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Katsikopoulos, Konstantinos V.; Schooler, Lael J.; Hertwig, Ralph – Psychological Review, 2010
Heuristics embodying limited information search and noncompensatory processing of information can yield robust performance relative to computationally more complex models. One criticism raised against heuristics is the argument that complexity is hidden in the calculation of the cue order used to make predictions. We discuss ways to order cues…
Descriptors: Heuristics, Computer Simulation, Cues, Prediction
Bajgier, Steve M.; Atkinson, MaryAnne – Collegiate Microcomputer, 1989
Describes the use of a simulated learning environment (SLE) as an instructional aid in teaching multivariate statistics, particularly inferential statistics. A prototype microcomputer-based SLE called MVWORLD developed at Drexel University for upper level statistics courses is explained, and implementing a statistics laboratory for multivariate…
Descriptors: Computer Assisted Instruction, Computer Simulation, Courseware, Educational Environment
Levy, Roy; Mislevy, Robert J. – US Department of Education, 2004
The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…
Descriptors: Probability, Markov Processes, Monte Carlo Methods, Bayesian Statistics
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Caulkins, Jonathan P. – Journal of Policy Analysis and Management, 2002
In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…
Descriptors: Statistical Inference, Models, Policy Analysis, Sampling