Modelling and assessment of dependent performance shaping factors through Analytic Network Process
Introduction
One of the undisputed assumptions in all Human Reliability Analysis (HRA) methods is that the human performance depends on the conditions under which the tasks or activities are carried out [1], [2]. These conditions, which can be related to both personal and environmental factors, have generally been referred to as performance shaping factors (PSFs) or performance influencing factors (PIFs). Although many authors have developed different competing lists of various lengths [1], there is a general agreement on the core set of these factors [3]. On the other hand, very different conceptual and analytical models are proposed for describing how these factors exert their influence on the human error probability (HEP); indeed if a PSF has an effect on human performance it is crucial to account for how this influence comes about.
One of the major challenges of modelling the influence of PSFs is how to represent and quantify the dependencies among PSFs [4]. Typically, HRA methods try to provide guidance as to how to treat dependencies at the level of the factor assessments; yet this guidance is hard to provide.
In general, it is possible to recognise two types of dependency among PSFs (Fig. 1):
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dependency between the states of the PSFs: the presence or the state of one PSF influences the state of another (e.g. a high level of stress increases the probability of low attention);
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dependency between the influences (impacts) of the PSFs on the human performance: the state of one PSFs increases the influence (impact) of another PSF on the HEP without changing its state (e.g. a poor state of the PSF “team factors” affects the influence of “personal factor” on the HEP).
Among HRA methods, which cope with dependency issues between PSFs, it is particularly worthy to cite the Information, Decision and Action in Crew Context model (IDAC) [5], the Cognitive Reliability Error Analysis Method (CREAM) [2], the Standardised Plant Analysis Risk-Human Reliability Analysis (SPAR-H) [6]. Some of them describe how the PSFs affect each other merely in a qualitative way (e.g. CREAM [2]), whereas some recent methods try to describe analytically the mutual dependencies among the states of PSFs and result in a very complex application that requires a great deal of effort by the analyst (e.g. IDAC [3]). Hallbert et al. [7] discusses how empirical data could be helpful for determining their size effect, their relative effects, as well as their interactions but they do not provide a procedure to guide the analysts on using these data.
This paper deals with the development of a framework for modelling the mutual influences existing among PSFs and a related method to assess the importance of each PSF in influencing the performance of an operator, in a specific context, considering these interactions (Fig. 1B). Furthermore, the proposed model allows to continue to use linear weighting methods for modelling the functional relationship between HEP and related PSFs (e.g. SLIM [8]), thus keeping the computational model relatively simple.
The paper is organised as follows. Section 2 summarises the state-of-the-art on modelling dependencies among PSFs. Section 3 introduces the theory of Analytic Network Process (ANP; [12], [13], [14]), its use for assessing dependencies among PSFs, and describes the proposed method based on the integration of ANP and SLIM. In order to test its applicability, the method has been included in the cognitive simulator PROCOS [9], [10], [11] and a pilot study in Air Traffic Control has been performed. By this way, using the same scenario and data reported in [11], the pilot study also returns a preliminary assessment of the variation of HEP estimates when a model of mutual dependent PSFs is used. Section 4 describes the pilot application, while Section 5 discusses the results. Finally, the last section summarises the main findings and the conclusions.
Section snippets
State of art review of approaches to modelling dependencies among PSFs
A number of research studies assume and describe, at least in a qualitative way, the dependencies between PSFs, which should be taken into account in analysing human performance and assessing HEP [15]. To this end, considering a specific HRA study, the dependencies between the list of PSFs lying in the selected taxonomy can be postulated referring to these studies and with the support of experts with professional experience or knowledge in the reference domain.
Nevertheless, only few approaches
Modelling dependent PSFs through ANP
The Analytic Network Process (ANP, [12]) is a general theory of relative measurement used to derive composite-priority-ratio scales from individual-ratio scales that represent relative influence of factors that interact with respect to control criteria [13]. ANP is already applied in many areas such as economic, social and political decisions, and also technological design [14]. Thus, the Analytic Network Process is a powerful method suitable to evaluate the contribution of each PSF to HEP, by
A case study in Air Traffic Control
This section describes the application of the proposed method on a case study developed in the domain of Air Traffic Control. The application of the proposed method was directed towards a case study already developed within the ConOps framework adopted by Eurocontrol [28]. The framework identifies the functions and processes, and their corresponding interactions and information flows, main actors and their roles and responsibilities in possible future ATM European Operations. Whitin ConOps is
Main findings and conclusions
The concept of Performance shaping factor (or influencing factor) supports a simplified modelling of the interaction between the operator and the operational context. The assumption of independent PSFs has been frequently used in the past for the sake of simplicity of HEP quantification. Nevertheless, in order to better determine the effects of PSFs on the operator’s performance it is necessary to take into account their mutual dependencies, which encompass two different mechanisms:
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dependency
Acknowledgements
The authors are very grateful to Maurizio Salvestrini and the other members of Malpensa and Linate Control Towers (Milan, Italy) for their valuable and enthusiastic support in developing the material necessary for this study.
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