Small Theories and Large Risks—Is Risk Analysis Relevant for Epistemology?

Ought we to take seriously large risks predicted by “exotic” or improbable theories? We routinely assess risks on the basis or either common sense, or some developed theoretical framework based on the best available scientific explanations. Recently, there has been a substantial increase of interest in the low‐probability “failure modes” of well‐established theories, which can involve global catastrophic risks. However, here I wish to discuss a partially antithetical situation: alternative, low‐probability (“small”) scientific theories predicting catastrophic outcomes with large probability. I argue that there is an important methodological issue (determining what counts as the best available explanation in cases where the theories involved describe possibilities of extremely destructive global catastrophes), which has been neglected thus far. There is no simple answer to the correct method for dealing with high‐probability high‐stakes risks following from low‐probability theories that still cannot be rejected outright, and much further work is required in this area. I further argue that cases like these are more numerous than usually assumed, for reasons including cognitive biases, sociological issues in science and the media image of science. If that is indeed so, it might lead to a greater weight of these cases in areas such as moral deliberation and policy making.     

基于信息化的企业知识管理

本文在企业信息化的背景下,阐述了企业知识管理的概念和特征,并在此基础上提出了知识管理的一个流程模型。该模型对于企业在信息化的基础上有效实施知识管理有重要借鉴作用。同时,以农机企业为例,在信息化基础之上,将农机企业知识管理理论分析与实施运用相结合,简要介绍了农机企业知识管理的解决方案与具体构建方法。     

Perceptions of Migration and Diversity by Local Public Administrators

This article contributes to the recent debate on the perception of migration and diversity; it empirically examines whether public decision-makers and public administrators perceive migration and diversity as related or if they distinguish between them. It also seeks to identify the most important characteristics of respondents with positive attitudes to migration and diversity. The sample of the respondents covers Turkey, Greece, Hungary and Slovakia; those located on the “Balkan migration route” into Europe. Due to different experiences, respondents from countries with large-scale migration perceive migration and diversity differently from those in countries directly threatened by migration or countries without any direct impact of migration. Gender appears to be a statistically significant predictor in the need for high managerial skills to manage migration and diversity. Some size categories of municipalities where a respondent works appear to be statistically significant when ranking the local impacts and local economic benefits of migration.     

A Bivariate Beta-Gamma Autoregressive Process (BVBGAR(1))

In this article, we introduce a bivariate autoregressive process with Gamma marginal distributions using the form of the BGAR(2) process (Risti?, 2005 Risti? , M. M. ( 2005 ). A Beta-Gamma autoregressive process of the second-order (BGAR(2)) . Statist. Probab. Lett. 73 : 403 – 410 . [Google Scholar]) and the Beta-Gamma transformation. Some properties of the process such as the autocovariance matrix, the autocorrelation matrix, and the spectral density matrix are derived. The unknown parameters of the process are estimated using the method of moments and the method of conditional least squares. Some numerical results of the estimators are given. We investigate nonparametric and parametric estimation of the spectral density matrix of this process.     

Visualization and Bandwidth Matrix Choice

In this study we compare three estimators of the extreme value index: Pickands estimator, the moment estimator and a maximum likelihood estimator. The estimators are explored both theoretically and by Monte Carlo simulation. We obtain two estimators for large quantiles using Pickands and the maximum likelihood estimators. The latter and one based on the moment estimator are then compared through simulation.     

Two step estimation for Neyman-Scott point process with inhomogeneous cluster centers

This paper is concerned with parameter estimation for the Neyman-Scott point process with inhomogeneous cluster centers. Inhomogeneity depends on spatial covariates. The regression parameters are estimated at the first step using a Poisson likelihood score function. Three estimation procedures (minimum contrast method based on a modified K function, composite likelihood and Bayesian methods) are introduced for estimation of clustering parameters at the second step. The performance of the estimation methods are studied and compared via a simulation study. This work has been motivated and illustrated by ecological studies of fish spatial distribution in an inland reservoir.