scholarly journals DEVELOPMENT OF ENDF/B-VII.1 AND ITS COVARIANCE COMPONENT

2010 ◽  
Author(s):  
M. Herman
Keyword(s):  
Covariance Component

Covariance component models for multivariate binary traits in family data analysis

2008 ◽  
Vol 27 (7) ◽  
pp. 1086-1105 ◽  
Author(s):  
Benjamin H. Yip ◽  
Camilla Björk ◽  
Paul Lichtenstein ◽  
Christina M. Hultman ◽  
Yudi Pawitan
Keyword(s):  
Data Analysis ◽  
Family Data ◽  
Binary Traits ◽  
Covariance Component ◽  
Component Models

scholarly journals The Strategy Aggregation Effect in Judgment

2020 ◽  
Author(s):  
Henrik Olsson
Keyword(s):  
Mean Squared Error ◽  
Cognitive Strategies ◽  
Judgment Task ◽  
Unified Framework ◽  
Organizational Settings ◽  
Aggregation Effect ◽  
Individual Judgment ◽  
Covariance Component ◽  
The Individual ◽  
Decision Environments

Predicting a criterion that is probabilistically related to pieces of information, or cues, is a paradigmatic judgment task that has been investigated both, in research trying to identify the individual judgment and decision making strategies people use, and in the wisdom-of-crowds literature where the focus is on how aggregation can improve accuracy. I combine these two lines of research to investigate how the performance of individual and aggregated linear strategies are affected by different environmental and group aggregation factors and how performance differences between individual and aggregated linear strategies can be understood in a unified framework. I show that constrained linear strategies are more accurate for individual judgments, but when these judgments are averaged, an unconstrained linear strategy is more accurate. This strategy aggregation effect can be understood by analyzing a decomposition of the mean squared error into bias, variance, and covariance. Because of their lower bias but higher variance, unconstrained linear strategies perform worse for individual judgments, but better for averaged judgments where aggregation minimizes variance. In simulations with artificial and real environments, I further show that this aggregation effect does not occur if there are correlations between individual judgments. Here, constrained linear strategies always outperform an unconstrained linear strategy, because the larger covariance component of the unconstrained linear strategy outweighs its lower bias. I end with real-world implications of the results for cognitive strategies and decision environments in group and organizational settings.

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