基于复杂网络理论的企业营销网络鲁棒性分析

基于复杂网络理论,探讨企业营销网络拓扑结构及其演化机制。在此基础上,对企业营销网络鲁棒性进行了分析。企业营销网络的无标度性具有对随机攻击的鲁棒性,以及对于恶意破坏的脆弱性。为了提高企业营销网络对于恶意破坏的鲁棒性,通过改变网络的演化机制,消除其无标度特性,生成节点度指数分布的网络。     

On Long-term Forecasting of Social Security: A Robustness Analysis

In this paper we present a macro-economic demographic growth modelhaving a special focus on social security. It is designed to study the variability of responses of the system in presence of risks and uncertainties. Here we analyze the robustness of the model towards uncertainties in parameterspecifications, introduced by ARCH-M models with the incorporation ofintervention processes. The parameters varied are labor force participationrates (one of the key sources of uncertainty in the social security policydebate), and the parameters of the production function (the key source ofuncertainty in any long-run economic analysis). The sensitivity analysisfocuses on two variables: assets of the private pension system and thebalance of the public `Pay As You Go' pension system. Special attentionis given to convergence properties of the macro-economic model.     

Make a guess: a robust mechanism for King Solomon’s dilemma

We introduce endogenous fees for participating in second-price auction which we use for a two-stage mechanism to solve King Solomon’s dilemma. They are positive for all agents. They are nonetheless shown to maintain the agents’ incentives for truthful bidding and guarantee participation by the highest-value agent. This feature of the endogenous fees is powerful enough for the efficient outcome to uniquely result from one round elimination of weakly dominated strategies, followed by at most four rounds of iterative elimination of strictly dominated stage-strategies. We provide an extension to cases with n agents and k identical prizes. We thank Gary Charness, Harrison Cheng, Jeffrey Ely, Rod Garratt, Mamoru Kaneko, Xiao Luo, Albert Ma, and Steven Morris for helpful discussions. We also thank an anonymous referee for comments that have helped to improve the paper.     

基于线性二次型的永磁同步电机最优滑模控制

永磁同步电机的伺服控制系统是一个典型的非线性、强耦合、多输入多输出的系统,负载扰动和参数摄动都会使系统不稳定。针对这一问题,提出了一种基于线性二次型最优的滑模控制设计方法。将滑模控制与最优控制二者的优点相结合,设计了最优滑模控制律,并分别搭建基于传统滑模控制的永磁同步电机速度控制器和基于线性二次型最优的滑模速度控制器的仿真模型,在相同的条件下进行仿真,观察系统在两种控制器下的抗干扰能力。结果表明,不论系统受到突加负载信号还是突减负载信号的干扰,最优滑模控制器都具有很好的抗干扰能力,有效地增强了系统的鲁棒性。所提出的方法提高了永磁同步电机控制系统的动静态性能,对深入研究永磁同步电机控制系统具有一定的参考价值。     

Machine learning algorithms for the creation of clinical healthcare enterprise systems

Clinical recommender systems are increasingly becoming popular for improving modern healthcare systems. Enterprise systems are persuasively used for creating effective nurse care plans to provide nurse training, clinical recommendations and clinical quality control. A novel design of a reliable clinical recommender system based on multiple classifier system (MCS) is implemented. A hybrid machine learning (ML) ensemble based on random subspace method and random forest is presented. The performance accuracy and robustness of proposed enterprise architecture are quantitatively estimated to be above 99% and 97%, respectively (above 95% confidence interval). The study then extends to experimental analysis of the clinical recommender system with respect to the noisy data environment. The ranking of items in nurse care plan is demonstrated using machine learning algorithms (MLAs) to overcome the drawback of the traditional association rule method. The promising experimental results are compared against the sate-of-the-art approaches to highlight the advancement in recommendation technology. The proposed recommender system is experimentally validated using five benchmark clinical data to reinforce the research findings.     

A remark on topological robustness to bounded rationality in semialgebraic models

In this short note, we show that the compact semialgebraic class of Anderlini and Canning (2001) is topologically robust, i.e. the topological properties of the equilibrium set are preserved, deviating parameter values and introducing a small amount of bounded rationality.     

Tourism firm restructuring: Does the attention of individual investor matter?

We examine the power of individual investor attention in increasing tourism firms' restructured performance, the different effects caused by ‘attention heterogeneity’, the moderating effect of media coverage on the relationship between individual investor attention and tourism firms' restructured performance, and the robustness of the effects. The results indicate that 1) individual investor attention has a significant positive effect on tourism firms' restructured performance, 2) individual investor attention via mobile devices influences tourism firms' restructured performance more than attention via computers, 3) the moderating effect of media coverage on this relationship varies with time and media heterogeneity, and 4) the effect of individual investor attention is immediate, while time is needed for the moderating effect of media coverage to appear. The main effect of individual investor attention and the moderating effect of media coverage on tourism firm performance do not depend on the firm size or the proportion of individual shareholders.     

Coarsened Propensity Scores and Hybrid Estimators for Missing Data and Causal Inference

In the areas of missing data and causal inference, there is great interest in doubly robust (DR) estimators that involve both an outcome regression (RG) model and a propensity score (PS) model. These DR estimators are consistent and asymptotically normal if either model is correctly specified. Despite their theoretical appeal, the practical utility of DR estimators has been disputed (e.g. Kang and Schaffer, Statistical Science 2007; 22: 523–539). One of the major concerns is the possibility of erratic estimates resulting from near‐zero denominators due to extreme values of the estimated PS. In contrast, the usual RG estimator based on the RG model alone is efficient when the RG model is correct and generally more stable than the DR estimators, although it can be biased when the RG model is incorrect. In light of the unique advantages of the RG and DR estimators, we propose a class of hybrid estimators that attempt to strike a reasonable balance between the RG and DR estimators. These hybrid estimators are motivated by heuristic arguments that coarsened PS estimates are less likely to take extreme values and less sensitive to misspecification of the PS model than the original model‐based PS estimates. The proposed estimators are compared with existing estimators in simulation studies and illustrated with real data from a large observational study on obstetric labour progression and birth outcomes.     

Robust and stable multi-task manufacturing scheduling with uncertainties using a two-stage extended genetic algorithm

ABSTRACT

In cloud manufacturing (CMfg), unexpected uncertainties can occur in real-world manufacturing processes that could make the predetermined schedule infeasible. A new multi-objective proactive method is proposed in this situation to evaluate the proactive schedule. A novel two-stage extended genetic algorithm (2S-EGA) is proposed to generate proactive schedules that consider service interruptions. The experimental results confirmed that the obtained proactive schedule produces great performance when applied to multi-task scheduling problems with service interruptions. Furthermore, the results also showed that the proactive schedule obtained by the proposed approach is more robust and stable than other baseline algorithms taken from the literature.