Micro/macro viability analysis of individual‐based models: Investigation into the viability of a stylized agricultural cooperative

The mathematical viability theory proposes methods and tools to study at a global level how controlled dynamical systems can be confined in a desirable subset of the state space. Multilevel viability problems are rarely studied since they induce combinatorial explosion (the set of N agents each evolving in a p‐dimensional state space, can evolve in a Np dimensional state space). In this article, we propose an original approach which consists in solving first local viability problems and then studying the real viability of the combination of the local strategies, by simulation where necessary. In this article, we consider as multilevel viability problem a stylized agricultural cooperative which has to keep a minimum of members. Members have an economical constraint and some members have a simple model of the functioning of the cooperative and make assumptions on other members' behavior, especially proviable agents which are concerned about their own viability. In this framework, the model assumptions allow us to solve the local viability problem at the agent level. At the cooperative level, considering mixture of agents, simulation results indicate if and when including proviable agents increases the viability of the whole cooperative. © 2014 Wiley Periodicals, Inc. Complexity 21: 276–296, 2015     

基于序列稳定性的冲突当局者行为分析模型

不同稳定性可以折射冲突当局者的不同行为类型,为了更全面且深入地了解当局者的行为类型,本文将现有四种基本稳定性拓展至六种,构建了一个能够提高冲突分析图模型理论普遍实用性的决策方法。首先,提出了强、弱序列稳定性(SEQ稳定)的逻辑概念,以此分析当局者具有不同程度的过度自信下的冲突决策过程。然后,定义了强、弱SEQ稳定的矩阵表达,以此促进相应稳定解的计算和编程算法的实现。最后,使用“制造商与零售商渠道价格冲突”案例说明强弱SEQ稳定的意义。研究发现:冲突当局者的过度自信偏差并不都会产生负面影响,有时会利于当局者的集体利益。     

A Two-Stage Approach for Bayesian Joint Models of Longitudinal and Survival Data: Correcting Bias with Informative Prior

Joint models of longitudinal and survival outcomes have gained much popularity in recent years, both in applications and in methodological development. This type of modelling is usually characterised by two submodels, one longitudinal (e.g., mixed-effects model) and one survival (e.g., Cox model), which are connected by some common term. Naturally, sharing information makes the inferential process highly time-consuming. In particular, the Bayesian framework requires even more time for Markov chains to reach stationarity. Hence, in order to reduce the modelling complexity while maintaining the accuracy of the estimates, we propose a two-stage strategy that first fits the longitudinal submodel and then plug the shared information into the survival submodel. Unlike a standard two-stage approach, we apply a correction by incorporating an individual and multiplicative fixed-effect with informative prior into the survival submodel. Based on simulation studies and sensitivity analyses, we empirically compare our proposal with joint specification and standard two-stage approaches. The results show that our methodology is very promising, since it reduces the estimation bias compared to the other two-stage method and requires less processing time than the joint specification approach.     

Dynamic Spectrum for noninvasive blood component analysis and its advances

Noninvasive blood component analysis by spectroscopy has broad prospects that based on Dynamic Spectrum (DS) can effectively suppress the influences of individual differences (such as skin, muscle, fat) and the variations of measurement conditions, which shows its potential in the clinical applications. The purpose of this article is to review the starting point and the advances of DS, to assess the current situation of the field and to explore future directions. The principle of DS and the error sources of noninvasive blood component analysis were introduced. An overview of the six stages of noninvasive blood component analysis, including sensing principle, acquisition methods of spectral photoplethysmography (photoplethysmography signal at multiple wavelengths) signals, spectral photoplethysmography (SPPG) preprocessing, DS extraction methods from SPPG, the quality evaluation for DS and modeling methods, was presented. Finally, the existing issues of DS are discussed and the future directions are predicted.     

显微拉曼光谱用于研究大气单颗粒表面非均相反应

大气单颗粒表面的非均相反应研究因更接近大气实际条件,避免了堆积态研究中人为引入的误差,能够得到真实的反应过程与机理,获得反映大气实际条件的动力学参数.本研究建立了使用显微拉曼光谱研究大气单颗粒非均相反应的研究方法,并初步用于研究NO2与单颗粒CaCO3的非均相反应.研究结果表明显微拉曼光谱可同时获得颗粒物的化学组成和形貌变化,并能得到化学环境如相态的信息,对于研究反应过程很有帮助;而颗粒物沉降在基质上得到的拉曼光谱因不受形貌共振影响,有利于获得高质量的光谱.此外,将拉曼光谱研究单颗粒的方法与其他单颗粒非均相反应的研究方法进行了综合比较,表明显微拉曼光谱技术在单颗粒非均相反应研究中具有重要的特点和应用价值.     

Source identification of individual P M2.5 particles in Shanghai air in the winter of 2007 with synchrotron X-ray fluorescence microprobe

In order to further understand the sources of PM2.5 in Shanghai air, the synchrotron X-ray fluores- cence microprobe at the BL-4A Beamline of Photon Factory of High Energy Accelerator Research Organization, Japan, was applied to analyze the individual PM2.5 particles collected from Shanghai air in the winter of 2007. Eight categories of emission sources were recognized in these individual particles. The source identification shows that most of the analyzed PM2.5 particles are derived from vehicle exhaust and metallurgical emissions. This suggests that the important emission sources of PM2.5 in Shanghai air would be vehicle exhaust and metallurgical activities.     

Concave distortion risk minimizing reinsurance design under adverse selection

This article makes use of the well-known Principal–Agent (multidimensional screening) model commonly used in economics to analyze a monopolistic reinsurance market in the presence of adverse selection, where the risk preference of each insurer is guided by its concave distortion risk measure of the terminal wealth position; while the reinsurer, under information asymmetry, aims to maximize its expected profit by designing an optimal policy provision (menu) of “shirt-fit” stop-loss reinsurance contracts for every insurer of either type of low or high risk. In particular, the most representative case of Tail Value-at-Risk (TVaR) is further explored in detail so as to unveil the underlying insight from economics perspective.     

Clustering electricity consumers using high‐dimensional regression mixture models

A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.     

不确定性下的一主多从博弈及其ε-平衡稳定性分析

本文对主从博弈以及不确定性等问题进行研究,建立了不确定性下的一主多从博弈模型,并利用极大值定理证明了该模型均衡点的存在性。对于不确定性下的一主多从博弈的均衡点问题建立了有限理性模型,进而得到其均衡点的稳定性,即结构稳定以及对ε-平衡是鲁棒的。     

合作博弈的粒子群算法求解

随着局中人人数的增加,利用传统的“占优”方法和“估值”方法进行合作博弈求解无论从逻辑上还是计算上都变得非常困难。针对此问题,将合作博弈的求解看作是局中人遵照有效性和个体理性提出分配方案,并按照一定规则不断迭代调整直至所有方案趋向一致的过程。依据该思路,对合作博弈粒子群算法模型进行构建,确定适应度函数,设置速度公式中的参数。通过算例分析,利用粒子群算法收敛快、精度高、容易实现的特点,可以迅速得到合作博弈的唯一分配值,这为求解合作博弈提供了新的方法和工具。