对信息源的正向跟随倾向对决策效果的影响*

采用实验研究的方法,检验了二元选择情形下高准确率信息源(准确率x >50%)和低准确率信息源(准确率为1?x)对决策者预测效果的影响。尽管从逻辑上二者从准确率层面具有完全相等的信息价值,然而研究结果显示,决策者更倾向于采用正向跟随而非逆向跟随的方式处理来自信息源的信息。这导致低准确信息源的价值未能得以充分利用。学习和信息源的规范性对该信息处理倾向起到调节作用。     

Adult age differences in subjective and objective measures of strategy use on a sequentially cued prediction task

Differences in strategy use are thought to underlie age-related performance deficits on many learning and decision-making tasks. Recently, age-related differences in learning to make predictions were reported on the Triplets Prediction Task (TPT). Notably, deficits appeared early in training and continued with experience. To assess if age differences were due to early strategy use, neural networks were used to objectively assess the strategies implemented by participants during Session 1. Then, the relationship between these strategies and performance was examined. Results revealed that older adults were more likely to implement a disadvantageous strategy early in learning, and this led to poorer task performance. Importantly, the relationship between age and task performance was partially mediated by early strategy use, suggesting that early strategy selection played a role in the lower quality of predictions in older adults.     

Actuarial Criminology: Meeting Current Challenges in Southern Africa

South African prisons were overcrowded by more than sixty-three percent in 2005. Overcrowding and poor prison conditions have enormous implications for rehabilitation efforts made by the South African Department of Correctional Services. The South African White Paper on Corrections (2005) commits the Department of Correctional Services to provide needs-based rehabilitation processes and a framework of continuous evaluation to measure the impact and success of such processes. It also acknowledges that monitoring the rates of recidivism is a critical indicator of the success, the effectiveness of needs-based rehabilitation, and improved service. A reliable system for monitoring recidivism will have to be developed and put in place. In this respect, actuarial criminology can make a vital contribution. This article poses a strategic alternative to the correctional industry in Southern Africa that can be used for the realization of correctional ideals, especially where retribution and overcrowding are still the main features of correctional institutions.     

Predictor Variables Associated with Positive Fast Track Outcomes at the End of Third Grade

Progress has been made in understanding the outcome effects of preventive interventions and treatments designed to reduce children's conduct problems. However, limited research has explored the factors that may affect the degree to which an intervention is likely to benefit particular individuals. This study examines selected child, family, and community baseline characteristics that may predict proximal outcomes from the Fast Track intervention. The primary goal of this study was to examine predictors of outcomes after 3 years of intervention participation, at the end of 3rd grade. Three types of proximal outcomes were examined: parent-rated aggression, teacher-rated oppositional–aggressive behavior, and special education involvement. The relation between 11 risk factors and these 3 outcomes was examined, with separate regression analyses for the intervention and control groups. Moderate evidence of prediction of outcome effects was found, although none of the baseline variables were found to predict all 3 outcomes, and different patterns of prediction emerged for home versus school outcomes.     

风险偏好预测中的性别差异和框架效应

11 8名大学生被试在得益和损失两种任务框架下对自己、其他男性和女性的风险偏好进行了预测。结果发现 ,被试在损失框架下对各类对象风险偏好的预测均高于得益框架 ,且都高估了男性的风险偏好 ,低估了女性的风险偏好。在损失框架下 ,女性被试对各类对象的风险偏好预测高于男性的预测。研究认为 ,性别原型知识、任务框架和即时情绪共同影响被试的风险偏好预测。     

Towards a new approach to predict business performance using machine learning

Financial ratio plays a crucial role in business performance prediction, but the ability of the decision maker to use this method in adjusting management strategy has been extensively ignored. In this paper we attempt to build a fuzzy chance constrained least squares twin support vector machine (FCC-LSTSVM) to predict the business performance through the financial ratios. Specifically, machine learning techniques are utilized to build the models and 796 listed companies in China are selected as the data set. We find that different efficiencies are performed for different models with the same industry and different effectiveness are shown for different predicting time periods with the same method. In addition, the predicting achievements of business performance depend on the types of industries. This paper has extent significance both in theoretical development and managerial practices.     

Conditions for factor (in)determinacy in factor analysis

The subject of factor indeterminacy has a vast history in factor analysis (Guttman, 1955; Lederman, 1938; Wilson, 1928). It has lead to strong differences in opinion (Steiger, 1979). The current paper gives necessary and sufficient conditions for observability of factors in terms of the parameter matrices and a finite number of variables. Five conditions are given which rigorously define indeterminacy. It is shown that (un)observable factors are (in)determinate. Specifically, the indeterminacy proof by Guttman is extended to Heywood cases. The results are illustrated by two examples and implications for indeterminacy are discussed.     

How do investors make predictions? Insights from analogical reasoning research

Previous work on investor decision making has focused almost exclusively on information specific to the company being judged. Consequently, every decision is viewed as a novel event, disconnected from the investor's existing knowledge. In this study, the analogical reasoning literature provides the theoretical support for arguing that investors frequently utilize existing knowledge as a basis for generating predictions about a company's future. The specific proposal is that investors transfer their existing knowledge via two different forms of analogical reasoning. The first, relational reasoning, is based primarily on structural correspondence between a novel company and an existing schema. The second, literal similarity reasoning, is based primarily on surface correspondence of a novel company and a previously encountered company. Our theoretical framework is tested in a study in which experienced investors predict the outcome of a novel company's strategy after reading about the experiences of other companies who implemented a similar strategy. The results are consistent with the occurrence of both relational and literal similarity reasoning, with relational reasoning emerging as the dominant approach to generating investors' predictions. Copyright © 2000 John Wiley & Sons, Ltd.