Attitude-behaviour consistency: the role of group norms, attitude accessibility, and mode of behavioural decision-making

The interplay between two perspectives that have recently been applied in the attitude area-the social identity approach to attitude-behaviour relations (Terry & Hogg, 1996) and the MODE model (Fazio, 1990a)-was examined in the present research. Two experimental studies were conducted to examine the role of group norms, group identification, attitude accessibility, and mode of behavioural decision-making in the attitude-behaviour relationship. In Study I (N = 211), the effects of norms and identification on attitude-behaviour consistency as a function of attitude accessibility and mood were investigated. Study 2 (N = 354) replicated and extended the first experiment by using time pressure to manipulate mode of behavioural decision-making. As expected, the effects of norm congruency varied as a function of identification and mode of behavioural decision-making. Under conditions assumed to promote deliberative processing (neutral mood/low time pressure), high identifiers behaved in a manner consistent with the norm. No effects emerged under positive mood and high time pressure conditions. In Study 2, there was evidence that exposure to an attitude-incongruent norm resulted in attitude change only under low accessibility conditions. The results of these studies highlight the powerful role of group norms in directing individual behaviour and suggest limited support for the MODE model in this context. Copyright (C) 2003 John Wiley Sons, Ltd.

An Incremental EM-based Learning Approach for On-Line Prediction of Hospital Resource Utilization

Objective: Inpatient length of stay (LOS) is an important measure of hospital activity, health care resource consumption, and patient acuity. This research work aims at developing an incremental expectation maximization (EM) based learning approach on mixture of experts (ME) system for on-line prediction of LOS. The use of a batchmode learning process in most existing artificial neural networks to predict LOS is unrealistic, as the data become available over time and their pattern change dynamically. In contrast, an on-line process is capable of providing an output whenever a new datum becomes available. This on-the-spot information is therefore more useful and practical for making decisions, especially when one deals with a tremendous amount of data. Methods and material: The proposed approach is illustrated using a real example of gastroenteritis LOS data. The data set was extracted from a retrospective cohort study on all infants born in 1995-1997 and their subsequent admissions for gastroenteritis. The total number of admissions in this data set was n = 692. Linked hospitalization records of the cohort were retrieved retrospectively to derive the outcome measure, patient demographics, and associated co-morbidities information. A comparative study of the incremental learning and the batch-mode learning algorithms is considered. The performances of the learning algorithms are compared based on the mean absolute difference (MAD) between the predictions and the actual LOS, and the proportion of predictions with MAD < 1 day (Prop(MAD < 1)). The significance of the comparison is assessed through a regression analysis. Results: The incremental learning algorithm provides better on-line prediction of LOS when the system has gained sufficient training from more examples (MAD = 1.77 days and Prop(MAD < 1) = 54.3%), compared to that using the batch-mode learning. The regression analysis indicates a significant decrease of MAD (p-value = 0.063) and a significant (p-value = 0.044) increase of Prop(MAD

An automated failure mode and effect analysis based on high-level design specificication with behavior trees

Formal methods have significant benefits for developing safety critical systems, in that they allow for correctness proofs, model checking safety and liveness properties, deadlock checking, etc. However, formal methods do not scale very well and demand specialist skills, when developing real-world systems. For these reasons, development and analysis of large-scale safety critical systems will require effective integration of formal and informal methods. In this paper, we use such an integrative approach to automate Failure Modes and Effects Analysis (FMEA), a widely used system safety analysis technique, using a high-level graphical modelling notation (Behavior Trees) and model checking. We inject component failure modes into the Behavior Trees and translate the resulting Behavior Trees to SAL code. This enables us to model check if the system in the presence of these faults satisfies its safety properties, specified by temporal logic formulas. The benefit of this process is tool support that automates the tedious and error-prone aspects of FMEA.