基于多Agent的运动图像匹配任务分配策略研究

本文针对基于多Agent的运动图像匹配系统的任务分配策略进行了讨论,并结合遗传算法和模拟退火算法给出了一种新的基于多Agent的运动图像模式匹配系统的任务分配策略。     

中介效应的三类区间估计方法

由于中介效应ab的估计量通常不是正态分布, 因此需用不对称置信区间进行中介效应分析。详述了三类获得不对称置信区间的方法, 包括乘积分布法(M法和经验M法)、Bootstrap方法(偏差校正和未校正的非参数百分位Bootstrap方法、偏差校正和未校正的参数百分位残差Bootstrap方法)和马尔科夫链蒙特卡罗(MCMC)方法。比较了三类方法在单层(简单和多重)和多层中介效应分析中的表现, 发现三类方法的表现相近, 与乘积分布法相比, 偏差校正的百分位Bootstrap方法表现较好, 但有先验信息的MCMC方法能更有效降低均方误。最后对中介效应不对称置信区间研究的拓展方向做了展望。     

Using aspiration levels in an interior primal-dual multiobjective linear programming algorithm

We introduce in this paper a new multiple-objective linear programming (MOLP) algorithm. The algorithm is based on the single-objective path-following primal—dual linear programming algorithm and combines it with aspiration levels and the use of achievement scalarizing functions. The resulting algorithm falls in the class of interactive MOLP algorithms, as it requires interaction with the decision maker (DM) during the iterative process to obtain statements of aspirations for levels of objectives of the MOLP problem. The interior point algorithm is then used to trace a path of interates from a current (interior) solution and approach as closely as desired a non-dominated solution corresponding to the optimum of the achievement scalarizing function. The timing of the interaction with the DM is dependent on the progress of the interior algorithm. It can take place every few, pre-specified, iterations or after the duality gap achieved for the stated aspirations has fallen below a certain threshold. It is expected that an interior algorithm will speed up the overall process of searching and finding the most preferred MOLP solution—especially in large-scale problems—by avoiding the need for numerous pivot operations and their corresponding interactive sessions inherent in simplex-based algorithms.     

多题多做测验模型及其应用

在IRT框架下,建立了0-1评分方式下单维双参数Logistic多题多做（MAMI）测验模型。与Spray给出的一题多做（MASI）模型相比,MAMI不仅模型更加精致,而且扩展了适用范围,参数估计方法也不同,采用EM算法求取项目参数。Monte Carlo模拟结果显示,应用MAMI测验模型与测验题量作相应增加的作法相比,两者给出的能力估计精度相同,但MAMI模型给出的项目参数估计精度更高。如果将MAMI测验模型与被试人数相应增加的作法相比,项目参数的估计精度相同,但MAMI给出的能力参数估计精度更高。这个发现表明,在一定条件下若允许修改答案,并采用累加式记分方式,纵使题量不变,也可使能力估计的精度相当于题量增加一倍的估计精度,而项目参数估计精度也会提高。这些发现不仅对技能评价和认知能力评价有参考价值,而且对数据的处理方式也有参考价值     

Maximum likelihood estimation of latent interaction effects with the LMS method

In the context of structural equation modeling, a general interaction model with multiple latent interaction effects is introduced. A stochastic analysis represents the nonnormal distribution of the joint indicator vector as a finite mixture of normal distributions. The Latent Moderated Structural Equations (LMS) approach is a new method developed for the analysis of the general interaction model that utilizes the mixture distribution and provides a ML estimation of model parameters by adapting the EM algorithm. The finite sample properties and the robustness of LMS are discussed. Finally, the applicability of the new method is illustrated by an empirical example. This research has been supported by a grant from the Deutsche Forschungsgemeinschaft, Germany, No. Mo 474/1 and Mo 474/2. The data for the empirical example have been provided by Andreas Thiele of the University of Frankfurt, Germany. The authors are indebted to an associate editor and to three anonymous reviewers ofPsychometrika whose comments and suggestions have been very helpful.     

BANOVA: Bayesian Analysis of Experiments in Consumer Psychology

This article introduces a Bayesian extension of ANOVA for the analysis of experimental data in consumer psychology. The approach, called BANOVA (Bayesian ANOVA), addresses some common challenges that consumer psychologists encounter in their experimental work, and is specifically suited for the analysis of repeated measures designs. There appears to be a recent surge in interest in those designs based on the recognition that they are sensitive to individual differences in response to experimental treatments and that they offer advantages for assessing causal mediating mechanisms, even at the individual level. BANOVA enables the analysis of repeated measures data derived from mixed within–between‐subjects experiments with Normal and nonNormal‐dependent variables and accommodates unobserved individual differences. It allows for the calculation of effect sizes, planned comparisons, simple effects, spotlight and floodlight analyses, and includes a wide range of mediation, moderation, and moderated mediation analyses. An R software package implements these analyses, and aims to provide a one‐stop shop for the analysis of experiments in consumer psychology. The package is illustrated through applications to a number of data sets from previously published studies.     

Marginalized maximum a posteriori estimation for the four-parameter logistic model under a mixture modelling framework

The four-parameter logistic model (4PLM) has recently attracted much interest in various applications. Motivated by recent studies that re-express the four-parameter model as a mixture model with two levels of latent variables, this paper develops a new expectation–maximization (EM) algorithm for marginalized maximum a posteriori estimation of the 4PLM parameters. The mixture modelling framework of the 4PLM not only makes the proposed EM algorithm easier to implement in practice, but also provides a natural connection with popular cognitive diagnosis models. Simulation studies were conducted to show the good performance of the proposed estimation method and to investigate the impact of the additional upper asymptote parameter on the estimation of other parameters. Moreover, a real data set was analysed using the 4PLM to show its improved performance over the three-parameter logistic model.     

人工智能决策的公平感知

不平等问题是全球社会和经济发展需要应对的首要挑战,也是实现全球可持续发展目标的核心障碍。人工智能(artificial intelligence, AI)为缓解不平等、促进社会公平提供了新的途径。然而,新近研究发现,即使客观上AI决策具有公平性和准确性,个体仍可能对AI决策的公平感知较低。因此,近年来越来越多的研究开始关注AI决策公平感知的影响因素。然而,目前研究较为分散,呈现出研究范式不统一、理论不清晰和机制未厘清等特征。这既不利于跨学科的研究对话,也不利于研究者和实践者对AI决策公平感知形成系统性理解。基于此,通过系统的梳理,现有研究可以划分为两类:(1) AI单一决策的公平感知研究,主要聚焦于AI特征和个体特征如何影响个体对AI决策的公平感知;(2) AI-人类二元决策的公平感知研究,主要聚焦于对比个体对AI决策与人类决策公平感知的差异。在上述梳理基础上,未来研究可以进一步探索AI决策公平感知的情绪影响机制等方向。     

卡方自动交叉检验在人群细分中的应用

卡方自动交叉检验（CHi-squaredAutomaticInteractionDetector,CHAID）是一种定性的统计分类技术,主要解决根据一个因变量的不同反应确定若干预测变量的特征问题。这种算法模型可广泛应用于社会调查和市场细分中,根据不同的目的细分人群。该文主要介绍了CHAID算法模型的概念和发展,理论基础和应用过程,以及其效度检验方法,并将其与Logit模型等作了比较,指出其优势和局限。最后提出了研究展望     

不同链接函数下多级评分认知诊断模型的比较及应用研究

认知诊断测验因具有传统测验所不具备的诊断功能而日益受到重视。当前多级评分认知诊断模型开发中,研究者采用不同的链接函数（Link Function）开发出不同的多级评分认知诊断模型。本研究基于局部或相邻类别链接函数(Local or Adjacent Categories Link Function)的思想,开发出多级评分认知诊断模型LC-DINA研究采用Monte Carlo模拟研究与实证应用研究相结合的方法,将新开发模型与已有模型进行比较并应用于国际数学与科学评估（TIMMS）中,为实际应用者提供了借鉴。