In a $textit{satisficing equilibrium}$ each agent plays one of their $k$ best pure actions, but not necessarily their best action. We show that satisficing equilibria in which agents play only their best or second-best action exist in almost all games. In fact, in almost all games, there exist satisficing equilibria in which all but one agent best-respond and the remaining agent plays at least a second-best action. By contrast, more than one third of games possess no pure Nash equilibrium. In addition to providing static foundations for satisficing equilibria, we show that a parsimonious dynamic converges to satisficing equilibria in almost all games. We apply our results to market design and show that a mediator who can control a single agent can enforce stability in most games. Finally, we use our results to study the existence of $epsilon$-equilibria.
{"title":"Satisficing Equilibrium","authors":"Bary S. R. Pradelski, Bassel Tarbush","doi":"arxiv-2409.00832","DOIUrl":"https://doi.org/arxiv-2409.00832","url":null,"abstract":"In a $textit{satisficing equilibrium}$ each agent plays one of their $k$\u0000best pure actions, but not necessarily their best action. We show that\u0000satisficing equilibria in which agents play only their best or second-best\u0000action exist in almost all games. In fact, in almost all games, there exist\u0000satisficing equilibria in which all but one agent best-respond and the\u0000remaining agent plays at least a second-best action. By contrast, more than one\u0000third of games possess no pure Nash equilibrium. In addition to providing\u0000static foundations for satisficing equilibria, we show that a parsimonious\u0000dynamic converges to satisficing equilibria in almost all games. We apply our\u0000results to market design and show that a mediator who can control a single\u0000agent can enforce stability in most games. Finally, we use our results to study\u0000the existence of $epsilon$-equilibria.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197108","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}
This paper presents a new rationale for a self-interested economic elite voluntarily extending property rights. When agents make endogenous investment decisions, there is a commitment problem. Ex-post, the elite face strong incentives to expropriate investments from the non-elite (who don't have property rights), which dissuades investment. Extending property rights to new groups can resolve this problem, even for those not given property rights, by making public good provision more attractive to the elite. Unlike other models of franchise extensions, extending property rights in this paper does not involve the elite ceding control to others. Rather, it changes the incentives they face. Additionally, adding identity groups to the model shows that an elite faces weaker incentives to resolve the commitment problem when it is part of a minority identity -- identity fragmentation makes it harder for a society to extend property rights.
{"title":"Why do elites extend property rights: unlocking investment and the switch to public goods","authors":"Alastair Langtry","doi":"arxiv-2408.17335","DOIUrl":"https://doi.org/arxiv-2408.17335","url":null,"abstract":"This paper presents a new rationale for a self-interested economic elite\u0000voluntarily extending property rights. When agents make endogenous investment\u0000decisions, there is a commitment problem. Ex-post, the elite face strong\u0000incentives to expropriate investments from the non-elite (who don't have\u0000property rights), which dissuades investment. Extending property rights to new\u0000groups can resolve this problem, even for those not given property rights, by\u0000making public good provision more attractive to the elite. Unlike other models\u0000of franchise extensions, extending property rights in this paper does not\u0000involve the elite ceding control to others. Rather, it changes the incentives\u0000they face. Additionally, adding identity groups to the model shows that an\u0000elite faces weaker incentives to resolve the commitment problem when it is part\u0000of a minority identity -- identity fragmentation makes it harder for a society\u0000to extend property rights.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"170 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197110","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 how uncertain technologies should be robustly regulated. An agent develops a new technology and, while privately learning about its harms and benefits, continually chooses whether to continue development. A principal, uncertain about what the agent might learn, chooses among dynamic mechanisms (e.g., paths of taxes or subsidies) to influence the agent's choices in different states. We show that learning robust mechanisms -- those which deliver the highest payoff guarantee across all learning processes -- are simple and resemble `regulatory sandboxes' consisting of zero marginal tax on R&D which keeps the agent maximally sensitive to new information up to a hard quota, upon which the agent turns maximally insensitive. Robustness is important: we characterize the worst-case learning process under non-robust mechanisms and show that they induce growing but weak optimism which can deliver unboundedly poor principal payoffs; hard quotas safeguard against this. If the regulator also learns, adaptive hard quotas are robustly optimal which highlights the importance of expertise in regulation.
{"title":"Robust Technology Regulation","authors":"Andrew Koh, Sivakorn Sanguanmoo","doi":"arxiv-2408.17398","DOIUrl":"https://doi.org/arxiv-2408.17398","url":null,"abstract":"We analyze how uncertain technologies should be robustly regulated. An agent\u0000develops a new technology and, while privately learning about its harms and\u0000benefits, continually chooses whether to continue development. A principal,\u0000uncertain about what the agent might learn, chooses among dynamic mechanisms\u0000(e.g., paths of taxes or subsidies) to influence the agent's choices in\u0000different states. We show that learning robust mechanisms -- those which\u0000deliver the highest payoff guarantee across all learning processes -- are\u0000simple and resemble `regulatory sandboxes' consisting of zero marginal tax on\u0000R&D which keeps the agent maximally sensitive to new information up to a hard\u0000quota, upon which the agent turns maximally insensitive. Robustness is\u0000important: we characterize the worst-case learning process under non-robust\u0000mechanisms and show that they induce growing but weak optimism which can\u0000deliver unboundedly poor principal payoffs; hard quotas safeguard against this.\u0000If the regulator also learns, adaptive hard quotas are robustly optimal which\u0000highlights the importance of expertise in regulation.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225110","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 maxmin approach to distributional robustness evaluates each mechanism according to its payoff guarantee over all priors in an ambiguity set. We propose a refinement: the guarantee must be approximately satisfied at priors near the ambiguity set (in the weak topology). We call such a guarantee robust. The payoff guarantees from some maxmin-optimal mechanisms in the literature are not robust. We show, however, that over certain standard ambiguity sets (such as continuous moment sets), every mechanism's payoff guarantee is robust. We give a behavioral characterization of our refined robustness notion by imposing a new continuity axiom on maxmin preferences.
{"title":"Robust Robustness","authors":"Ian Ball, Deniz Kattwinkel","doi":"arxiv-2408.16898","DOIUrl":"https://doi.org/arxiv-2408.16898","url":null,"abstract":"The maxmin approach to distributional robustness evaluates each mechanism\u0000according to its payoff guarantee over all priors in an ambiguity set. We\u0000propose a refinement: the guarantee must be approximately satisfied at priors\u0000near the ambiguity set (in the weak topology). We call such a guarantee robust.\u0000The payoff guarantees from some maxmin-optimal mechanisms in the literature are\u0000not robust. We show, however, that over certain standard ambiguity sets (such\u0000as continuous moment sets), every mechanism's payoff guarantee is robust. We\u0000give a behavioral characterization of our refined robustness notion by imposing\u0000a new continuity axiom on maxmin preferences.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225136","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 design the profit-maximizing mechanism to sell an excludable and non-rival good with network effects. Buyers have heterogeneous private values that depend on how many others also consume the good. We characterize an algorithm that implements the optimal allocation in dominant strategies. We apply our insights to digital content creation, and we are able to rationalize features seen in monetization schemes in this industry such as voluntary contributions, community subsidies, and exclusivity bids.
{"title":"Monetizing digital content with network effects: A mechanism-design approach","authors":"Vincent Meisner, Pascal Pillath","doi":"arxiv-2408.15196","DOIUrl":"https://doi.org/arxiv-2408.15196","url":null,"abstract":"We design the profit-maximizing mechanism to sell an excludable and non-rival\u0000good with network effects. Buyers have heterogeneous private values that depend\u0000on how many others also consume the good. We characterize an algorithm that\u0000implements the optimal allocation in dominant strategies. We apply our insights\u0000to digital content creation, and we are able to rationalize features seen in\u0000monetization schemes in this industry such as voluntary contributions,\u0000community subsidies, and exclusivity bids.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"184 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142225134","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 measure of learning efficiency for non-finite state spaces. We characterize the complexity of a learning problem by the metric entropy of its state space. We then describe how learning efficiency is determined by this measure of complexity. This is, then, applied to two models where agents learn high-dimensional states.
{"title":"The Asymptotic Cost of Complexity","authors":"Martin W Cripps","doi":"arxiv-2408.14949","DOIUrl":"https://doi.org/arxiv-2408.14949","url":null,"abstract":"We propose a measure of learning efficiency for non-finite state spaces. We\u0000characterize the complexity of a learning problem by the metric entropy of its\u0000state space. We then describe how learning efficiency is determined by this\u0000measure of complexity. This is, then, applied to two models where agents learn\u0000high-dimensional states.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197111","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 introduce a class of super heavy-tailed distributions and establish the inequality that any weighted average of independent and identically distributed super heavy-tailed random variables stochastically dominates one such random variable. We show that many commonly used extremely heavy-tailed (i.e., infinite-mean) distributions, such as the Pareto, Fr'echet, and Burr distributions, belong to the class of super heavy-tailed distributions. The established stochastic dominance relation is further generalized to allow negatively dependent or non-identically distributed random variables. In particular, the weighted average of non-identically distributed random variables stochastically dominates their distribution mixtures. Applications of these results in portfolio diversification, goods bundling, and inventory management are discussed. Remarkably, in the presence of super heavy-tailedness, the results that hold for finite-mean models in these applications are flipped.
{"title":"Stochastic dominance for super heavy-tailed random variables","authors":"Yuyu Chen, Seva Shneer","doi":"arxiv-2408.15033","DOIUrl":"https://doi.org/arxiv-2408.15033","url":null,"abstract":"We introduce a class of super heavy-tailed distributions and establish the\u0000inequality that any weighted average of independent and identically distributed\u0000super heavy-tailed random variables stochastically dominates one such random\u0000variable. We show that many commonly used extremely heavy-tailed (i.e.,\u0000infinite-mean) distributions, such as the Pareto, Fr'echet, and Burr\u0000distributions, belong to the class of super heavy-tailed distributions. The\u0000established stochastic dominance relation is further generalized to allow\u0000negatively dependent or non-identically distributed random variables. In\u0000particular, the weighted average of non-identically distributed random\u0000variables stochastically dominates their distribution mixtures. Applications of\u0000these results in portfolio diversification, goods bundling, and inventory\u0000management are discussed. Remarkably, in the presence of super\u0000heavy-tailedness, the results that hold for finite-mean models in these\u0000applications are flipped.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197112","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}
Immersive technologies such as Metaverse, AR, and VR are at a crossroads, with many actors pondering their adoption and potential sectors interested in integration. The cultural and tourism industries are particularly impacted, facing significant pressure to make decisions that could shape their future landscapes. Stakeholders' perceptions play a crucial role in this process, influencing the speed and extent of technology adoption. As immersive technologies promise to revolutionize experiences, stakeholders in these fields weigh the benefits and challenges of embracing such innovations. The current choices will likely determine the trajectory of cultural preservation and tourism enhancement, potentially transforming how we engage with history, art, and travel. Starting from a decomposition of stakeholders' perceptions into principal components using Q-methodology, this article employs an evolutionary game model to attempt to map possible scenarios and highlight potential decision-making trajectories. The proposed approach highlights how evolutionary dynamics lead to identifying a dominant long-term strategy that emerges from the complex system of coexistence among various stakeholders.
{"title":"Evolutionary Game Dynamics Applied to Strategic Adoption of Immersive Technologies in Cultural Heritage and Tourism","authors":"Gioacchino Fazio, Stefano Fricano, Claudio Pirrone","doi":"arxiv-2409.06720","DOIUrl":"https://doi.org/arxiv-2409.06720","url":null,"abstract":"Immersive technologies such as Metaverse, AR, and VR are at a crossroads,\u0000with many actors pondering their adoption and potential sectors interested in\u0000integration. The cultural and tourism industries are particularly impacted,\u0000facing significant pressure to make decisions that could shape their future\u0000landscapes. Stakeholders' perceptions play a crucial role in this process,\u0000influencing the speed and extent of technology adoption. As immersive\u0000technologies promise to revolutionize experiences, stakeholders in these fields\u0000weigh the benefits and challenges of embracing such innovations. The current\u0000choices will likely determine the trajectory of cultural preservation and\u0000tourism enhancement, potentially transforming how we engage with history, art,\u0000and travel. Starting from a decomposition of stakeholders' perceptions into\u0000principal components using Q-methodology, this article employs an evolutionary\u0000game model to attempt to map possible scenarios and highlight potential\u0000decision-making trajectories. The proposed approach highlights how evolutionary\u0000dynamics lead to identifying a dominant long-term strategy that emerges from\u0000the complex system of coexistence among various stakeholders.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197126","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 Terao [24], Hiroaki Terao defined and studied "admissible map", which is a generalization of "social welfare function" in the context of hyperplane arrangements. Using this, he proved a generalized Arrow's Impossibility Theorem using combinatorial arguments. This paper provides another proof of this generalized Arrow's Impossibility Theorem, using the idea of algebraic topology.
{"title":"A topological proof of Terao's generalized Arrow's Impossibility Theorem","authors":"Takuma Okura","doi":"arxiv-2408.14263","DOIUrl":"https://doi.org/arxiv-2408.14263","url":null,"abstract":"In Terao [24], Hiroaki Terao defined and studied \"admissible map\", which is a\u0000generalization of \"social welfare function\" in the context of hyperplane\u0000arrangements. Using this, he proved a generalized Arrow's Impossibility Theorem\u0000using combinatorial arguments. This paper provides another proof of this\u0000generalized Arrow's Impossibility Theorem, using the idea of algebraic\u0000topology.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197114","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 persuasion policy successfully persuades an agent to pick a particular action only if the information is designed in a manner that convinces the agent that it is in their best interest to pick that action. Thus, it is natural to ask, what makes the agent trust the persuader's suggestion? We study a Bayesian persuasion interaction between a sender and a receiver where the sender has access to private information and the receiver attempts to recover this information from messages sent by the sender. The sender crafts these messages in an attempt to maximize its utility which depends on the source symbol and the symbol recovered by the receiver. Our goal is to characterize the textit{Stackelberg game value}, and the amount of true information revealed by the sender during persuasion. We find that the SGV is given by the optimal value of a textit{linear program} on probability distributions constrained by certain textit{trust constraints}. These constraints encode that any signal in a persuasion strategy must contain more truth than untruth and thus impose a fundamental bound on the extent of obfuscation a sender can perform. We define textit{informativeness} of the sender as the minimum expected number of symbols truthfully revealed by the sender in any accumulation point of a sequence of $varepsilon$-equilibrium persuasion strategies, and show that it is given by another linear program. Informativeness is a fundamental bound on the amount of information the sender must reveal to persuade a receiver. Closed form expressions for the SGV and the informativeness are presented for structured utility functions. This work generalizes our previous work where the sender and the receiver were constrained to play only deterministic strategies and a similar notion of informativeness was characterized. Comparisons between the previous and current notions are discussed.
只有当信息的设计方式能让代理人相信选择某项行动最符合他们的利益时，说服政策才能成功地说服代理人选择该行动。因此，我们自然会问，是什么让代理人相信劝说者的建议？我们研究了发送者和接收者之间的贝叶斯说服互动，在这种互动中，发送者可以获取私人信息，而接收者则试图从发送者发送的信息中恢复这些信息。发送方精心制作这些信息，试图使其效用最大化，而效用取决于源符号和接收方恢复的符号。我们的目标是描述 "斯塔克尔伯格博弈值"（textit{Stackelberg game value}）以及发送者在说服过程中透露的真实信息量。我们发现，SGV 是由textit{信任约束}约束下的概率分布上的textit{线性程序}的最优值给出的。这些约束表明，任何说服策略中的信号都必须包含更多的真实信息，而不是虚假信息，因此对发送者所能进行的混淆程度施加了基本约束。我们将发送者的文本信息定义为：在$varepsilon$均衡说服策略序列的任意累积点中，发送者如实透露的符号的最小预期数量，并证明它是由另一个线性规划给出的。信息量是发送者为说服接收者而必须披露的信息量的基本约束。本文给出了结构化效用函数的 SGV 和信息量的封闭表达式。这项工作推广了我们以前的工作，在以前的工作中，发送方和接收方被限制只能采取确定性策略，而信息量的概念与此类似。我们还讨论了以前的概念和现在的概念之间的比较。
{"title":"Informativeness and Trust in Bayesian Persuasion","authors":"Reema Deori, Ankur A. Kulkarni","doi":"arxiv-2408.13822","DOIUrl":"https://doi.org/arxiv-2408.13822","url":null,"abstract":"A persuasion policy successfully persuades an agent to pick a particular\u0000action only if the information is designed in a manner that convinces the agent\u0000that it is in their best interest to pick that action. Thus, it is natural to\u0000ask, what makes the agent trust the persuader's suggestion? We study a Bayesian\u0000persuasion interaction between a sender and a receiver where the sender has\u0000access to private information and the receiver attempts to recover this\u0000information from messages sent by the sender. The sender crafts these messages\u0000in an attempt to maximize its utility which depends on the source symbol and\u0000the symbol recovered by the receiver. Our goal is to characterize the\u0000textit{Stackelberg game value}, and the amount of true information revealed by\u0000the sender during persuasion. We find that the SGV is given by the optimal\u0000value of a textit{linear program} on probability distributions constrained by\u0000certain textit{trust constraints}. These constraints encode that any signal in\u0000a persuasion strategy must contain more truth than untruth and thus impose a\u0000fundamental bound on the extent of obfuscation a sender can perform. We define\u0000textit{informativeness} of the sender as the minimum expected number of\u0000symbols truthfully revealed by the sender in any accumulation point of a\u0000sequence of $varepsilon$-equilibrium persuasion strategies, and show that it\u0000is given by another linear program. Informativeness is a fundamental bound on\u0000the amount of information the sender must reveal to persuade a receiver. Closed\u0000form expressions for the SGV and the informativeness are presented for\u0000structured utility functions. This work generalizes our previous work where the\u0000sender and the receiver were constrained to play only deterministic strategies\u0000and a similar notion of informativeness was characterized. Comparisons between\u0000the previous and current notions are discussed.","PeriodicalId":501188,"journal":{"name":"arXiv - ECON - Theoretical Economics","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142197115","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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