Selecting a new technology strategy using multiattribute,multilevel decision trees

Research indicates that selecting a strategy to best exploit a new technology is a complex decision-making process. The task involves making a series of decisions with multiple alternatives, each to be evaluated by multiple criteria whose values have high levels of uncertainty. This paper presents a methodology for modelling a new technology decision using decision trees and an optimizing algorithm. A problem of a mining company considering the adoption of new technology is used to illustrate the decision-making task and modelling methodology. A numerical solution to the case demonstrates the potential of the optimizing technique in strategy selection.     

Philosophy and psychology at the Sorbonne, 1885–1913

The first university course in experimental psychology in Paris was located in the Faculty of Letters rather than the Faculty of Sciences or Medicine. The historical association of psychology with philosophy helps explain this placement, but this choice reinforced the philosophical character of the position at the expense of the experimental. In fact, the course included no laboratory instruction, with the exception of optional demonstrations conducted at psychological laboratories associated with the Faculty of Medicine. The fragmentation of the emerging discipline, distributed among divergent and competing Faculties, meant that training in experimental psychology may have been more difficult in France than in the United States or Germany, where laboratory research and training were more integrated. The first three instructors of the course—Théodule Ribot, Pierre Janet, and Georges Dumas—had a coherent vision of psychology as a synthesis of medical and philosophical approaches, but the matrix of institutional and disciplinary boundaries prevented them from bringing these approaches together.     

The effects of statistical training on choice heuristics in choice under uncertainty

Kahneman and Tversky (1984) proposed that decision makers perceive choice uncertainty in two ways: (1) as a distribution of possible outcomes or (2) as a single uncertain outcome. Using statistical training as a factor that influences these perceptions, and thus the type of decision approach individuals use, we found that individuals with different levels of experience displayed differences in the decisions they made and in the choice heuristics used to make those decisions. Statistically naive individuals were more likely to prefer loss-minimizing alternatives, use a more non-compensatory heuristic, and spend more time on loss-related information than their statistically experienced counterparts. When a distributional cue, indicating the distributional nature of choice outcomes, was presented to both experience groups, the naive group was found to use a decision approach similar to the experienced group and to make similar decisions. The results are discussed in terms of the need to include factors that alter individuals' approaches to uncertainty in future behavioral models of uncertain choice.     

Improved Test–retest Reliability Criteria as Applied to Probability Assessments

Test–retest reliability is a common indicator of response consistency. It is argued that using regression coefficients for detecting systematic response error is less appropriate than testing for shifts in the mean (median) and variance. This procedure is exemplified using probability response data. For this data, shifts in centrality were found to be about 2.5 times more likely than shifts in variability. The shifts in centrality did not favor any particular direction; however, variability tended to decrease over time in early sessions.     

Data integration in legal evaluations: Can clinicians deliver on their premises?

Clinicians often claim to base their conclusions on configural analysis and integration of most or all of the data. However, research suggests that subjective beliefs about complex interpretive strategies and their resultant success are largely illusory. Studies show that: (1) access to more data does not necessarily improve judgmental accuracy, (2) clinical judgments, which purportedly depend on configural analyses, can usually be duplicated by simple linear models, (3) individuals often have difficulty deciphering interactions or patterns among just two or three variables, and (4) actuarial methods, even those that simply add variables together, almost always equal or exceed the accuracy of clinicians who purport to integrate large amounts of information. Corrective suggestions are discussed that may help the clinician better manage large data sets.     

Influence of timing of post-weaning isolation on play fighting and serious aggression in the male golden hamster (Mesocricetus auratus)

Effects of timing of social isolation on play fighting and serious fighting were studied at different ages in male golden hamsters. Litters were isolated at 21, 35, and 65 days of age, and tested in a resident-intruder paradigm. Behaviors were compared within grous and with a fourth group of socially reared conspecifics. The earlier the pups were isolated, the more they engaged in play activities. Later, in adulthood, the aggression level of the same animals was retested using the same paradigm. The three isolated groups showed a high level of aggression, with significant differences among them. When compared with socially reared subjects, a reliable difference in the level of aggression was also found. These results support the view that early social experience is important, suggesting that isolation during early critical periods of socialization has a significant impact on play fighting, whereas short periods of isolation may be enough to trigger adult agonistic behavior. © 1994 Wiley-Liss, Inc.     

Hubert's multi-rater kappa revisited

There is a frequent need to measure the degree of agreement among R observers who independently classify n subjects within K nominal or ordinal categories. The most popular methods are usually kappa-type measurements. When R = 2, Cohen's kappa coefficient (weighted or not) is well known. When defined in the ordinal case while assuming quadratic weights, Cohen's kappa has the advantage of coinciding with the intraclass and concordance correlation coefficients. When R > 2, there are more discrepancies because the definition of the kappa coefficient depends on how the phrase ‘an agreement has occurred’ is interpreted. In this paper, Hubert's interpretation, that ‘an agreement occurs if and only if all raters agree on the categorization of an object’, is used, which leads to Hubert's (nominal) and Schuster and Smith's (ordinal) kappa coefficients. Formulae for the large-sample variances for the estimators of all these coefficients are given, allowing the latter to illustrate the different ways of carrying out inference and, with the use of simulation, to select the optimal procedure. In addition, it is shown that Schuster and Smith's kappa coefficient coincides with the intraclass and concordance correlation coefficients if the first coefficient is also defined assuming quadratic weights.     

The danger of conflating level-specific effects of control variables when primary interest lies in level-2 effects

In the multilevel modelling literature, methodologists widely acknowledge that a level-1 variable can have distinct within-cluster and between-cluster effects, and that failing to disaggregate these can yield a slope estimate that is an uninterpretable, conflated blend of the two. Methodologists have stated, however, that including conflated slopes of level-1 variables in a model is not problematic if substantive interest lies only in effects of level-2 predictors. Researchers commonly follow this advice and use methods that do not disaggregate effects of level-1 control variables (e.g., grand mean centering) when examining effects of level-2 predictors. The primary purpose of this paper is to show that this is a dangerous practice. When level-specific effects of level-1 variables differ, failing to disaggregate them can severely bias estimation of level-2 predictor slopes. We show mathematically why this is the case and highlight factors that can exacerbate such bias. We corroborate these findings with simulations and present an empirical example, showing how such distortions can severely alter substantive conclusions. We ultimately recommend that simply including the cluster mean of the level-1 variable as a control will alleviate the problem.