Suchergebnisse

Background: It was proposed that misuse and disuse often occur because operators (a) cannot determine if automation or a nonautomated alternative maximizes the likelihood of task success (appraisal errors) or (b) know the utilities of the options but disregard this information when deciding to use automation (intent errors). Objective: This investigation assessed the effectiveness of performance feedback, a procedure developed to attenuate appraisal errors, and scenario training, an intervention designed to decrease intent errors. Methods: Operators given feedback were told how many errors they and an automated device made on a target detection task. Scenario training took operators through the thought processes of optimal decision makers after the utilities of the automated and nonautomated alternatives had been determined. Following 200 training trials, participants chose between relying on their observations or an automated device. Results: There was little misuse, but disuse rates were high (84%) among operators receiving neither feedback nor scenario training. Operators paired with a more accurate machine and given feedback made approximately twice as many errors as the automated device. Nevertheless, intent errors were commonplace; 55% of the operators who received feedback without scenario training did not rely on automation. Feedback effectiveness was enhanced when used in conjunction with scenario training; the disuse rate decreased to 29%. Conclusion: A combination of feedback and scenario training was more effective in mitigating disuse than either intervention used in isolation. Application: An important application of these findings is that operator training programs should incorporate techniques to control intent and appraisal errors.

The 'cultivated' nature of the Chinese science parks, against the background of a transitional economy, differentiates them from spontaneous and cooperative Western models, and is a phenomenon deserving close examination. We study the dynamics and features of the so-called Optics Valley of China (OVC) in Hubei, aiming to explore the characteristics of an embryonic local innovation system constructed in a less-favoured region. The results show that institutional factors are the leading forces in a cultivated science park like the OVC. However, along with the shifting focus of the local government, the OVC's industrial scale has remained small and its industrial chain has remained incomplete. Moreover, the lack of trust and interactions between various components in this innovation system has been highly noticeable. All these features may be seen as warnings to the OVC that a revision of this innovation system is needed in order to avoid the fate of becoming an 'optical illusion'. ; Postprint ; Peer reviewed

Ausgehend von der Annahme, daß eine starke Unabhängigkeit der Zentralbank die Inflationsbekämpfung erleichtert, untersucht der Autor die Auswirkungen dieser Unabhängigkeit auf die Beschäftigung unter der Voraussetzung unterschiedlicher Tarifsysteme. so trägt eine große Zentralbankunabhängigkeit zum Rückgang der Inflation ohne negative Beschäftigungseffekte dort bei, wo Lohnverhandlungen in einem koordinierten Rahmen staatfinden, führt aber zu einem Anstieg der Arbeitslosigkeit, wo solch eine Koordinierung nicht gegeben ist. Im Hinblick auf die Währungsunion ist in diesem Zusammenhang von einem Anstieg der Arbeitslosigkeit auszugehen. (swp-clv)

We analyse additive regression model fitting via the backfitting algorithm. We show that in the case of a large class of curve estimators, which includes regressograms, simple step‐by‐step formulae can be given for the back‐fitting algorithm. The result of each cycle of the algorithm may be represented succinctly in terms of a sequence of d projections in n‐dimensional space, where d is the number of design coordinates and n is sample size. It follows from our formulae that the limit of the algorithm is simply the projection of the data onto that vector space which is orthogonal to the space of all n‐vectors fixed by each of the projections. The formulae also provide the convergence rate of the algorithm, the variance of the backfitting estimator, consistency of the estimator, and the relationship of the estimator to that obtained by directly minimizing mean squared distance.

Although increases in the use of automation have occurred across society, research has found that human operators often underutilize (disuse) and overly rely on (misuse) automated aids (R. Parasuraman & V. Riley, 1997). Nearly 275 Cameron University students participated in 1 of 3 experiments performed to examine the effects of perceived utility (M. T. Dzindolet, H. P. Beck, L. G. Pierce, & L. A. Dawe, 2001) on automation use in a visual detection task and to compare reliance on automated aids with reliance on humans. Results revealed a bias for human operators to rely on themselves. Although self-report data indicate a bias toward automated aids over human aids, performance data revealed that participants were more likely to disuse automated aids than to disuse human aids. This discrepancy was accounted for by assuming human operators have a "perfect automation" schema. Actual or potential applications of this research include the design of future automated decision aids and training procedures for operators relying on such aids.

Objective: This investigation examined the impact of human—machine competition (John Henry effects) on intent errors. John Henry effects, expressed as an unwillingness to use automation, were hypothesized to increase as a function of operators' personal investment in unaided performance. Background: Misuse and disuse often occur because operators (a) cannot determine if automation or a nonautomated alternative maximizes the likelihood of task success (appraisal errors) or (b) know the utilities of the options but disregard this information when deciding to use or not to use automation (intent errors). Although appraisal errors have been extensively studied, there is a paucity of information regarding the causes and prevention of intent errors. Methods: Operators were told how many errors they and an automated device made on a target detection task. Self-reliant operators (high personal investment) could depend on their performance or automation to identify a target. Other-reliant operators (low personal investment) could rely on another person or automation. Results: As predicted, self-reliance increased disuse and decreased misuse. Conclusion: When the disuse and misuse data are viewed together, they strongly support the supposition that personal investment in unaided performance affects the likelihood of John Henry effects and intent errors. Application: These results demonstrate the need for a model of operator decision making that takes into account intent as well as appraisal errors. Potential applications include developing interventions to counter the deleterious effects of human—machine competition and intent errors on automation usage decisions.