Uniform Topologies on Types

We study the robustness of interim correlated rationalizability to perturbations of higher-order beliefs. We introduce a new metric topology on the universal type space, called uniform weak topology, under which two types are close if they have similar first-order beliefs, attach similar probabilities to other players having similar first-order beliefs, and so on, where the degree of similarity is uniform over the levels of the belief hierarchy. This topology generalizes the now classic notion of proximity to common knowledge based on common p-beliefs (Monderer and Samet (1989)). We show that convergence in the uniform weak topology implies convergence in the uniform strategic topology (Dekel, Fudenberg, and Morris (2006)). Moreover, when the limit is a finite type, uniform-weak convergence is also a necessary condition for convergence in the strategic topology. Finally, we show that the set of finite types is nowhere dense under the uniform strategic topology. Thus, our results shed light on the connection between similarity of beliefs and similarity of behaviors in games.Rationalizability, Incomplete information, Higher-order beliefs, Strategic topology, Electronic mail game

Uniform topologies on types

We study the robustness of interim correlated rationalizability to perturbations of higher-order beliefs. We introduce a new metric topology on the universal type space, called uniform weak topology, under which two types are close if they have similar first-order beliefs, attach similar probabilities to other players having similar first-order beliefs, and so on, where the degree of similarity is uniform over the levels of the belief hierarchy. This topology generalizes the now classic notion of proximity to common knowledge based on common p-beliefs (Monderer and Samet 1989). We show that convergence in the uniform weak topology implies convergence in the uniform strategic topology (Dekel, Fudenberg, and Morris 2006). Moreover, when the limit is a finite type, uniform-weak convergence is also a necessary condition for convergence in the strategic topology. Finally, we show that the set of finite types is nowhere dense under the uniform strategic topology. Thus, our results shed light on the connection between similarity of beliefs and similarity of behaviors in games.Rationalizability, incomplete information, higher-order beliefs, strategic topology, electronic mail game

Configuring innovative societies: the crossvergent role of cultural and institutional varieties

The study aims to explore why some societies are more innovative than others in high-technology sectors. Following a crossvergence perspective, we generate nine causal conditions by accommodating both cultural and institutional varieties: uncertainty avoidance, masculinity, individualism and power distance as culture indicators, and union density, skill development, market capitalization to credit, prevalence of cluster and state dominance as institutional indicators. Applying the configurational approach, we conducted fuzzy-set qualitative comparative analysis (fsQCA) on Organisation for Economic Co-operation and Development (OECD) member countries. We confirm the equal importance of both cultural and institutional mechanisms as contributors to national innovativeness, and identify equifinal configurations of cultural and institutional varieties as leading to a high-tech society. The implication is that a society can adjust or develop various cultural and/or institutional conditions to maintain or create leadership in innovation

Utilization-Based Scheduling of Flexible Mixed-Criticality Real-Time Tasks

Mixed-criticality models are an emerging paradigm for the design of real-time systems because of their significantly improved resource efficiency. However, formal mixed-criticality models have traditionally been characterized by two impractical assumptions: once \textit{any} high-criticality task overruns, \textit{all} low-criticality tasks are suspended and \textit{all other} high-criticality tasks are assumed to exhibit high-criticality behaviors at the same time. In this paper, we propose a more realistic mixed-criticality model, called the flexible mixed-criticality (FMC) model, in which these two issues are addressed in a combined manner. In this new model, only the overrun task itself is assumed to exhibit high-criticality behavior, while other high-criticality tasks remain in the same mode as before. The guaranteed service levels of low-criticality tasks are gracefully degraded with the overruns of high-criticality tasks. We derive a utilization-based technique to analyze the schedulability of this new mixed-criticality model under EDF-VD scheduling. During runtime, the proposed test condition serves an important criterion for dynamic service level tuning, by means of which the maximum available execution budget for low-criticality tasks can be directly determined with minimal overhead while guaranteeing mixed-criticality schedulability. Experiments demonstrate the effectiveness of the FMC scheme compared with state-of-the-art techniques.Comment: This paper has been submitted to IEEE Transaction on Computers (TC) on Sept-09th-201

Do Poor Students Benefit from China’s Merger Program? Transfer Path and Educational Performance.

Aiming to provide better education facilities and improve the educational attainment of poor rural students, China’s government has been merging remote rural primary schools to centralized village, town, or county schools since the late 1990s. To accompany the policy, boarding facilities have been constructed that allow (mandate) primary school-aged children to live at school rather than at home. More generally, there also have been efforts to improve rural schools, especially those in counties and towns. Unfortunately, little empirical work has been available to evaluate the impact of the new merger and investment programs on the educational performance of students. Drawing on a unique dataset that records both the path by which students navigate their primary school years (i.e., which different types of schools did students attend) as well as math test scores in three poverty-stricken counties, we use descriptive statistics and multivariate analysis (both OLS and covariate matching) to analyze the relationship between different transfer paths and student educational performance. This allows us to examine the costs and benefits of the school merger and investment programs. The results of the analysis show that students who attend county schools perform systematically better than those attend village or town schools. However, completing primary school in town schools seems to have no effect on students’ academic performance. Surprisingly, starting primary education in a teaching point does not hurt rural students; on the contrary, it increases their test scores in some cases. Finally, in terms of the boarding effect, the neutral estimate in OLS and the negative estimate in covariate matching results confirm that boarding at school does not help the students; in some cases it may even reduce their academic performance.

An Online Sparse Streaming Feature Selection Algorithm

Online streaming feature selection (OSFS), which conducts feature selection in an online manner, plays an important role in dealing with high-dimensional data. In many real applications such as intelligent healthcare platform, streaming feature always has some missing data, which raises a crucial challenge in conducting OSFS, i.e., how to establish the uncertain relationship between sparse streaming features and labels. Unfortunately, existing OSFS algorithms never consider such uncertain relationship. To fill this gap, we in this paper propose an online sparse streaming feature selection with uncertainty (OS2FSU) algorithm. OS2FSU consists of two main parts: 1) latent factor analysis is utilized to pre-estimate the missing data in sparse streaming features before con-ducting feature selection, and 2) fuzzy logic and neighborhood rough set are employed to alleviate the uncertainty between estimated streaming features and labels during conducting feature selection. In the experiments, OS2FSU is compared with five state-of-the-art OSFS algorithms on six real datasets. The results demonstrate that OS2FSU outperforms its competitors when missing data are encountered in OSFS