从合作的演化到合作的复杂性——评阿克斯罗德关于人类合作生成机制的博弈论试验及其相关研究

合作是人类社会构成和存在的最深层基础。人类合作的生成机制若何?人类合作的道德含蕴和意义何在?这是经济学、政治学以及其它社会科学中的一些基本问题。自20世纪80年代以来,美国政治学家和博弈论专家阿克斯罗德教授设计出了一些计算机程序,对博弈论中重复囚徒困境博弈的收益结果进行了一系列的模拟试验,取得了很多惊人的理论发现,在国际学术界产生了重大影响,尤其是对社会学、人类学、伦理学、法学、甚至生物学等学科产生了广泛且深远的理论冲击。本文较全面地介绍了阿克斯罗德的重复囚徒困境博弈试验研究,并对其结果、理论发现做了一些评述和解读。     

The Protestant Ethic and Rockefeller Benevolence: The Religious Impulse in American Philanthropy

This paper is an application of Max Weber's thesis about the "elective affinity" between Protestant religious impulses and the rise of capitalism, and rationalization of benevolence. Exploring the history of organized philanthropy in the United States, using the life and work of John D. Rockefeller, the paper presents the power of the religious motive in Rockefeller's commitment to philanthropy, especially towards support for scientific university based research in medicine. Presenting historical evidence, the paper argues against those who see U.S. philanthropists as giving out of guilt or for selfish motivation, and those who critique philanthropic foundations as producers and reproducers of cultural hegemony     

也谈农业人口与非农业人口的统计问题

时代在前进,经济在发展,观念要转变,思想要提高,生产关系要适应生产力的发展,一切规章制度要符合社会发展的要求,并在不断改进中逐步完善。而这一切又要在马列主义、毛泽东思想、邓小平理论和“三个代表”重要思想的指导下进行。笔者从事统计和经济实际工作以及教学工作多年,深     

Relative performance of stein-rule and preliminary test estimators in linear models least squares theory

In the classical (univariare) linear model, bearing the plausibility of a subset of the regression parameters being close to a pivot, shrinkage least squares estimation of the complementary subset is considered. Based on the usual James-Stein rule, shrinkage least squares estimators are constructed, and under an asymptotic setup (allowing the shrinkage parameters to be 'close to ' the pivot), the relative performance of such estimators and the prcliminary test estimators is studied. In this context, the normality of the errors is also avoided under the same asymptotic setup. None of the shrinkage and preliminary test estimators may dominate the other (in the light of the asymptotic distributional risk criterion, as has been developed here), though each of them fares well relative to the classical least squeres estimator. The chice of the shrinkage factor is also examined properly.     

Locally optimal test for simultaneously testing the mean and variance of a normal distribution

A generalization of the locally most powerful unbiased (LMPU) test for the single parameter case to the k-parameter case was proposed by SenGupta and Vermeire (1986). In particular we defined a locally most mean power unbiased (LMMPU) test based on the mean curvature of the power hypersurface. Compared to the type C tests of Neyman and Pearson and the type D tests (Isaacson, 1951), LMMPU tests possess better theoretical properties and enjoy ease of construction of critical regions. In this paper we present an interesting example of a two-parameter univariate normal population for which Isaacson (1951, p. 233) was unsuccessful in finding a type D test. For the case of one observation, we prove that no Type D region exists but the LMMPU test is obtained - it is an example of a test with singular Hessian matrix for its power but is nevertheless a strictly locally unbiased (LU) test.     

On Symmetrizing Transformation of the Sample Coefficient of Variation from a Normal Population

Variance-stabilizing transformation (VST) for the sample coefficient of variation is often used as a normalizing transformation and may be used for inference on the population coefficient of variation. However, for small samples, the VST may not be symmetric and hence there is a scope of improvement in its performance by seeking a symmetrizing transformation. This article investigates such a transformation that has been obtained by solving a differential equation. The solution may be complex; hence, a numerical strategy is employed in order to make the approximation practically useful. This transformation has been compared with explicitly available VST. The approach has been illustrated on real data from an agricultural experiment concentrating on inference on single samples; however, the method may be generally applicable to multiple samples when testing the homogeneity of coefficients of variation for many populations by following usual normal-theory-based methods applied on transformed statistics.     

Cure-rate estimation under Case-1 interval censoring

We consider nonparametric estimation of cure-rate based on mixture model under Case-1 interval censoring. We show that the nonparametric maximum-likelihood estimator (NPMLE) of cure-rate is non-unique as well as inconsistent, and propose two estimators based on the NPMLE of the distribution function under this censoring model. We present a cross-validation method for choosing a ‘cut-off’ point needed for the estimators. The limiting distributions of the latter are obtained using extreme-value theory. Graphical illustration of the procedures based on simulated data is provided.     

Nonparametric testing for simple regression under progressive censoring with stagering entry and random withdrawal

In the context of (multi-center) clinical trials and life testins problems, a general model incorporating both the staggering entry and random withdrawal and pertaining to a simple regression problem (including the two-sample location problem as a special case) is conceived, and, within this framework, a scheme allowing progressive censoring (continuous monitoring of experimentation from the beginning) is developed along with the proposal for and study of some nonparametric testing procedures, The proposed tests rest on the construction of certain two-dimensional time-parameter stochastic processes from a triangular array of progressively censored linear rank statistics and their weak convergence to appropriate Gaussian functions. Asymptotic properties of these procedures are studied. A computer program pertaining to the numerical computations and practical administrations of these testing procedures is also provided at the end.     

Invariance principles for jackknifing U-statistics for finite population sampling and some applications

For simple random sampling (without replacement) from a finite population, suitable stochastic processes are constructed from the entire sequence of jackknife estimators based on smooth functions of U-statistics and these are approximated (in distributions) by some Brownian bridge processes. Strong convergence of the Tukey estimator of the variance of a jackknife U-statistic has been interpreted suitably and established. Some applications of these results in sequential analysis relating to finite population sampling are also considered.     

A test for bivariate two sample location problem

A consistent test for difference in locations between two bivariate populations is proposed, The test is similar as the Mann-Whitney test and depends on the exceedances of slopes of the two samples where slope for each sample observation is computed by taking the ratios of the observed values. In terms of the slopes, it reduces to a univariate problem, The power of the test has been compared with those of various existing tests by simulation. The proposed test statistic is compared with Mardia's(1967) test statistics, Peters-Randies(1991) test statistic, Wilcoxon's rank sum test. statistic and Hotelling' T² test statistic using Monte Carlo technique. It performs better than other statistics compared for small differences in locations between two populations when underlying population is population 7(light tailed population) and sample size 15 and 18 respectively. When underlying population is population 6(heavy tailed population) and sample sizes are 15 and 18 it performas better than other statistic compared except Wilcoxon's rank sum test statistics for small differences in location between two populations. It performs better than Mardia's(1967) test statistic for large differences in location between two population when underlying population is bivariate normal mixture with probability p=0.5, population 6, Pearson type II population and Pearson type VII population for sample size 15 and 18 .Under bivariate normal population it performs as good as Mardia' (1967) test statistic for small differences in locations between two populations and sample sizes 15 and 18. For sample sizes 25 and 28 respectively it performs better than Mardia's (1967) test statistic when underlying population is population 6, Pearson type II population and Pearson type VII population