The Internet has become an essential part of the citizens' life and of the economy. In this online ecosystem, service providers collect large amounts of personal data about individuals and use it to offer services from which they derive high profits. Personal data therefore has clear intrinsic economic value, which is extensively exploited by online services. At the same time, users also benefit from this ecosystem by being granted free access to services but it is unclear whether this appropriately compensates them for the release of their personal data. As the amount of data collected by online services is exploding, users and organizations increasingly ask for more fair, transparent, and personalized compensations. This naturally raises the question how much is personal data worth?
{"title":"A cooperative game-theoretic approach to quantify the value of personal data in networks","authors":"Michela Chessa, P. Loiseau","doi":"10.1145/3106723.3106732","DOIUrl":"https://doi.org/10.1145/3106723.3106732","url":null,"abstract":"The Internet has become an essential part of the citizens' life and of the economy. In this online ecosystem, service providers collect large amounts of personal data about individuals and use it to offer services from which they derive high profits. Personal data therefore has clear intrinsic economic value, which is extensively exploited by online services. At the same time, users also benefit from this ecosystem by being granted free access to services but it is unclear whether this appropriately compensates them for the release of their personal data. As the amount of data collected by online services is exploding, users and organizations increasingly ask for more fair, transparent, and personalized compensations. This naturally raises the question how much is personal data worth?","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129856715","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}
A set of economic entities embedded in a network graph collaborate by opportunistically exchanging their resources to satisfy their dynamically generated needs. Under what conditions their collaboration leads to a sustainable economy? Which online policy can ensure a feasible resource exchange point will be attained, and what information is needed to implement it? Furthermore, assuming there are different resources and the entities have diverse production capabilities, which production policy each entity should employ in order to maximize the economy's sustainability? Importantly, can we design such policies that are also incentive compatible even when there is no a priori information about the entities' needs? We introduce a dynamic production scheduling and resource exchange model to capture this fundamental problem and provide answers to the above questions. Applications range from infrastructure sharing, trade and organization management, to social networks and sharing economy services.
{"title":"Dynamic policies for cooperative networked systems","authors":"G. Iosifidis, L. Tassiulas","doi":"10.1145/3106723.3106735","DOIUrl":"https://doi.org/10.1145/3106723.3106735","url":null,"abstract":"A set of economic entities embedded in a network graph collaborate by opportunistically exchanging their resources to satisfy their dynamically generated needs. Under what conditions their collaboration leads to a sustainable economy? Which online policy can ensure a feasible resource exchange point will be attained, and what information is needed to implement it? Furthermore, assuming there are different resources and the entities have diverse production capabilities, which production policy each entity should employ in order to maximize the economy's sustainability? Importantly, can we design such policies that are also incentive compatible even when there is no a priori information about the entities' needs? We introduce a dynamic production scheduling and resource exchange model to capture this fundamental problem and provide answers to the above questions. Applications range from infrastructure sharing, trade and organization management, to social networks and sharing economy services.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115346920","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}
A large body of existing work in social science as well as computer science attempts to infer preferences of individuals from the actions they take. This includes research areas such as industrial organization [4], marketing [1], political science [12], analysis of auctions [3], recommender systems [8], search engine ranking [9], and many others. The workhorse model used either implicitly or explicitly in these disparate literatures is the rational choice model.
{"title":"Learning context-dependent preferences from raw data","authors":"A. Peysakhovich, J. Ugander","doi":"10.1145/3106723.3106731","DOIUrl":"https://doi.org/10.1145/3106723.3106731","url":null,"abstract":"A large body of existing work in social science as well as computer science attempts to infer preferences of individuals from the actions they take. This includes research areas such as industrial organization [4], marketing [1], political science [12], analysis of auctions [3], recommender systems [8], search engine ranking [9], and many others. The workhorse model used either implicitly or explicitly in these disparate literatures is the rational choice model.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130838358","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}
{"title":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","authors":"Vincent Conitzer, R. Guérin","doi":"10.1145/3106723","DOIUrl":"https://doi.org/10.1145/3106723","url":null,"abstract":"","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123512668","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}
Social goods are goods that grant value not only to their owners but also to the owners' surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others' purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales. To study this problem, we consider a setting with a single type of good, of unlimited supply, and a set of n agents; each agent i ϵ [n] has a non-negative valuation vi for purchasing the good, drawn independently from a distribution Fi. We denote the product distribution by F = Xiϵ[n]Fi. An agent i who purchases the good derives value vi from it. If an agent does not purchase the good, but the good is purchased by others, then this agent derives only a fraction of her value, depending on the set of agents and the type of externality the good exhibits on the agent. This type of externality is captured in our model by an externality function xi : 2[n] → [0, 1], where xi(S) denotes the fraction of vi an agent i derives when the good is purchased by the set of agents S. We assume that xi is publicly known (as it captures the agent's externalities), monotonically nondecreasing and normalized; i.e., for every T ⊆ S, xi(T) ≤ xi(S), xi(∅) = 0, and xi(S) = 1 whenever i ϵ S. We consider three structures of the function xi, corresponding to three types of externalities of social goods. (a) Full externalities (commonly known as "public goods"): in this scenario all agents derive their entire value if the good is purchased by any agent. Therefore, xi(S) = 1 if and only if S ≠ ∅. This model captures goods that are non-excludable, such as a coffee machine in a shared office. A special case of this scenario, where valuations are independently and identically distributed, has been studied in [1]. (b) Status-based externalities: in this scenario, agent i's "social status" is captured by some discount factor wi ϵ [0, 1], which corresponds to the fraction of the value agent i derives from a good when purchased by another party. This model captures settings that exhibit asymmetry with respect to the benefit different agents derive from goods they do not own (e.g., a fast food restaurant might benefit from any traffic in the shopp
{"title":"Pricing social goods","authors":"Alon Eden, Tomer Ezra, M. Feldman","doi":"10.1145/3106723.3106733","DOIUrl":"https://doi.org/10.1145/3106723.3106733","url":null,"abstract":"Social goods are goods that grant value not only to their owners but also to the owners' surroundings, be it their families, friends or office mates. The benefit a non-owner derives from the good is affected by many factors, including the type of the good, its availability, and the social status of the non-owner. Depending on the magnitude of the benefit and on the price of the good, a potential buyer might stay away from purchasing the good, hoping to free ride on others' purchases. A revenue-maximizing seller who sells social goods must take these considerations into account when setting prices for the good. The literature on optimal pricing has advanced considerably over the last decade, but little is known about optimal pricing schemes for selling social goods. In this paper, we conduct a systematic study of revenue-maximizing pricing schemes for social goods: we introduce a Bayesian model for this scenario, and devise nearly-optimal pricing schemes for various types of externalities, both for simultaneous sales and for sequential sales. To study this problem, we consider a setting with a single type of good, of unlimited supply, and a set of n agents; each agent i ϵ [n] has a non-negative valuation vi for purchasing the good, drawn independently from a distribution Fi. We denote the product distribution by F = Xiϵ[n]Fi. An agent i who purchases the good derives value vi from it. If an agent does not purchase the good, but the good is purchased by others, then this agent derives only a fraction of her value, depending on the set of agents and the type of externality the good exhibits on the agent. This type of externality is captured in our model by an externality function xi : 2[n] → [0, 1], where xi(S) denotes the fraction of vi an agent i derives when the good is purchased by the set of agents S. We assume that xi is publicly known (as it captures the agent's externalities), monotonically nondecreasing and normalized; i.e., for every T ⊆ S, xi(T) ≤ xi(S), xi(∅) = 0, and xi(S) = 1 whenever i ϵ S. We consider three structures of the function xi, corresponding to three types of externalities of social goods. (a) Full externalities (commonly known as \"public goods\"): in this scenario all agents derive their entire value if the good is purchased by any agent. Therefore, xi(S) = 1 if and only if S ≠ ∅. This model captures goods that are non-excludable, such as a coffee machine in a shared office. A special case of this scenario, where valuations are independently and identically distributed, has been studied in [1]. (b) Status-based externalities: in this scenario, agent i's \"social status\" is captured by some discount factor wi ϵ [0, 1], which corresponds to the fraction of the value agent i derives from a good when purchased by another party. This model captures settings that exhibit asymmetry with respect to the benefit different agents derive from goods they do not own (e.g., a fast food restaurant might benefit from any traffic in the shopp","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120948242","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}
With the increase of market competition, many Mobile Network Operators (MNOs) are introducing various innovative data mechanisms as complementary to the "traditional" data plan to attract more subscribers and increase profits. The traditional data plan is a three-part tariff, where a subscriber pays the MNO a lump-sum subscription fee for the data usage up to a data cap, and then pays a linear usage-based fee for the data usage over the data cap. Due to the stochastic nature of users' data usage, it is often difficult for a user to decide the best choice of monthly data cap to achieve an optimal balance between the data waste (when the actual data usage is lower than the data cap) and the overage usage (when the actual data usage exceeds the data cap). Therefore, the MNOs are investigating various innovative data mechanisms that can give users more flexibility and attract more subscriptions.
{"title":"A contract-theoretic design of mobile data plan with time flexibility","authors":"Zhiyuan Wang, Lin Gao, Jianwei Huang","doi":"10.1145/3106723.3106729","DOIUrl":"https://doi.org/10.1145/3106723.3106729","url":null,"abstract":"With the increase of market competition, many Mobile Network Operators (MNOs) are introducing various innovative data mechanisms as complementary to the \"traditional\" data plan to attract more subscribers and increase profits. The traditional data plan is a three-part tariff, where a subscriber pays the MNO a lump-sum subscription fee for the data usage up to a data cap, and then pays a linear usage-based fee for the data usage over the data cap. Due to the stochastic nature of users' data usage, it is often difficult for a user to decide the best choice of monthly data cap to achieve an optimal balance between the data waste (when the actual data usage is lower than the data cap) and the overage usage (when the actual data usage exceeds the data cap). Therefore, the MNOs are investigating various innovative data mechanisms that can give users more flexibility and attract more subscriptions.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"124 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128109995","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 sharing denotes the practice of sharing a car such that more than one person travels in a car during a journey. Sharing rides was traditionally restricted to family members and close friends or long-distance journeys scheduled well before the intended time of departure. Only the emergence of mobile computing technologies and GPS location services in combination with electronic payments and online reputation systems provided for the technological cornerstones to make on-demand short-distance ride sharing among strangers viable. Typically individuals enter their trip details on an online platform1 which then facilitates the matching of riders with cars and drivers - and within minutes the individual's trip commences. Such platforms must attract both, supply and demand for rides.
{"title":"Drivers, riders and service providers: the impact of the sharing economy on mobility","authors":"S. Benjaafar, Harald Bernhard, C. Courcoubetis","doi":"10.1145/3106723.3106724","DOIUrl":"https://doi.org/10.1145/3106723.3106724","url":null,"abstract":"Ride sharing denotes the practice of sharing a car such that more than one person travels in a car during a journey. Sharing rides was traditionally restricted to family members and close friends or long-distance journeys scheduled well before the intended time of departure. Only the emergence of mobile computing technologies and GPS location services in combination with electronic payments and online reputation systems provided for the technological cornerstones to make on-demand short-distance ride sharing among strangers viable. Typically individuals enter their trip details on an online platform1 which then facilitates the matching of riders with cars and drivers - and within minutes the individual's trip commences. Such platforms must attract both, supply and demand for rides.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121094545","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}
Mohammad Mahdi Khalili, Parinaz Naghizadeh Ardabili, M. Liu
Cyber insurance is a method for risk transfer but may or may not improve the state of network security. In this work, we consider a profit-maximizing insurer with voluntarily participating insureds. We are particularly interested in two features of cybersecurity and their impact on the contract design problem. The first is the interdependent nature of cybersecurity, whereby one entity's state of security depends on its own effort and others' effort. The second is our ability to perform accurate quantitative assessment of security posture at a firm level by combining recent advances in Internet measurement and machine learning techniques. We observe that security interdependency leads to a "profit opportunity" for the insurer, created by the inefficient effort levels exerted by agents who do not account for risk externalities when insurance is not available; this is in addition to risk transfer that an insurer profits from. Security pre-screening allows the insurer to take advantage of this opportunity by designing appropriate contracts which incentivize agents to increase their effort levels, allowing the insurer to effectively "sell commitment" to interdependent agents, in addition to risk transfer. We identify conditions under which this type of contracts lead to an improved state of network security.
{"title":"Designing cyber insurance policies in the presence of security interdependence","authors":"Mohammad Mahdi Khalili, Parinaz Naghizadeh Ardabili, M. Liu","doi":"10.1145/3106723.3106730","DOIUrl":"https://doi.org/10.1145/3106723.3106730","url":null,"abstract":"Cyber insurance is a method for risk transfer but may or may not improve the state of network security. In this work, we consider a profit-maximizing insurer with voluntarily participating insureds. We are particularly interested in two features of cybersecurity and their impact on the contract design problem. The first is the interdependent nature of cybersecurity, whereby one entity's state of security depends on its own effort and others' effort. The second is our ability to perform accurate quantitative assessment of security posture at a firm level by combining recent advances in Internet measurement and machine learning techniques. We observe that security interdependency leads to a \"profit opportunity\" for the insurer, created by the inefficient effort levels exerted by agents who do not account for risk externalities when insurance is not available; this is in addition to risk transfer that an insurer profits from. Security pre-screening allows the insurer to take advantage of this opportunity by designing appropriate contracts which incentivize agents to increase their effort levels, allowing the insurer to effectively \"sell commitment\" to interdependent agents, in addition to risk transfer. We identify conditions under which this type of contracts lead to an improved state of network security.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129559762","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 problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters.
{"title":"Simple pricing schemes for the cloud","authors":"Ian A. Kash, P. Key, Warut Suksompong","doi":"10.1145/3106723.3106727","DOIUrl":"https://doi.org/10.1145/3106723.3106727","url":null,"abstract":"The problem of pricing the cloud has attracted much recent attention due to the widespread use of cloud computing and cloud services. From a theoretical perspective, several mechanisms that provide strong efficiency or fairness guarantees and desirable incentive properties have been designed. However, these mechanisms often rely on a rigid model, with several parameters needing to be precisely known in order for the guarantees to hold. In this paper, we consider a stochastic model and show that it is possible to obtain good welfare and revenue guarantees with simple mechanisms that do not make use of the information on some of these parameters.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"97 12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131845360","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 consider the public goods resource allocation problem (also known as Lindahl allocation) of determining the level of an infinitely divisible public good with P features, that is shared between strategic agents. We present an efficient mechanism, i.e., a mechanism that produces a unique Nash equilibrium (NE), with the corresponding allocation at NE being the social welfare maximizing allocation and taxes at NE being budget-balanced. The main contribution of this paper is that the designed mechanism has two properties, which have not been addressed together in the literature, and aim to make it practically implementable. First, we assume that agents can communicate only through a given network and thus the designed mechanism obeys the agents' informational constraints. This means that each agent's outcome through the mechanism can be determined by only the messages of his/her neighbors. Second, it is guaranteed that agents can learn the NE induced by the mechanism through repeated play when each agent selects a learning strategy from within the "adaptive best-response" dynamics class. This is a class of adaptive learning strategies that includes well-known dynamics such as Cournot best-response, k-period best-response and fictitious play, among others. The convergence result is a consequence of the fact that the best-response of the induced game is a contraction mapping. Finally, we present a numerical study of convergence to NE, for two different underlying communication graphs and two different learning dynamics within the ABR class.
{"title":"A distributed mechanism for public goods allocation with dynamic learning guarantees","authors":"Abhinav Sinha, A. Anastasopoulos","doi":"10.1145/3106723.3106725","DOIUrl":"https://doi.org/10.1145/3106723.3106725","url":null,"abstract":"In this paper we consider the public goods resource allocation problem (also known as Lindahl allocation) of determining the level of an infinitely divisible public good with P features, that is shared between strategic agents. We present an efficient mechanism, i.e., a mechanism that produces a unique Nash equilibrium (NE), with the corresponding allocation at NE being the social welfare maximizing allocation and taxes at NE being budget-balanced. The main contribution of this paper is that the designed mechanism has two properties, which have not been addressed together in the literature, and aim to make it practically implementable. First, we assume that agents can communicate only through a given network and thus the designed mechanism obeys the agents' informational constraints. This means that each agent's outcome through the mechanism can be determined by only the messages of his/her neighbors. Second, it is guaranteed that agents can learn the NE induced by the mechanism through repeated play when each agent selects a learning strategy from within the \"adaptive best-response\" dynamics class. This is a class of adaptive learning strategies that includes well-known dynamics such as Cournot best-response, k-period best-response and fictitious play, among others. The convergence result is a consequence of the fact that the best-response of the induced game is a contraction mapping. Finally, we present a numerical study of convergence to NE, for two different underlying communication graphs and two different learning dynamics within the ABR class.","PeriodicalId":130519,"journal":{"name":"Proceedings of the 12th workshop on the Economics of Networks, Systems and Computation","volume":"215 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133538323","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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