Multi-attribute utility methods in group decision making: Past applications and potential for inclusion in GDSS

This paper reviews a variety of studies in which multi-attribute utility theory (MAU) has been used successfully to aid group decision processes and discusses the incorporation of MAU in Group Decision Support Systems (GDSS). GDSS of various levels of automation have been proposed to aid group decision making. A number of commercial GDSSs are available which are capable of aiding generation of alternatives, their evaluation, and selection. Many authors have promoted the incorporation of quantitative decision making models in GDSS. The most appropriate quantitative tool for GDSS is MAU, but MAU has rarely been utilized. The cases reviewed illustrate that the use of MAU techniques is not prohibitively difficult or complex.     

The Big Five personality factors and team performance: implications for selecting successful product design teams

In the pursuit of faster product development, product design teams are a growing phenomenon in many organizations. In order to be successful, these teams must be composed of people who work well together. However, despite the benefit of selecting the optimal combination of team members, this topic has received little attention. Personality has been identified as a potentially helpful selection variable in the determination of optimal team composition. This study examines the relationships between the ‘Big Five’ personality factors (Conscientiousness, Extraversion, Neuroticism, Agreeableness, and Openness to Experience) and objective team performance for three-member product design teams. In addition to this, the potential incremental contribution of personality to the variance in team performance over that accounted for by established selection measures such as general cognitive ability was investigated. In the short duration of the study, it became apparent that some teams were capable of success, and some were not. Successful teams were characterized by higher levels of general cognitive ability, higher extraversion, higher agreeableness, and lower neuroticism than their unsuccessful counterparts. In successful teams, the heterogeneity of conscientiousness was negatively related to increments in product performance. Implications for the selection of product design teams and future directions for research are discussed.     

Confidence intervals for treatment effect from restricted maximum likelihood

An explicit form of confidence intervals for the treatment effect in random effects meta-analysis model obtained from Harville–Jeske–Kenward–Roger approach is given. These restricted likelihood based intervals are compared to alternative procedures commonly used in collaborative studies when the number of participants is small and study-specific variances are heterogeneous. Monte Carlo simulation experiments show that the former intervals have quite conservative coverage probabilities and favor the latter intervals.     

SNA关于生产资产的修订及对中国国民经济核算的影响研究

本文对2008年SNA关于生产资产分类的修订及对资产边界的扩展进行了梳理,分析了这些变化对中国国民经济核算体系的影响,提出了中国实施这些修订的建议。     

Bayesian and Robust Bayesian analysis under a general class of balanced loss functions

For estimating an unknown parameter θ, we introduce and motivate the use of balanced loss functions of the form L_{r, w, d0}(q, d)=wr(d₀, d)+ (1-w) r(q, d){L_{rho, omega, delta_0}(theta, delta)=omega rho(delta_0, delta)+ (1-omega) rho(theta, delta)}, as well as the weighted version q(q) L_{r, w, d0}(q, d){q(theta) L_{rho, omega, delta_0}(theta, delta)}, where ρ(θ, δ) is an arbitrary loss function, δ ₀ is a chosen a priori “target” estimator of q, w ? [0,1){theta, omega in[0,1)}, and q(·) is a positive weight function. we develop Bayesian estimators under L_{r, w, d0}{L_{rho, omega, delta_0}} with ω > 0 by relating such estimators to Bayesian solutions under L_{r, w, d0}{L_{rho, omega, delta_0}} with ω = 0. Illustrations are given for various choices of ρ, such as absolute value, entropy, linex, and squared error type losses. Finally, under various robust Bayesian analysis criteria including posterior regret gamma-minimaxity, conditional gamma-minimaxity, and most stable, we establish explicit connections between optimal actions derived under balanced and unbalanced losses.     

Sequential Monte Carlo for rare event estimation

This paper discusses a novel strategy for simulating rare events and an associated Monte Carlo estimation of tail probabilities. Our method uses a system of interacting particles and exploits a Feynman-Kac representation of that system to analyze their fluctuations. Our precise analysis of the variance of a standard multilevel splitting algorithm reveals an opportunity for improvement. This leads to a novel method that relies on adaptive levels and produces, in the limit of an idealized version of the algorithm, estimates with optimal variance. The motivation for this theoretical work comes from problems occurring in watermarking and fingerprinting of digital contents, which represents a new field of applications of rare event simulation techniques. Some numerical results show performance close to the idealized version of our technique for these practical applications.     

Generalized neighbor designs with block size 3

A generalized neighbor design relaxes the equality condition on the number of times two treatments as neighbors in the design. In this article, we have considered the construction of some classes of generalized neighbor designs with block size k=3 by using the method of cyclic shifts. The distinguishing feature of this construction method is that the properties of a design can easily be obtained from the sets of shifts instead of constructing the actual blocks of the design. A catalog of generalized neighbor designs with block size k=3 is compiled for v∈{5,6,…,18} treatments and for different replications. We provide the reader with a simpler method of construction, and in general the catalog that gives an open choice to the experimenter for selecting any class of neighbor designs.     

Latin hypercube sampling with multidimensional uniformity

Complex models can only be realized a limited number of times due to large computational requirements. Methods exist for generating input parameters for model realizations including Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of the input parameters through sequential realization elimination. Correlations are considered in the LHSMDU sampling matrix using a Cholesky decomposition of the correlation matrix. Computer code implementing the proposed algorithm supplements this article. A simulation study comparing MCS, LHS, maximinLHS and LHSMDU demonstrates that increased multidimensional uniformity can significantly improve realization efficiency and that LHSMDU is effective for large multivariate problems.