Read the paper carefully for this assignment and write a 5-page "reaction paper" (double-spaced, 10-point font, 1 to 1.5-inch margins). In the paper you write, describe what the article is about (make sure you give enough detail to convince me you have read the article thoroughly) and tell me your thoughts (what you learned, insights you had, your evaluation of the arguments in the article, etc.). Also, please include if you can think of applications or examples of the ideas in the paper that relate to your own experiences in the workplace or school. The late Robyn Dawes was a male, so please use male pronouns when referring to him. Prof. Dawes was a leading scholar in behavioral decision-making and wrote many important articles and books. This paper was particularly famous and the ideas expressed therein are important in arguments persuading people of the usefulness of models instead of just using human judgment. This approach is somewhat reflected in subsequent developments of simple and complex quantitative models of human performance (e.g., "Moneyball" style advanced stats and predictive analytics.)
Reference article details below:
The robust beauty of improper linear models in decision-making.
Authors:
Dawes, Robyn M., U Oregon
Source:
American Psychologist, Vol 34(7), Jul, 1979. pp. 571-582
Publisher:
US: American Psychological Association
ISSN:
0003-066X (Print)
1935-990X (Electronic)
Language:
English
Keywords:
improper linear models in decision making
Abstract:
Proper linear models are those in which predictor variables are given weights such that the resulting linear composite optimally predicts some criterion of interest; examples of proper linear models are standard regression analysis, discriminant function analysis, and ridge regression analysis. Research summarized in P. Meehl's (1954) book on clinical vs statistical prediction and research stimulated in part by that book indicate that when a numerical criterion variable (e.g., graduate GPA) is to be predicted from numerical predictor variables, proper linear models outperform clinical intuition. Improper linear models are those in which the weights of the predictor variables are obtained by some nonoptimal method. The present article presents evidence that even such improper linear models are superior to clinical intuition when predicting a numerical criterion from numerical predictors. In fact, unit (i.e., equal) weighting is quite robust for making such predictions. The application of unit weights to decide what bullet the Denver Police Department should use is described; some technical, psychological, and ethical resistances to using linear models in making social decisions are considered; and arguments that could weaken these resistances are presented. (50 ref) (PsycINFO Database Record (c) 2010 APA, all rights reserved)
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