The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from...The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.展开更多
Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual inform...Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.展开更多
The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomen...The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomenal aspects of phenomenal consciousness with certain representational content. Pain is often thought to be a counterexample torepresentationalism. However, current objections from this perspective mostly focus on the second step and try to show that pains have some special qualities that representational content cannot explain. This paper objects to representationalism with regard to pain （that pain is not representation） by way of a focus on the first step. First, it shows that by borrowing the notion of ＂representation＂ from the causal co-variation theory of representation, representationalists are not able to demonstrate that pain is representation. Second, by laying out some well-accepted criteria for what counts as representation, it argues that pains do not satisfy them. Thus, pain is not representation.展开更多
基金Supported by the National Natural Science Foundation of China under Grant Nos.7110317971102129+1 种基金11121403by Program for Young Innovative Research Team in China University of Political Science and Law
文摘The recent financial crisis highlights the inherent weaknesses of the financial market. To explore the mechanism that maintains the financial market as a system, we study the interactions of U.S. financial market from the network perspective. Applied with conditional Granger causality network analysis, network density, in-degree and out-degree rankings are important indicators to analyze the conditional causal relationships among financial agents, and further to assess the stability of U.S. financial systems. It is found that the topological structure of G-causality network in U.S. financial market changed in different stages over the last decade, especially during the recent global financial crisis. Network density of the G-causality model is much higher during the period of 2007-2009 crisis stage, and it reaches the peak value in 2008, the most turbulent time in the crisis. Ranked by in-degrees and out-degrees, insurance companies are listed in the top of 68 financial institutions during the crisis. They act as the hubs which are more easily influenced by other financial institutions and simultaneously influence others during the global financial disturbance.
基金supported by the National Natural Science Foundation of China under Grant Nos.60972150, 10926197,61201323
文摘Detection and clarification of cause-effect relationships among variables is an important problem in time series analysis.This paper provides a method that employs both mutual information and conditional mutual information to identify the causal structure of multivariate time series causal graphical models.A three-step procedure is developed to learn the contemporaneous and the lagged causal relationships of time series causal graphs.Contrary to conventional constraint-based algorithm, the proposed algorithm does not involve any special kinds of distribution and is nonparametric.These properties are especially appealing for inference of time series causal graphs when the prior knowledge about the data model is not available.Simulations and case analysis demonstrate the effectiveness of the method.
文摘The argument given by strong representationalists about phenomenal consciousness usually has two steps. The first is to identify all phenomenal consciousness with representation. The second is to identify all phenomenal aspects of phenomenal consciousness with certain representational content. Pain is often thought to be a counterexample torepresentationalism. However, current objections from this perspective mostly focus on the second step and try to show that pains have some special qualities that representational content cannot explain. This paper objects to representationalism with regard to pain （that pain is not representation） by way of a focus on the first step. First, it shows that by borrowing the notion of ＂representation＂ from the causal co-variation theory of representation, representationalists are not able to demonstrate that pain is representation. Second, by laying out some well-accepted criteria for what counts as representation, it argues that pains do not satisfy them. Thus, pain is not representation.
