The classic herding model examines the asymptotic behavior of agents who observe their predecessors' actions as well as a private signal from an exogenous information structure. In this paper we introduce a self-interested sender into the model, and study the sender's problem of designing this information structure. If agents cannot observe each other the model reduces to Bayesian persuasion. However, when agents observe predecessors' actions, they may learn from each other, potentially harming the sender. We identify necessary and sufficient conditions under which the sender can nevertheless obtain the same utility as when the agents are unable to observe each other.
{"title":"Herd Design","authors":"Itai Arieli, R. Gradwohl, Rann Smorodinsky","doi":"10.1145/3490486.3538292","DOIUrl":"https://doi.org/10.1145/3490486.3538292","url":null,"abstract":"The classic herding model examines the asymptotic behavior of agents who observe their predecessors' actions as well as a private signal from an exogenous information structure. In this paper we introduce a self-interested sender into the model, and study the sender's problem of designing this information structure. If agents cannot observe each other the model reduces to Bayesian persuasion. However, when agents observe predecessors' actions, they may learn from each other, potentially harming the sender. We identify necessary and sufficient conditions under which the sender can nevertheless obtain the same utility as when the agents are unable to observe each other.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115273266","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 revisit the classical object reallocation problem under strict preferences. When attention is constrained to the set of Pareto-efficient rules, it is known that top trading cycles (TTC) is the only rule that is strategyproof and individually-rational. We relax this constraint and consider pair-efficiency. A rule is pair-efficient if it never induces an allocation at which a pair of agents gain from trading their assigned objects. Remarkably, even in the larger set of pair-efficient rules, we find that TTC is still the only rule that is strategyproof and individually-rational. Pair-efficiency is a minimal efficiency notion, ruling out gainful trades between only pairs of agents. Individual-rationality is a minimal voluntary participation constraint, assuring agents only that they never receive objects worse than their endowments. Therefore, our characterization result, by showing that TTC is the only strategyproof rule satisfying these two minimal conditions, gives strong support to the use of TTC in object reallocation problems. Our proof technique is based on a metric that measures the level of similarity of an arbitrary rule with TTC. We define our similarity metric by exploiting TTC's procedural nature. In future studies, we believe defining and working with similarity metrics, as done in our paper, can become functional when studying the properties of other procedural rules.
{"title":"Pair-efficient Reallocation of Indivisible Objects","authors":"Özgün Ekici","doi":"10.1145/3490486.3538341","DOIUrl":"https://doi.org/10.1145/3490486.3538341","url":null,"abstract":"We revisit the classical object reallocation problem under strict preferences. When attention is constrained to the set of Pareto-efficient rules, it is known that top trading cycles (TTC) is the only rule that is strategyproof and individually-rational. We relax this constraint and consider pair-efficiency. A rule is pair-efficient if it never induces an allocation at which a pair of agents gain from trading their assigned objects. Remarkably, even in the larger set of pair-efficient rules, we find that TTC is still the only rule that is strategyproof and individually-rational. Pair-efficiency is a minimal efficiency notion, ruling out gainful trades between only pairs of agents. Individual-rationality is a minimal voluntary participation constraint, assuring agents only that they never receive objects worse than their endowments. Therefore, our characterization result, by showing that TTC is the only strategyproof rule satisfying these two minimal conditions, gives strong support to the use of TTC in object reallocation problems. Our proof technique is based on a metric that measures the level of similarity of an arbitrary rule with TTC. We define our similarity metric by exploiting TTC's procedural nature. In future studies, we believe defining and working with similarity metrics, as done in our paper, can become functional when studying the properties of other procedural rules.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123170213","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. Brustle, J. Correa, Paul Dütting, Victor Verdugo
We study the competition complexity of dynamic pricing relative to the optimal auction in the fundamental single-item setting. In prophet inequality terminology, we compare the expected reward Am(F) achievable by the optimal online policy on m i.i.d. random variables drawn from F to the expected maximum Mn(F) of n i.i.d. draws from the same distribution. We ask how big does m have to be to ensure that (1+ε) Am(F) ≥ Mn(F) for all F. We resolve this question and exhibit a stark phase transition: When ε = 0 the competition complexity is unbounded. That is, for any n and any m there is a distribution F such that Am(F) > Mn(F). In contrast, for any ε < 0, it is sufficient and necessary to have $m = φ(ε)n where φ(ε) = Θ(log log 1/ε). Therefore, the competition complexity not only drops from being unbounded to being linear, it is actually linear with a very small constant. The technical core of our analysis is a loss-less reduction to an infinite dimensional and non-linear optimization problem that we solve optimally. A corollary of this reduction, which may be of independent interest, is a novel proof of the factor ~0.745 i.i.d. prophet inequality, which simultaneously establishes matching upper and lower bounds.
{"title":"The Competition Complexity of Dynamic Pricing","authors":"J. Brustle, J. Correa, Paul Dütting, Victor Verdugo","doi":"10.1145/3490486.3538366","DOIUrl":"https://doi.org/10.1145/3490486.3538366","url":null,"abstract":"We study the competition complexity of dynamic pricing relative to the optimal auction in the fundamental single-item setting. In prophet inequality terminology, we compare the expected reward Am(F) achievable by the optimal online policy on m i.i.d. random variables drawn from F to the expected maximum Mn(F) of n i.i.d. draws from the same distribution. We ask how big does m have to be to ensure that (1+ε) Am(F) ≥ Mn(F) for all F. We resolve this question and exhibit a stark phase transition: When ε = 0 the competition complexity is unbounded. That is, for any n and any m there is a distribution F such that Am(F) > Mn(F). In contrast, for any ε < 0, it is sufficient and necessary to have $m = φ(ε)n where φ(ε) = Θ(log log 1/ε). Therefore, the competition complexity not only drops from being unbounded to being linear, it is actually linear with a very small constant. The technical core of our analysis is a loss-less reduction to an infinite dimensional and non-linear optimization problem that we solve optimally. A corollary of this reduction, which may be of independent interest, is a novel proof of the factor ~0.745 i.i.d. prophet inequality, which simultaneously establishes matching upper and lower bounds.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130600911","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}
I propose a new approach to mechanism design: rather than assume a common prior belief, assume access to a common dataset. I restrict attention to incomplete information games where a designer commits to a policy and a single agent responds. I propose a penalized policy that performs well under weak assumptions on how the agent learns from data. Policies that are too complex, in a precise sense, are penalized because they lead to unpredictable responses by the agent. This approach leads to new insights in models of vaccine distribution, prescription drug approval, performance pay, and product bundling. The full paper is available at https://mkcamara.github.io/mdcd.pdf.
{"title":"Mechanism Design with a Common Dataset","authors":"Modibo K. Camara","doi":"10.1145/3490486.3538316","DOIUrl":"https://doi.org/10.1145/3490486.3538316","url":null,"abstract":"I propose a new approach to mechanism design: rather than assume a common prior belief, assume access to a common dataset. I restrict attention to incomplete information games where a designer commits to a policy and a single agent responds. I propose a penalized policy that performs well under weak assumptions on how the agent learns from data. Policies that are too complex, in a precise sense, are penalized because they lead to unpredictable responses by the agent. This approach leads to new insights in models of vaccine distribution, prescription drug approval, performance pay, and product bundling. The full paper is available at https://mkcamara.github.io/mdcd.pdf.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127214053","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}
As the economy digitizes, menu costs fall, and firms can more easily monitor prices. These trends have led to the rise of automatic pricing tools. We employ a novel e-commerce dataset to examine the potential implications of these developments on price competition. We provide evidence from an RDD that the immediate impact of automatic pricing is a significant decline in prices. However, repricers have developed strategies to avoid the stark competitive realities of Bertrand-Nash competition. By employing plausibly exogenous variation in the execution of repricing strategies, we find that 'resetting' strategies (which regularly raise prices, e.g., at night) effectively coax competitors to raise their prices. While the resulting patterns of cycling prices are reminiscent of Maskin-Tirole's Edgeworth cycles, a model of equilibrium in delegated strategies fits the data better. This model suggests that if the available repricing technology remains fixed, cycling will increase, and prices could rise significantly in the future.
{"title":"Algorithmic Pricing Facilitates Tacit Collusion: Evidence from E-Commerce","authors":"Leon Musolff","doi":"10.1145/3490486.3538239","DOIUrl":"https://doi.org/10.1145/3490486.3538239","url":null,"abstract":"As the economy digitizes, menu costs fall, and firms can more easily monitor prices. These trends have led to the rise of automatic pricing tools. We employ a novel e-commerce dataset to examine the potential implications of these developments on price competition. We provide evidence from an RDD that the immediate impact of automatic pricing is a significant decline in prices. However, repricers have developed strategies to avoid the stark competitive realities of Bertrand-Nash competition. By employing plausibly exogenous variation in the execution of repricing strategies, we find that 'resetting' strategies (which regularly raise prices, e.g., at night) effectively coax competitors to raise their prices. While the resulting patterns of cycling prices are reminiscent of Maskin-Tirole's Edgeworth cycles, a model of equilibrium in delegated strategies fits the data better. This model suggests that if the available repricing technology remains fixed, cycling will increase, and prices could rise significantly in the future.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134311923","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 paper, we quantify the social interaction (peer) effect from friends' and strangers' product adoption decisions. While a large literature has separately documented the importance of the observed adoptions of friends [1,2,8,9] and strangers [3,4,6,7] for spurring further product adoption, by analyzing both together we are able to benchmark their relative magnitudes, evaluate how these two types of peer effects interact, and provide managerial guidance on how firms can jointly leverage both types of peer effects in product seeding campaigns. Our context is an online multiplayer gaming app, where product adoptions correspond to microtransactions for in-game cosmetic items. We observe a rich panel of players' in-app purchases, friendship networks, interactions with friends, and, unique to this setting, interactions with strangers. Correlated adoptions may represent correlated unobserved shocks and not peer effects; we leverage the game's quasi-random assignment of strangers onto players' teams during matchmaking to generate conditionally exogenous exposure to strangers' adoptions. For peer effects from friends, we use the panel structure of our data to account for homophily and the reflection problem [5], and conduct placebo tests to check causality. Our baseline result shows that observed adoptions by friends and strangers both have positive effects on focal consumers' purchasing, and that the marginal peer effect from friends is nearly twice as large as the effect from strangers. We show that peer effects from friends and strangers are substitutes. More encounters with strangers who have adopted the product diminish the marginal peer effect from friends who have adopted the product, and vice versa. The substitution is not symmetric: encounters with friends can completely substitute for encounters with strangers. We analyze the mechanism behind the transmission of peer effects, and find evidence that while encounters with friends and strangers both raise awareness of the product, encounters with friends also lead to a mere exposure effect. We rule out learning about unobserved quality as a primary mechanism, but find that visibility of the product is important. To analyze how peer effects inform counterfactual product seeding strategies, we estimate a model of product diffusion using the observed in-app social network. We evaluate two seeding strategies which are designed to leverage peer effects from strangers and friends respectively: the first strategy seeds products to individuals who are more active on the app and therefore interact with more strangers, and the second strategy seeds products to individuals who have more friends. While both strategies improve on simple random seeding, seeding to well-connected individuals substantially outperforms seeding to active individuals, confirming the value of collecting social network data for marketing campaigns. The full paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_i
{"title":"Peer Effects from Friends and Strangers: Evidence from Random Matchmaking in an Online Game","authors":"Daniel Goetz, Weixu Lu","doi":"10.1145/3490486.3538343","DOIUrl":"https://doi.org/10.1145/3490486.3538343","url":null,"abstract":"In this paper, we quantify the social interaction (peer) effect from friends' and strangers' product adoption decisions. While a large literature has separately documented the importance of the observed adoptions of friends [1,2,8,9] and strangers [3,4,6,7] for spurring further product adoption, by analyzing both together we are able to benchmark their relative magnitudes, evaluate how these two types of peer effects interact, and provide managerial guidance on how firms can jointly leverage both types of peer effects in product seeding campaigns. Our context is an online multiplayer gaming app, where product adoptions correspond to microtransactions for in-game cosmetic items. We observe a rich panel of players' in-app purchases, friendship networks, interactions with friends, and, unique to this setting, interactions with strangers. Correlated adoptions may represent correlated unobserved shocks and not peer effects; we leverage the game's quasi-random assignment of strangers onto players' teams during matchmaking to generate conditionally exogenous exposure to strangers' adoptions. For peer effects from friends, we use the panel structure of our data to account for homophily and the reflection problem [5], and conduct placebo tests to check causality. Our baseline result shows that observed adoptions by friends and strangers both have positive effects on focal consumers' purchasing, and that the marginal peer effect from friends is nearly twice as large as the effect from strangers. We show that peer effects from friends and strangers are substitutes. More encounters with strangers who have adopted the product diminish the marginal peer effect from friends who have adopted the product, and vice versa. The substitution is not symmetric: encounters with friends can completely substitute for encounters with strangers. We analyze the mechanism behind the transmission of peer effects, and find evidence that while encounters with friends and strangers both raise awareness of the product, encounters with friends also lead to a mere exposure effect. We rule out learning about unobserved quality as a primary mechanism, but find that visibility of the product is important. To analyze how peer effects inform counterfactual product seeding strategies, we estimate a model of product diffusion using the observed in-app social network. We evaluate two seeding strategies which are designed to leverage peer effects from strangers and friends respectively: the first strategy seeds products to individuals who are more active on the app and therefore interact with more strangers, and the second strategy seeds products to individuals who have more friends. While both strategies improve on simple random seeding, seeding to well-connected individuals substantially outperforms seeding to active individuals, confirming the value of collecting social network data for marketing campaigns. The full paper is available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_i","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128615102","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}
Emily Dale, Jessica Fielding, Hari Ramakrishnan, Sacheth Sathyanarayanan, S. Weinberg
We consider the manipulability of tournament rules which take the results of (n2) pairwise matches and select a ranking over the teams. Prior work designs simple tournament rules such that no pair of teams can manipulate the outcome of their match to improve their probability of being ranked first by more than 1/3, and this is the best possible among any Condorcet-consistent tournament rule (which selects an undefeated team whenever one exists) [15,16]. We initiate the consideration of teams who may manipulate their match to improve their ranking (not necessarily to reach first). Specifically, teams compete for a monetary prize, and the ith ranked team takes home $p_i$ in prize money (pi ≥ pi+1 for all i). In this language, prior work designs tournament rules such that no pair of teams can manipulate the outcome of their match to improve their (collective) expected prize money by more than 1/3, when the price vector is ⃭1,0,..., 0›. We design a simple tournament rule (that we call Nested Randomized King of the Hill) such that: a) no pair of teams can improve their collective expected prize money by more than 1/3 for any prize vector in [0,1]n, and b) no set of any teams can gain any prize money for the uniform prize vector with pi:=(n-i)/(n-1).
{"title":"Approximately Strategyproof Tournament Rules with Multiple Prizes","authors":"Emily Dale, Jessica Fielding, Hari Ramakrishnan, Sacheth Sathyanarayanan, S. Weinberg","doi":"10.1145/3490486.3538242","DOIUrl":"https://doi.org/10.1145/3490486.3538242","url":null,"abstract":"We consider the manipulability of tournament rules which take the results of (n2) pairwise matches and select a ranking over the teams. Prior work designs simple tournament rules such that no pair of teams can manipulate the outcome of their match to improve their probability of being ranked first by more than 1/3, and this is the best possible among any Condorcet-consistent tournament rule (which selects an undefeated team whenever one exists) [15,16]. We initiate the consideration of teams who may manipulate their match to improve their ranking (not necessarily to reach first). Specifically, teams compete for a monetary prize, and the ith ranked team takes home $p_i$ in prize money (pi ≥ pi+1 for all i). In this language, prior work designs tournament rules such that no pair of teams can manipulate the outcome of their match to improve their (collective) expected prize money by more than 1/3, when the price vector is ⃭1,0,..., 0›. We design a simple tournament rule (that we call Nested Randomized King of the Hill) such that: a) no pair of teams can improve their collective expected prize money by more than 1/3 for any prize vector in [0,1]n, and b) no set of any teams can gain any prize money for the uniform prize vector with pi:=(n-i)/(n-1).","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128518962","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 analyze a model in which a firm with multiple locations chooses capacity and prices to maximize efficiency. We find that the firm provisions capacity in such a way that the expected fraction of demand that will be unfilled is lower in locations with greater expected demand. The firm also sets lower prices in larger locations. Finally, if a customer is indifferent between multiple locations, then it is more efficient to place this customer in a location with greater expected demand. These theoretical results are consistent with empirical evidence that we present from a major public cloud provider.
{"title":"Efficient Capacity Provisioning for Firms with Multiple Locations: The Case of the Public Cloud","authors":"Patrick Hummel, M. Schwarz","doi":"10.1145/3490486.3538281","DOIUrl":"https://doi.org/10.1145/3490486.3538281","url":null,"abstract":"We analyze a model in which a firm with multiple locations chooses capacity and prices to maximize efficiency. We find that the firm provisions capacity in such a way that the expected fraction of demand that will be unfilled is lower in locations with greater expected demand. The firm also sets lower prices in larger locations. Finally, if a customer is indifferent between multiple locations, then it is more efficient to place this customer in a location with greater expected demand. These theoretical results are consistent with empirical evidence that we present from a major public cloud provider.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116039351","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 simple method to estimate demand in markets for differentiated products. The method augments price and quantity data with triplets data (of the form "product A is closer to B than it is to C'') obtained from an online survey. Using a machine learning algorithm, the triplets data are used to estimate an embedding---i.e., a low-dimensional representation of the latent product space. Distances between pairs of products, computed from the embedding, discipline substitution patterns in a simple log-linear demand model. This approach solves the dimensionality problem of product-space demand models (too many cross-price elasticity parameters to estimate). We illustrate the performance of the method by estimating demand for ready-to-eat cereals and comparing our estimates to those obtained from the standard method of (BLP). We find that our elasticity estimates imply credible substitution patterns and compare favorably to the BLP estimates. Beyond our current implementation of the method, the embedding data can be incorporated in either characteristic-space demand approaches, or in more complex product-space models. Full paper available at https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=4113399.
{"title":"Embeddings and Distance-based Demand for Differentiated Products","authors":"Lorenzo Magnolfi, J. McClure, Alan T. Sorensen","doi":"10.1145/3490486.3538282","DOIUrl":"https://doi.org/10.1145/3490486.3538282","url":null,"abstract":"We propose a simple method to estimate demand in markets for differentiated products. The method augments price and quantity data with triplets data (of the form \"product A is closer to B than it is to C'') obtained from an online survey. Using a machine learning algorithm, the triplets data are used to estimate an embedding---i.e., a low-dimensional representation of the latent product space. Distances between pairs of products, computed from the embedding, discipline substitution patterns in a simple log-linear demand model. This approach solves the dimensionality problem of product-space demand models (too many cross-price elasticity parameters to estimate). We illustrate the performance of the method by estimating demand for ready-to-eat cereals and comparing our estimates to those obtained from the standard method of (BLP). We find that our elasticity estimates imply credible substitution patterns and compare favorably to the BLP estimates. Beyond our current implementation of the method, the embedding data can be incorporated in either characteristic-space demand approaches, or in more complex product-space models. Full paper available at https://papers.ssrn.com/ sol3/papers.cfm?abstract_id=4113399.","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127718832","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 information design in games with a continuum of actions such that the payoff of each player is concave in his action. A designer chooses an information structure--a joint distribution of a state and a private signal of each player. The information structure induces a Bayesian game and is evaluated according to the expected designer's payoff under the equilibrium play. We develop a method that allows to find an optimal information structure, one that cannot be outperformed by any other information structure, however complex. To do so, we exploit the property that each player's incentive is summarized by his marginal payoff. We show that an information structure is optimal whenever the induced strategies can be implemented by an incentive contract in a principal-agent problem that incorporates the players' marginal payoffs. We use this result to establish the optimality of Gaussian information structures in the settings with quadratic payoffs and a multivariate normally-distributed state. We analyze the details of optimal structures in a differentiated Bertrand competition and in a prediction game. The full paper is available at: https://arxiv.org/pdf/2202.10883.pdf
{"title":"Information Design in Concave Games","authors":"Alex Smolin, Takuro Yamashita","doi":"10.1145/3490486.3538303","DOIUrl":"https://doi.org/10.1145/3490486.3538303","url":null,"abstract":"We study information design in games with a continuum of actions such that the payoff of each player is concave in his action. A designer chooses an information structure--a joint distribution of a state and a private signal of each player. The information structure induces a Bayesian game and is evaluated according to the expected designer's payoff under the equilibrium play. We develop a method that allows to find an optimal information structure, one that cannot be outperformed by any other information structure, however complex. To do so, we exploit the property that each player's incentive is summarized by his marginal payoff. We show that an information structure is optimal whenever the induced strategies can be implemented by an incentive contract in a principal-agent problem that incorporates the players' marginal payoffs. We use this result to establish the optimality of Gaussian information structures in the settings with quadratic payoffs and a multivariate normally-distributed state. We analyze the details of optimal structures in a differentiated Bertrand competition and in a prediction game. The full paper is available at: https://arxiv.org/pdf/2202.10883.pdf","PeriodicalId":209859,"journal":{"name":"Proceedings of the 23rd ACM Conference on Economics and Computation","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122744374","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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