being too thinly spread throughout a city, forcing matches between drivers and passengers that are far away from each other. Cars are thus sent on a wild goose chase (WGC) to pick up distant customers, wasting drivers' time and reducing earnings. This effectively removes cars from the road both directly (as the cars are busy making pick-ups) and indirectly (as cars exit in the face of reduced earnings), exacerbating the problem. This harmful feedback cycle results in a dramatic fall in welfare, hurting both drivers and passengers. A ride-hailing market that falls into WGCs frequently might therefore perform worse than traditional street-hailing taxi systems, so it is essential to understand WGCs in order to design markets in a way that avoids WGCs and exploits the potential welfare gains from the new technology. [1] dismissed WGCs as Pareto-dominated equilibria and thus just a theoretical curiosity. However, we show that when prices are too low relative to demand all equilibria of the market are WGCs when using a first-dispatch protocol, in which an idle driver is immediately dispatched every time a rider requests a trip (as many ride-hailing services have committed to). This suggests two ways in which pricing can avoid WGCs. First, one might set a single high price all the time, sufficiently high to avoid WGCs even at peak-demand periods. Of course this design has the drawback that prices will be unnecessarily high, and thus demand inefficiently suppressed, at times of low demand. A more elaborate mechanism is to use dynamic ``surge pricing'' that responds to market conditions. Such a system was introduced by Uber early in its development. Prices are set high during peak-loads, but can fall when demand is more normal. Thus, against the common perception, surge pricing allows ride-hailing apps to reduce prices from the static baseline instead of increasing them.
{"title":"Surge Pricing Solves the Wild Goose Chase","authors":"Juan-Camilo Castillo, Daniel T. Knoepfle, E. Weyl","doi":"10.1145/3033274.3085098","DOIUrl":"https://doi.org/10.1145/3033274.3085098","url":null,"abstract":"Ride-hailing applications (apps) like Uber and Lyft introduced a matching technology and market design that recent research has found is more efficient than traditional taxi systems [2]. However, unlike traditional street-hailing taxi systems, they are prone to a failure mode first anticipated by [1]. In this paper we model and empirically establish the existence of these dynamics. We then show how surge pricing and, to a lesser extent, other market design interventions can prevent this problem from crippling a ride-hailing market. An over-burdened dispatch system results in available idle drivers being too thinly spread throughout a city, forcing matches between drivers and passengers that are far away from each other. Cars are thus sent on a wild goose chase (WGC) to pick up distant customers, wasting drivers' time and reducing earnings. This effectively removes cars from the road both directly (as the cars are busy making pick-ups) and indirectly (as cars exit in the face of reduced earnings), exacerbating the problem. This harmful feedback cycle results in a dramatic fall in welfare, hurting both drivers and passengers. A ride-hailing market that falls into WGCs frequently might therefore perform worse than traditional street-hailing taxi systems, so it is essential to understand WGCs in order to design markets in a way that avoids WGCs and exploits the potential welfare gains from the new technology. [1] dismissed WGCs as Pareto-dominated equilibria and thus just a theoretical curiosity. However, we show that when prices are too low relative to demand all equilibria of the market are WGCs when using a first-dispatch protocol, in which an idle driver is immediately dispatched every time a rider requests a trip (as many ride-hailing services have committed to). This suggests two ways in which pricing can avoid WGCs. First, one might set a single high price all the time, sufficiently high to avoid WGCs even at peak-demand periods. Of course this design has the drawback that prices will be unnecessarily high, and thus demand inefficiently suppressed, at times of low demand. A more elaborate mechanism is to use dynamic ``surge pricing'' that responds to market conditions. Such a system was introduced by Uber early in its development. Prices are set high during peak-loads, but can fall when demand is more normal. Thus, against the common perception, surge pricing allows ride-hailing apps to reduce prices from the static baseline instead of increasing them.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129016709","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 address two related unanswered questions in maximum revenue multi-item auctions. Is dominant-strategy implementation equivalent to the semantically less stringent Bayesian one (as in the case of Myerson's 1-item auction)? Can one find explicit solutions for non-trivial families of multi-item auctions (as in the 1-item case)? In this paper, we present such natural families whose explicit solutions exhibit a revenue gap between the two implementations. More precisely, consider the k-item n-buyer maximum revenue auction where k, n >1 with additive valuation in the independent setting (i.e., the buyers i have independent private distributions Fij on items j). We derive exact formulas for the maximum revenue when k=2 and Fij are any IID distributions on support of size 2, for both the dominant-strategy (DIC) and the Bayesian (BIC) implementations. The formulas lead to the simple characterization that, the two models have identical maximum revenue if and only if selling-separately is optimal for the distribution. Our results also give the first demonstration, in this setting, of revenue gaps between the two models. For instance, if k=n=2 and Pr{X{F = 1} = Pr{XF =2 } = 1/2, then the maximum revenue in the Bayesian implementation exceeds that in the dominant-strategy by exactly 2%; the same gap exists for the continuous uniform distribution XF over [a, a+1] ∪ [2a, 2a+1] for all large a.
{"title":"Dominant-Strategy versus Bayesian Multi-item Auctions: Maximum Revenue Determination and Comparison","authors":"A. Yao","doi":"10.1145/3033274.3085120","DOIUrl":"https://doi.org/10.1145/3033274.3085120","url":null,"abstract":"We address two related unanswered questions in maximum revenue multi-item auctions. Is dominant-strategy implementation equivalent to the semantically less stringent Bayesian one (as in the case of Myerson's 1-item auction)? Can one find explicit solutions for non-trivial families of multi-item auctions (as in the 1-item case)? In this paper, we present such natural families whose explicit solutions exhibit a revenue gap between the two implementations. More precisely, consider the k-item n-buyer maximum revenue auction where k, n >1 with additive valuation in the independent setting (i.e., the buyers i have independent private distributions Fij on items j). We derive exact formulas for the maximum revenue when k=2 and Fij are any IID distributions on support of size 2, for both the dominant-strategy (DIC) and the Bayesian (BIC) implementations. The formulas lead to the simple characterization that, the two models have identical maximum revenue if and only if selling-separately is optimal for the distribution. Our results also give the first demonstration, in this setting, of revenue gaps between the two models. For instance, if k=n=2 and Pr{X{F = 1} = Pr{XF =2 } = 1/2, then the maximum revenue in the Bayesian implementation exceeds that in the dominant-strategy by exactly 2%; the same gap exists for the continuous uniform distribution XF over [a, a+1] ∪ [2a, 2a+1] for all large a.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125656008","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 revenue-maximizing seller with a single item facing a single buyer with a private budget. The (value, budget) pair is drawn from an arbitrary and possibly correlated distribution. We characterize the optimal mechanism in such cases, and quantify the amount of price discrimination that might be present. For example, there could be up to 3·2k-1 -1 distinct non-trivial menu options in the optimal mechanism for such a buyer with k distinct possible budgets (compared to k if the marginal distribution of values conditioned on each budget has decreasing marginal revenue [CG00], or 2 if there is an arbitrary distribution and one possible budget [CMM11]). Our approach makes use of the duality framework of [CDW16], and duality techniques related to the "FedEx Problem" of [FGKK16]. In contrast to [FGKK16] and other prior work, we characterize the optimal primal/dual without nailing down an explicit closed form.
{"title":"The Optimal Mechanism for Selling to a Budget Constrained Buyer: The General Case","authors":"Nikhil R. Devanur","doi":"10.1145/3033274.3085132","DOIUrl":"https://doi.org/10.1145/3033274.3085132","url":null,"abstract":"We consider a revenue-maximizing seller with a single item facing a single buyer with a private budget. The (value, budget) pair is drawn from an arbitrary and possibly correlated distribution. We characterize the optimal mechanism in such cases, and quantify the amount of price discrimination that might be present. For example, there could be up to 3·2k-1 -1 distinct non-trivial menu options in the optimal mechanism for such a buyer with k distinct possible budgets (compared to k if the marginal distribution of values conditioned on each budget has decreasing marginal revenue [CG00], or 2 if there is an arbitrary distribution and one possible budget [CMM11]). Our approach makes use of the duality framework of [CDW16], and duality techniques related to the \"FedEx Problem\" of [FGKK16]. In contrast to [FGKK16] and other prior work, we characterize the optimal primal/dual without nailing down an explicit closed form.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126843334","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}
Artur Gorokh, Siddhartha Banerjee, Krishnamurthy Iyer
Non-monetary mechanisms for repeated resource allocation are gaining widespread use in many real-world settings. Our aim in this work is to study the allocative efficiency and incentive properties of simple repeated mechanisms based on artificial currencies. Within this framework, we make three main contributions: We provide a general black-box technique to convert any static monetary mechanism to a dynamic mechanism with artificial currency, that simultaneously guarantees vanishing loss in efficiency, and vanishing gains from non-truthful bidding over time. On a computational front, we show how such a mechanism can be implemented using only sample-access to the agents' type distributions, and requires roughly twice the amount of computation as needed to run the monetary mechanism alone. For settings with two agents, we show that a particular artificial currency mechanism also results in a vanishing price of anarchy. This provides additional justification for the use of artificial currency mechanisms in practice. Moreover, we show how to leverage this result to demonstrate the existence of a Bayesian incentive-compatible mechanism with vanishing efficiency loss in this setting. Our work takes a significant step towards bridging the gap between monetary and non-monetary mechanisms, and also points to several open problems.
{"title":"From Monetary to Non-Monetary Mechanism Design via Artificial Currencies","authors":"Artur Gorokh, Siddhartha Banerjee, Krishnamurthy Iyer","doi":"10.2139/ssrn.2964082","DOIUrl":"https://doi.org/10.2139/ssrn.2964082","url":null,"abstract":"Non-monetary mechanisms for repeated resource allocation are gaining widespread use in many real-world settings. Our aim in this work is to study the allocative efficiency and incentive properties of simple repeated mechanisms based on artificial currencies. Within this framework, we make three main contributions: We provide a general black-box technique to convert any static monetary mechanism to a dynamic mechanism with artificial currency, that simultaneously guarantees vanishing loss in efficiency, and vanishing gains from non-truthful bidding over time. On a computational front, we show how such a mechanism can be implemented using only sample-access to the agents' type distributions, and requires roughly twice the amount of computation as needed to run the monetary mechanism alone. For settings with two agents, we show that a particular artificial currency mechanism also results in a vanishing price of anarchy. This provides additional justification for the use of artificial currency mechanisms in practice. Moreover, we show how to leverage this result to demonstrate the existence of a Bayesian incentive-compatible mechanism with vanishing efficiency loss in this setting. Our work takes a significant step towards bridging the gap between monetary and non-monetary mechanisms, and also points to several open problems.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126200423","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}
Two-sided matching platforms, such as those for labor, accommodation, dating, and taxi hailing, can control and optimize over many aspects of the search for partners. To understand how the search for partners should be designed, we consider a dynamic model of search by strategic agents with costly discovery of pair-specific match value. We find that in many settings, the platform can mitigate wasteful competition in partner search via restricting what agents can see/do. For medium-sized screening costs (relative to idiosyncratic variation in utilities), the platform should prevent one side of the market from exercising choice (similar to Instant Book on Airbnb), whereas for large screening costs, the platform should centrally determine matches (similar to taxi hailing marketplaces). Surprisingly, simple restrictions can improve social welfare even when screening costs are small, and agents on each side are ex-ante homogeneous. In asymmetric markets where agents on one side have a tendency to be more selective (due to smaller screening costs or greater market power), the platform should force the more selective side of the market to reach out first, by explicitly disallowing the less selective side from doing so. This allows the agents on the less selective side to exercise more choice in equilibrium.When agents are vertically differentiated, forcing one side of the market to propose results in a significant increase in welfare even in the limit of vanishing screening costs. Furthermore, a Pareto improvement in welfare is possible in this limit: the weakest agents can be helped without hurting other agents. In addition, in this setting the platform can further boost welfare by hiding quality information.
{"title":"Facilitating the Search for Partners on Matching Platforms: Restricting Agent Actions","authors":"Yashodhan Kanoria, D. Sabán","doi":"10.1145/3033274.3084092","DOIUrl":"https://doi.org/10.1145/3033274.3084092","url":null,"abstract":"Two-sided matching platforms, such as those for labor, accommodation, dating, and taxi hailing, can control and optimize over many aspects of the search for partners. To understand how the search for partners should be designed, we consider a dynamic model of search by strategic agents with costly discovery of pair-specific match value. We find that in many settings, the platform can mitigate wasteful competition in partner search via restricting what agents can see/do. For medium-sized screening costs (relative to idiosyncratic variation in utilities), the platform should prevent one side of the market from exercising choice (similar to Instant Book on Airbnb), whereas for large screening costs, the platform should centrally determine matches (similar to taxi hailing marketplaces). Surprisingly, simple restrictions can improve social welfare even when screening costs are small, and agents on each side are ex-ante homogeneous. In asymmetric markets where agents on one side have a tendency to be more selective (due to smaller screening costs or greater market power), the platform should force the more selective side of the market to reach out first, by explicitly disallowing the less selective side from doing so. This allows the agents on the less selective side to exercise more choice in equilibrium.When agents are vertically differentiated, forcing one side of the market to propose results in a significant increase in welfare even in the limit of vanishing screening costs. Furthermore, a Pareto improvement in welfare is possible in this limit: the weakest agents can be helped without hurting other agents. In addition, in this setting the platform can further boost welfare by hiding quality information.","PeriodicalId":287551,"journal":{"name":"Proceedings of the 2017 ACM Conference on Economics and Computation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116221729","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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