When deciding whether to tax a product, policymakers need to consider the effects of the intervention on price and output. While the existing literature focuses on “symmetric taxes,” i.e., on schemes imposing the same tax amount on all products in the market, in practice, some taxes are “asymmetric”, in the sense that they levy different amounts on competing products, or even affect only a subset of them. Since the equilibrium responses to asymmetric taxes are going to affect the relative prices of different products in the market, asymmetric taxes can have large effects on competition and market outcomes. The goal of this paper is to study empirically the effects of asymmetric taxes on market outcomes. In January 2020, nominally motivated by a desire to reduce congestion and raise revenues for the city budget, the city of Chicago taxed ride-sharing (e.g., Uber and Lyft) but did not impose any surcharge to riders taking rides with traditional taxis. By taxing ride-sharing up to $3.00 per trip, this policy imposed the highest surcharge faced by ride-sharing companies in the US, providing an informative case study to investigate the consequences of asymmetric taxes. First, I investigate the pass-through rate of the tax on ride-sharing. Ride-sharing companies are peer-to-peer marketplaces which connect riders to drivers. This generates additional insights to the analysis of pass-through because the reduction in demand following a tax levied on riders can reduce drivers’ willingness to supply rides, which, in turn, increases the equilibrium price of ride-sharing. This means that, in a two-sided market, indirect network effects make it easier to rationalize tax overshifting than it would be in a conventional one-sided firm analysis. Second, I quantify the extent to which the tax shifts demand back to traditional taxis, and how this effect varies across different areas of the city. This sheds light on the substitutability between traditional taxis and ride-sharing and on its determinants. Third, I consider the effect of the policy on congestion. In fact, the exacerbation of congestion is a critical issue in many large and growing metropolitan areas, and congestion taxes have been a widely used tool to reduce traffic. Finally, I study the impact of the tax on consumer welfare, by developing and calibrating a logit demand framework that accommodates asymmetric tax schedules. The results of my analyses provide evidence that the tax on ride-sharing had economically significant effects on markets outcomes. Consistently with the existence of indirect network effects, I estimate pass-through rates exceeding unity on single rides, while for shared rides I find incomplete pass-through. This pattern can be explained by a higher elasticity of demand for shared rides as compared to single ones. The change in prices of ride-sharing shifts demand back to traditional taxis but only for downtown rides, where the number of equilibrium taxi pickups increases by 3.63%. T
{"title":"Asymmetric Taxation, Pass-through and Market Competition: Evidence from Ride-sharing and Taxis","authors":"Mario Leccese","doi":"10.1145/3490486.3538245","DOIUrl":"https://doi.org/10.1145/3490486.3538245","url":null,"abstract":"When deciding whether to tax a product, policymakers need to consider the effects of the intervention on price and output. While the existing literature focuses on “symmetric taxes,” i.e., on schemes imposing the same tax amount on all products in the market, in practice, some taxes are “asymmetric”, in the sense that they levy different amounts on competing products, or even affect only a subset of them. Since the equilibrium responses to asymmetric taxes are going to affect the relative prices of different products in the market, asymmetric taxes can have large effects on competition and market outcomes. The goal of this paper is to study empirically the effects of asymmetric taxes on market outcomes. In January 2020, nominally motivated by a desire to reduce congestion and raise revenues for the city budget, the city of Chicago taxed ride-sharing (e.g., Uber and Lyft) but did not impose any surcharge to riders taking rides with traditional taxis. By taxing ride-sharing up to $3.00 per trip, this policy imposed the highest surcharge faced by ride-sharing companies in the US, providing an informative case study to investigate the consequences of asymmetric taxes. First, I investigate the pass-through rate of the tax on ride-sharing. Ride-sharing companies are peer-to-peer marketplaces which connect riders to drivers. This generates additional insights to the analysis of pass-through because the reduction in demand following a tax levied on riders can reduce drivers’ willingness to supply rides, which, in turn, increases the equilibrium price of ride-sharing. This means that, in a two-sided market, indirect network effects make it easier to rationalize tax overshifting than it would be in a conventional one-sided firm analysis. Second, I quantify the extent to which the tax shifts demand back to traditional taxis, and how this effect varies across different areas of the city. This sheds light on the substitutability between traditional taxis and ride-sharing and on its determinants. Third, I consider the effect of the policy on congestion. In fact, the exacerbation of congestion is a critical issue in many large and growing metropolitan areas, and congestion taxes have been a widely used tool to reduce traffic. Finally, I study the impact of the tax on consumer welfare, by developing and calibrating a logit demand framework that accommodates asymmetric tax schedules. The results of my analyses provide evidence that the tax on ride-sharing had economically significant effects on markets outcomes. Consistently with the existence of indirect network effects, I estimate pass-through rates exceeding unity on single rides, while for shared rides I find incomplete pass-through. This pattern can be explained by a higher elasticity of demand for shared rides as compared to single ones. The change in prices of ride-sharing shifts demand back to traditional taxis but only for downtown rides, where the number of equilibrium taxi pickups increases by 3.63%. T","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":"122468532","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 present a novel way for cryptocurrency miners to manipulate the effective interest-rate on loans or deposits they make on decentralized finance (DeFi) platforms by manipulating difficulty-adjustment algorithms (DAAs) and changing the block-rate. This presents a new class of strategic manipulations available to miners. These manipulations allow miners to stretch and squeeze the time between consecutive blocks. We analyze these manipulations both analytically and empirically, and show that a 25% miner can stretch the time between consecutive blocks by up to 54% in Ethereum and 33% in Bitcoin, and squeeze it by up to 9% in Ethereum. Ethereum is particularly vulnerable, and even relatively small miners can seriously affect the block-rate. An interesting application of these manipulations is to create an artificial interest-rate gap between loans taken from one DeFi platform which accrues interest according to block height (such as Compound [66]) and deposited in some other platform that does so according to elapsed time (like a bank, or other DeFi platforms such as Aave [99]). Hence, stretching and squeezing the block-rate can decrease the interest paid on DeFi loans relative to external financial platforms. The profit made from this interest-rate gap provides a large incentive for miners to deviate. For example, a 25% Ethereum miner using our manipulations can increase mining profits by up to 35%, even after taking potential losses into consideration, such as less block-rewards. Our analysis of these manipulations and their mitigations has broad implications with regards to commonly-used cryptocurrency mechanisms and paradigms, such as Ethereum's difficulty-adjustment algorithm and reward schemes, with Ethereum's handling of uncle blocks being particularly manipulable. Interestingly, Bitcoin's mechanism is more resistant Ethereum's, owing to its larger incentives and a more resilient DAA.
{"title":"Blockchain Stretching & Squeezing: Manipulating Time for Your Best Interest","authors":"Aviv Yaish, Saar Tochner, Aviv Zohar","doi":"10.1145/3490486.3538250","DOIUrl":"https://doi.org/10.1145/3490486.3538250","url":null,"abstract":"We present a novel way for cryptocurrency miners to manipulate the effective interest-rate on loans or deposits they make on decentralized finance (DeFi) platforms by manipulating difficulty-adjustment algorithms (DAAs) and changing the block-rate. This presents a new class of strategic manipulations available to miners. These manipulations allow miners to stretch and squeeze the time between consecutive blocks. We analyze these manipulations both analytically and empirically, and show that a 25% miner can stretch the time between consecutive blocks by up to 54% in Ethereum and 33% in Bitcoin, and squeeze it by up to 9% in Ethereum. Ethereum is particularly vulnerable, and even relatively small miners can seriously affect the block-rate. An interesting application of these manipulations is to create an artificial interest-rate gap between loans taken from one DeFi platform which accrues interest according to block height (such as Compound [66]) and deposited in some other platform that does so according to elapsed time (like a bank, or other DeFi platforms such as Aave [99]). Hence, stretching and squeezing the block-rate can decrease the interest paid on DeFi loans relative to external financial platforms. The profit made from this interest-rate gap provides a large incentive for miners to deviate. For example, a 25% Ethereum miner using our manipulations can increase mining profits by up to 35%, even after taking potential losses into consideration, such as less block-rewards. Our analysis of these manipulations and their mitigations has broad implications with regards to commonly-used cryptocurrency mechanisms and paradigms, such as Ethereum's difficulty-adjustment algorithm and reward schemes, with Ethereum's handling of uncle blocks being particularly manipulable. Interestingly, Bitcoin's mechanism is more resistant Ethereum's, owing to its larger incentives and a more resilient DAA.","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":"126793076","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 undertake a system-level analysis of the conference peer review process. The process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned; the key obstacle is that the evaluation of the merits of a submission (both by the authors and the reviewers) is inherently noisy. Over the years, conferences have experimented with numerous policies and innovations to navigate the tradeoffs. These experiments include setting various bars for acceptance, varying the number of reviews per submission, requiring prior reviews to be included with resubmissions, and others. The purpose of the present work is to investigate, both analytically and using agent-based simulations, how well various policies work, and more importantly, why they do or do not work. We model the conference-author interactions as a Stackelberg game in which a prestigious conference commits to a threshold acceptance policy which will be applied to the (noisy) reviews of each submitted paper; the authors best-respond by submitting or not submitting to the conference, the alternative being a "sure accept" (such as arXiv or a lightly refereed venue). Our findings include: observing that the conference should typically set a higher acceptance threshold than the actual desired quality, which we call the resubmission gap and quantify in terms of various parameters; observing that the reviewing load is heavily driven by resubmissions of borderline papers --- therefore, a judicious choice of acceptance threshold may lead to fewer reviews while incurring an acceptable loss in quality; observing that depending on the paper quality distribution, stricter reviewing may lead to higher or lower acceptance rates --- the former is the result of self selection by the authors. As a rule of thumb, a relatively small number of reviews per paper, coupled with a strict acceptance policy, tends to do well in trading off these two objectives; finding that a relatively small increase in review quality or in self assessment by the authors is much more effective for conference quality control (without a large increase in review burden) than increases in the quantity of reviews per paper.; showing that keeping track of past reviews of papers can help reduce the review burden without a decrease in conference quality. For robustness, we consider different models of paper quality and learn some of the parameters from real data.
{"title":"A System-Level Analysis of Conference Peer Review","authors":"Yichi Zhang, Fang-Yi Yu, G. Schoenebeck, D. Kempe","doi":"10.1145/3490486.3538235","DOIUrl":"https://doi.org/10.1145/3490486.3538235","url":null,"abstract":"We undertake a system-level analysis of the conference peer review process. The process involves three constituencies with different objectives: authors want their papers accepted at prestigious venues (and quickly), conferences want to present a program with many high-quality and few low-quality papers, and reviewers want to avoid being overburdened by reviews. These objectives are far from aligned; the key obstacle is that the evaluation of the merits of a submission (both by the authors and the reviewers) is inherently noisy. Over the years, conferences have experimented with numerous policies and innovations to navigate the tradeoffs. These experiments include setting various bars for acceptance, varying the number of reviews per submission, requiring prior reviews to be included with resubmissions, and others. The purpose of the present work is to investigate, both analytically and using agent-based simulations, how well various policies work, and more importantly, why they do or do not work. We model the conference-author interactions as a Stackelberg game in which a prestigious conference commits to a threshold acceptance policy which will be applied to the (noisy) reviews of each submitted paper; the authors best-respond by submitting or not submitting to the conference, the alternative being a \"sure accept\" (such as arXiv or a lightly refereed venue). Our findings include: observing that the conference should typically set a higher acceptance threshold than the actual desired quality, which we call the resubmission gap and quantify in terms of various parameters; observing that the reviewing load is heavily driven by resubmissions of borderline papers --- therefore, a judicious choice of acceptance threshold may lead to fewer reviews while incurring an acceptable loss in quality; observing that depending on the paper quality distribution, stricter reviewing may lead to higher or lower acceptance rates --- the former is the result of self selection by the authors. As a rule of thumb, a relatively small number of reviews per paper, coupled with a strict acceptance policy, tends to do well in trading off these two objectives; finding that a relatively small increase in review quality or in self assessment by the authors is much more effective for conference quality control (without a large increase in review burden) than increases in the quantity of reviews per paper.; showing that keeping track of past reviews of papers can help reduce the review burden without a decrease in conference quality. For robustness, we consider different models of paper quality and learn some of the parameters from real data.","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":"124193009","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 a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about their opponents. The seller only knows the (possibly asymmetric) means of the marginal distributions of each bidder's valuation and the range. An adversarial nature chooses the worst-case distribution within this ambiguity set along with the worst-case information structure. We find that a second-price auction with a symmetric, random reserve price obtains the optimal revenue guarantee within a broad class of mechanisms we refer to as competitive mechanisms, which include standard auction formats such as the first-price auction. The optimal mechanism possesses two notable characteristics. First, the mechanism treats all bidders identically even in the presence of ex-ante asymmetries. Second, when bidders have identical means and the number of bidders n grows large, the seller's optimal reserve price converges in probability to a non-binding reserve price and the revenue guarantee converges to the mean at rate O(1/n).
{"title":"Robustly Optimal Auction Design under Mean Constraints","authors":"Ethan Che","doi":"10.1145/3490486.3538357","DOIUrl":"https://doi.org/10.1145/3490486.3538357","url":null,"abstract":"We study a seller who sells a single good to multiple bidders with uncertainty over the joint distribution of bidders' valuations, as well as bidders' higher-order beliefs about their opponents. The seller only knows the (possibly asymmetric) means of the marginal distributions of each bidder's valuation and the range. An adversarial nature chooses the worst-case distribution within this ambiguity set along with the worst-case information structure. We find that a second-price auction with a symmetric, random reserve price obtains the optimal revenue guarantee within a broad class of mechanisms we refer to as competitive mechanisms, which include standard auction formats such as the first-price auction. The optimal mechanism possesses two notable characteristics. First, the mechanism treats all bidders identically even in the presence of ex-ante asymmetries. Second, when bidders have identical means and the number of bidders n grows large, the seller's optimal reserve price converges in probability to a non-binding reserve price and the revenue guarantee converges to the mean at rate O(1/n).","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":"127884873","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}
Can a school-choice clearinghouse generate a stable matching if it does not allow students to express preferences over peers? Theoretically, we show stable matchings exist with peer preferences under mild conditions but finding one via canonical mechanisms is unlikely. Increasing transparency about the previous cohort's matching induces a tâtonnement process wherein prior matchings function as prices. We develop a test for stability and implement it empirically in the college admissions market in New South Wales, Australia. We find evidence of preferences over relative peer ability, but no convergence to stability. We propose a mechanism improving upon the current assignment process. Link to full version of paper: https://tinyurl.com/25kdz22e
{"title":"Do Peer Preferences Matter in School Choice Market Design?: Theory and Evidence","authors":"Natalie Cox, R. Fonseca, Bobak Pakzad-Hurson","doi":"10.1145/3490486.3538274","DOIUrl":"https://doi.org/10.1145/3490486.3538274","url":null,"abstract":"Can a school-choice clearinghouse generate a stable matching if it does not allow students to express preferences over peers? Theoretically, we show stable matchings exist with peer preferences under mild conditions but finding one via canonical mechanisms is unlikely. Increasing transparency about the previous cohort's matching induces a tâtonnement process wherein prior matchings function as prices. We develop a test for stability and implement it empirically in the college admissions market in New South Wales, Australia. We find evidence of preferences over relative peer ability, but no convergence to stability. We propose a mechanism improving upon the current assignment process. Link to full version of paper: https://tinyurl.com/25kdz22e","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":"130226230","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 Internet-scale peer-to-peer (P2P) systems usually build their success on distributed protocols. For example, the well-known BitTorrent network for resource exchange is based on the proportional response protocol, where each participant exchanges its resources with its neighbors in proportion to what it has received in the previous round. The dynamics of such a protocol has been proved to converge to a market equilibrium. On the other hand, it requires thorough incentive analysis to show the robustness of such protocol, as the distributed agents may strategically manipulate the system once they are able to benefit. Recent studies have developed strategyproofness results of the proportional response protocol against agent deviations in the forms of weight cheating and edge deleting. However, the protocol is not truthful against Sybil attacks, under which an agent may create several fictitious identities and control these fictitious identities to exchange resources with others. In this paper, we apply the concept of incentive ratio to measure how much the utility of a strategic agent in a market equilibrium can be improved by playing Sybil attacks. We prove a tight incentive ratio of two for any agent launching Sybil attacks over general networks. The tight incentive ratio of two closes an open problem modeling the successful tit-for-tat protocol for Internet resource exchanging and also presents a complete picture in this line of theoretical studies with real applications.
{"title":"Tight Incentive Analysis on Sybil Attacks to Market Equilibrium of Resource Exchange over General Networks","authors":"Yukun Cheng, Xiaotie Deng, Yuhao Li, X. Yan","doi":"10.1145/3490486.3538378","DOIUrl":"https://doi.org/10.1145/3490486.3538378","url":null,"abstract":"The Internet-scale peer-to-peer (P2P) systems usually build their success on distributed protocols. For example, the well-known BitTorrent network for resource exchange is based on the proportional response protocol, where each participant exchanges its resources with its neighbors in proportion to what it has received in the previous round. The dynamics of such a protocol has been proved to converge to a market equilibrium. On the other hand, it requires thorough incentive analysis to show the robustness of such protocol, as the distributed agents may strategically manipulate the system once they are able to benefit. Recent studies have developed strategyproofness results of the proportional response protocol against agent deviations in the forms of weight cheating and edge deleting. However, the protocol is not truthful against Sybil attacks, under which an agent may create several fictitious identities and control these fictitious identities to exchange resources with others. In this paper, we apply the concept of incentive ratio to measure how much the utility of a strategic agent in a market equilibrium can be improved by playing Sybil attacks. We prove a tight incentive ratio of two for any agent launching Sybil attacks over general networks. The tight incentive ratio of two closes an open problem modeling the successful tit-for-tat protocol for Internet resource exchanging and also presents a complete picture in this line of theoretical studies with real applications.","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":"116140568","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}
Matheus V. X. Ferreira, Ye Lin, Sally Hahn, S. Weinberg, Catherine Yu
Cryptographic Self-Selection is a subroutine used to select a leader for modern proof-of-stake consensus protocols. In cryptographic self-selection, each round r has a seed Qr. In round r, each account owner is asked to digitally sign Qr, hash their digital signature to produce a credential, and then broadcast this credential to the entire network. A publicly-known function scores each credential in a manner so that the distribution of the lowest scoring credential is identical to the distribution of stake owned by each account. The user who broadcasts the lowest-scoring credential is the leader for round r, and their credential becomes the seed Qr+1. Such protocols leave open the possibility of manipulation: a user who owns multiple accounts that each produce low-scoring credentials in round r can selectively choose which ones to broadcast in order to influence the seed for round r+1. Indeed, the user can pre-compute their credentials for round r+1 for each potential seed, and broadcast only the credential (among those with low enough score to be leader) that produces the most favorable seed. We consider an adversary who wishes to maximize the expected fraction of rounds in which an account they own is the leader. We show such an adversary always benefits from deviating from the intended protocol, regardless of the fraction of the stake controlled. We characterize the optimal strategy; first by proving the existence of optimal positive recurrent strategies whenever the adversary owns last than 3-5/2 ~38% of the stake. Then, we provide a Markov Decision Process formulation to compute the optimal strategy.
{"title":"Optimal Strategic Mining Against Cryptographic Self-Selection in Proof-of-Stake","authors":"Matheus V. X. Ferreira, Ye Lin, Sally Hahn, S. Weinberg, Catherine Yu","doi":"10.1145/3490486.3538337","DOIUrl":"https://doi.org/10.1145/3490486.3538337","url":null,"abstract":"Cryptographic Self-Selection is a subroutine used to select a leader for modern proof-of-stake consensus protocols. In cryptographic self-selection, each round r has a seed Qr. In round r, each account owner is asked to digitally sign Qr, hash their digital signature to produce a credential, and then broadcast this credential to the entire network. A publicly-known function scores each credential in a manner so that the distribution of the lowest scoring credential is identical to the distribution of stake owned by each account. The user who broadcasts the lowest-scoring credential is the leader for round r, and their credential becomes the seed Qr+1. Such protocols leave open the possibility of manipulation: a user who owns multiple accounts that each produce low-scoring credentials in round r can selectively choose which ones to broadcast in order to influence the seed for round r+1. Indeed, the user can pre-compute their credentials for round r+1 for each potential seed, and broadcast only the credential (among those with low enough score to be leader) that produces the most favorable seed. We consider an adversary who wishes to maximize the expected fraction of rounds in which an account they own is the leader. We show such an adversary always benefits from deviating from the intended protocol, regardless of the fraction of the stake controlled. We characterize the optimal strategy; first by proving the existence of optimal positive recurrent strategies whenever the adversary owns last than 3-5/2 ~38% of the stake. Then, we provide a Markov Decision Process formulation to compute the optimal strategy.","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":"123847345","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}
Gideon Amir, Itai Arieli, Galit Ashkenazi-Golan, R. Peretz
We study a model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state. It is known from Golub and Jackson [6] that under DeGroot [3] dynamics agents reach a consensus that is close to the state of the world when the network is large. The DeGroot dynamics, however, is highly non-robust and the presence of a single "stubborn agent" that does not adhere to the updating rule can sway the public consensus to any other value. We introduce a variant of DeGroot dynamics that we call 1/m-DeGroot. 1/m-DeGroot dynamics approximates standard DeGroot dynamics to the nearest rational number with m as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1/m-DeGroot dynamics is highly robust both to the presence of stubborn agents and to certain types of misspecifications.
{"title":"Granular DeGroot Dynamics -- a Model for Robust Naive Learning in Social Networks","authors":"Gideon Amir, Itai Arieli, Galit Ashkenazi-Golan, R. Peretz","doi":"10.1145/3490486.3538291","DOIUrl":"https://doi.org/10.1145/3490486.3538291","url":null,"abstract":"We study a model of opinion exchange in social networks where a state of the world is realized and every agent receives a zero-mean noisy signal of the realized state. It is known from Golub and Jackson [6] that under DeGroot [3] dynamics agents reach a consensus that is close to the state of the world when the network is large. The DeGroot dynamics, however, is highly non-robust and the presence of a single \"stubborn agent\" that does not adhere to the updating rule can sway the public consensus to any other value. We introduce a variant of DeGroot dynamics that we call 1/m-DeGroot. 1/m-DeGroot dynamics approximates standard DeGroot dynamics to the nearest rational number with m as its denominator and like the DeGroot dynamics it is Markovian and stationary. We show that in contrast to standard DeGroot dynamics, 1/m-DeGroot dynamics is highly robust both to the presence of stubborn agents and to certain types of misspecifications.","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":"124880329","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}
Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. (Examples include Angi, HomeAdvisor, Thumbtack and To8to.) This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy so as to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are determined by the providers and can depend on the platform's match recommendation policy. Despite the complications due to price endogeneity, an optimal match recommendation policy can be computed efficiently using stochastic subgradient descent. Under additional regularity conditions on the distribution of preferences, an optimal policy has a simple form: for each customer segment and each provider, the platform has a certain target on the rate that the provider is recommended to this segment. Any policy that achieves these targets is optimal. Finally, accounting for the endogeneity of prices is crucial: if the platform were to optimize its match recommendations while erroneously assuming that prices are exogenous, then the market is likely to get stuck at a strictly sub-optimal equilibrium, even if the platform were to continually re-optimize its match recommendation policy after prices re-equilibrate. Link to full paper: https://ssrn.com/abstract=4034950
{"title":"Optimal Match Recommendations in Two-sided Marketplaces with Endogenous Prices","authors":"Peng Shi","doi":"10.2139/ssrn.4034950","DOIUrl":"https://doi.org/10.2139/ssrn.4034950","url":null,"abstract":"Many two-sided marketplaces rely on match recommendations to help customers find suitable service providers at suitable prices. (Examples include Angi, HomeAdvisor, Thumbtack and To8to.) This paper develops a tractable methodology that a platform can use to optimize its match recommendation policy so as to maximize the total value generated by the platform while accounting for the endogeneity of transaction prices, which are determined by the providers and can depend on the platform's match recommendation policy. Despite the complications due to price endogeneity, an optimal match recommendation policy can be computed efficiently using stochastic subgradient descent. Under additional regularity conditions on the distribution of preferences, an optimal policy has a simple form: for each customer segment and each provider, the platform has a certain target on the rate that the provider is recommended to this segment. Any policy that achieves these targets is optimal. Finally, accounting for the endogeneity of prices is crucial: if the platform were to optimize its match recommendations while erroneously assuming that prices are exogenous, then the market is likely to get stuck at a strictly sub-optimal equilibrium, even if the platform were to continually re-optimize its match recommendation policy after prices re-equilibrate. Link to full paper: https://ssrn.com/abstract=4034950","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":"131222297","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}
Modern city governance relies heavily on crowdsourcing (or "co-production") to identify problems such as downed trees and power-lines. A major concern in these systems is that residents do not report problems at the same rates, leading to an inequitable allocation of government resources. However, measuring such under-reporting is a difficult statistical task, as, almost by definition, we do not observe incidents that are not reported. Thus, distinguishing between low reporting rates and low ground-truth incident rates is challenging. We develop a method to identify (heterogeneous) reporting rates, without using external (proxy) ground truth data. Our insight is that rates on duplicate reports about the same incident can be leveraged, to turn the question into a standard Poisson rate estimation task---even though the full incident reporting interval is also unobserved. We apply our method to over 100,000 resident reports made to the New York City Department of Parks and Recreation, finding that there are substantial spatial and socio-economic disparities in reporting rates, even after controlling for incident characteristics.
{"title":"Equity in Resident Crowdsourcing: Measuring Under-reporting without Ground Truth Data","authors":"Zhi Liu, Nikhil Garg","doi":"10.1145/3490486.3538283","DOIUrl":"https://doi.org/10.1145/3490486.3538283","url":null,"abstract":"Modern city governance relies heavily on crowdsourcing (or \"co-production\") to identify problems such as downed trees and power-lines. A major concern in these systems is that residents do not report problems at the same rates, leading to an inequitable allocation of government resources. However, measuring such under-reporting is a difficult statistical task, as, almost by definition, we do not observe incidents that are not reported. Thus, distinguishing between low reporting rates and low ground-truth incident rates is challenging. We develop a method to identify (heterogeneous) reporting rates, without using external (proxy) ground truth data. Our insight is that rates on duplicate reports about the same incident can be leveraged, to turn the question into a standard Poisson rate estimation task---even though the full incident reporting interval is also unobserved. We apply our method to over 100,000 resident reports made to the New York City Department of Parks and Recreation, finding that there are substantial spatial and socio-economic disparities in reporting rates, even after controlling for incident characteristics.","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":"132642362","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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