Design and Evaluation of Simulations for the Development of Complex Decision-Making Skills

2002 ◽  
Vol 16 (4) ◽  
pp. 239-248 ◽  
Author(s):  
Roger Hartley ◽  
Glen Varley

The management of large-scale incidents, such as demonstrations that can affect public order, requires complex decision making. In association with the London Metropolitan Police a computer-based simulation (CACTUS) was designed for improving the strategic and tactical management of public order events by senior police officers. It incorporates a digitized map with active (iconized) police, crowd and hostile agents able to navigate the map and interact autonomously in ways that simulate aggression and disorder if the police resources and their instructions are not managed with some skill. Adaptive training scenarios were designed in CACTUS by the trainer/facilitators covering planning, event management and debriefing. An evaluation study collected audio and video records of the training sessions and these data gave useful insights into the decision-making processes and how the CACTUS simulation, through its design features, became a dynamic mediational tool in developing such skills.

2018 ◽  
pp. 93-103
Author(s):  
Алексей Николаевич Рева ◽  
Шахин Шахвели-оглы Насиров ◽  
Бала Мушгюль-оглы Мирзоев

The human factor problem should be solved by identifying, qualifying and preventing the erroneous actions of the air traffic controllers. It is presented two schemes explaining the structure of human qualimetry factor and the interaction of the components of the ICAO safety concept, where the main emphasis is on an aviation personnel’ attitude to dangerous actions or conditions, which is revealed by the qualimetry of the decision-making processes’ characteristics: the attitude towards risk (the main dominants and fuzzy assessments), levels of claims, dangerous qualities and preferences systems. The preferences systems are considered as ordered characteristics and indicators of professional activity, which are subjectively compared with the positions of influence on flight safety. The spectrum of n = 21 characteristic errors was formed considering the recommendations of ICAO, EUROCONTROL and accident statistics. It is determined that procedures of collecting the information of errors danger contribute their recognition, memorization, and avoidance: controllers who passed the test according to the proposed method before training made by one third fewer errors in its process. Two criteria for assessing group preferences are realized: the level of consensus (known as Kendall’s coefficient of concordance) and the severity of the ranking, determined by the presence of "related" ranks, for which a special indicator is introduced. It is defined that this indicator should be determined both for the sample of respondents and for the preferences group systems of developed with the chosen method of individual opinions’ aggregation. It was performed the comparative analysis of complex decision-making strategies of effectiveness in the construction of a preferences group systems m = 65 controllers: sum and averaging of ranks, classical criteria (Wald's, Savage's and Laplace's criterion), optimal prediction, applying the non-parametric optimization of the preferences group systems. The non-parametric optimization of the group system of pre-readings was carried out by Kemeny median and it was proved that it was the closest to all the results obtained by other methods and strategies


Author(s):  
Aidé Maldonado-Macías ◽  
Jorge Luis García-Alcaraz ◽  
Francisco Javier Marrodan Esparza ◽  
Carlos Alberto Ochoa Ortiz Zezzatti

Advanced Manufacturing Technology (AMT) constitutes one of the most important resources of manufacturing companies to achieve success in an extremely competitive world. Decision making processes for the Evaluation and Selection of AMT in these companies must lead to the best alternative available. Industry is looking for a combination of flexibility and high quality by doing significant investments in AMT. The proliferation of this technology has generated a whole field of knowledge related to the design, evaluation and management of AMT systems which includes a broad variety of methodologies and applications. This chapter presents a theoretical review of the term AMT, its diverse classification and a collection of the most effective multi-attribute models and methodologies available to support these processes. Relevant advantages are found in these models since they can manage complex decision making problems which involve large amount of information and attributes. These attributes frequently can be tangible and intangible when vagueness and uncertainty exist. There are several multi-attribute methodologies which are extensively known and used in literature; nevertheless, a new fuzzy multi-attribute axiomatic design approach is explained for an ergonomic compatibility evaluation of AMT.


2010 ◽  
pp. 135-143 ◽  
Author(s):  
Udo Richard Averweg

Decision support systems (DSS) deal with semi-structured problems. Such problems arise when managers in organisations are faced with decisions where some but not all aspects of a task or procedure are known. To solve these problems and use the results for decision-making requires judgement of the manager using the system. Typically such systems include models, data manipulation tools, and the ability to handle uncertainty and risk. These systems involve information and decision technology (Forgionne, 2003). Many organisations are turning to DSS to improve decision-making (Turban, McLean, & Wetherbe, 2004). This is a result of the conventional information systems (IS) not being sufficient to support an organisation’s critical response activities—especially those requiring fast and/or complex decision-making. In general, DSS are a broad category of IS (Power, 2003). A DSS is defined as “an interactive, flexible, and adaptable computer-based information system, specially developed for supporting the solution of a non-structured management problem for improved decision-making. It utilises data, it provides easy user interface, and it allows for the decision maker’s own insights” (Turban, 1995). There is a growing trend to provide managers with IS that can assist them in their most important task—making decisions. All levels of management can benefit from the use of DSS capabilities. The highest level of support is usually for middle and upper management (Sprague & Watson, 1996). The question of how a DSS supports decision-making processes will be described in this article. This article is organised as follows: The background to decisionmaking is introduced. The main focus (of this article) describes the development of the DSS field. Some future trends for the DSS field are then suggested. Thereafter a conclusion is given.


Author(s):  
Udo Richard Averweg

Decision support systems (DSS) deal with semi-structured problems. Such problems arise when managers in organisations are faced with decisions where some but not all aspects of a task or procedure are known. To solve these problems and use the results for decision-making requires judgement of the manager using the system. Typically such systems include models, data manipulation tools, and the ability to handle uncertainty and risk. These systems involve information and decision technology (Forgionne, 2003). Many organisations are turning to DSS to improve decision-making (Turban, McLean, & Wetherbe, 2004). This is a result of the conventional information systems (IS) not being sufficient to support an organisation’s critical response activities—especially those requiring fast and/or complex decision-making. In general, DSS are a broad category of IS (Power, 2003). A DSS is defined as “an interactive, flexible, and adaptable computer-based information system, specially developed for supporting the solution of a non-structured management problem for improved decision-making. It utilises data, it provides easy user interface, and it allows for the decision maker’s own insights” (Turban, 1995). There is a growing trend to provide managers with IS that can assist them in their most important task—making decisions. All levels of management can benefit from the use of DSS capabilities. The highest level of support is usually for middle and upper management (Sprague & Watson, 1996). The question of how a DSS supports decision-making processes will be described in this article. This article is organised as follows: The background to decisionmaking is introduced. The main focus (of this article) describes the development of the DSS field. Some future trends for the DSS field are then suggested. Thereafter a conclusion is given.


2019 ◽  
Vol 25 (2) ◽  
pp. 213-235 ◽  
Author(s):  
Soumava Boral ◽  
Sanjay Kumar Chaturvedi ◽  
V.N.A. Naikan

Purpose Usually, the machinery in process plants is exposed to harsh and uncontrolled environmental conditions. Even after taking different types of preventive measures to detect and isolate the faults at the earliest possible opportunity becomes a complex decision-making process that often requires experts’ opinions and judicious decisions. The purpose of this paper is to propose a framework to detect, isolate and to suggest appropriate maintenance tasks for large-scale complex machinery (i.e. gearboxes of steel processing plant) in a simplified and structured manner by utilizing the prior fault histories available with the organization in conjunction with case-based reasoning (CBR) approach. It is also demonstrated that the proposed framework can easily be implemented by using today’s graphical user interface enabled tools such as Microsoft Visual Basic and similar. Design/methodology/approach CBR, an amalgamated domain of artificial intelligence and human cognitive process, has been applied to carry out the task of fault detection and isolation (FDI). Findings The equipment failure history and actions taken along with the pertinent health indicators are sufficient to detect and isolate the existing fault(s) and to suggest proper maintenance actions to minimize associated losses. The complex decision-making process of maintaining such equipment can exploit the principle of CBR and overcome the limitations of the techniques such as artificial neural networks and expert systems. The proposed CBR-based framework is able to provide inference with minimum or even with some missing information to take appropriate actions. This proposed framework would alleviate from the frequent requirement of expert’s interventions and in-depth knowledge of various analysis techniques expected to be known to process engineers. Originality/value The CBR approach has demonstrated its usefulness in many areas of practical applications. The authors perceive its application potentiality to FDI with suggested maintenance actions to alleviate an end-user from the frequent requirement of an expert for diagnosis or inference. The proposed framework can serve as a useful tool/aid to the process engineers to detect and isolate the fault of large-scale complex machinery with suggested actions in a simplified way.


Author(s):  
John Bang Mathiasen ◽  
Henning de Haas

This study aims to understand the extent of superfluous work at shop floors and suggests some managerial opportunities for reducing superfluous work. Drawing on the abductive reasoning, the research systematically combines a theoretical conceptualisation of decision-making processes in a digitalised manufacturing with an empirical enquiry of a smart manufacturing. The paper reveals superfluous work if decision-making processes cross disciplinary and/or organisational boundaries. Superfluous work occurs because of lacking data and information to guide reflective thinking and knowledge sharing. In relation to high complex decision making the ongoing implementation of workarounds does also cause superfluous work. Prerequisites for reducing superfluous work are accessibility of applicable data to guide reflective thinking and knowledge sharing.


2013 ◽  
Vol 27 (3) ◽  
pp. 113-123 ◽  
Author(s):  
Evelien Kostermans ◽  
Renske Spijkerman ◽  
Rutger C. M. E. Engels ◽  
Harold Bekkering ◽  
Ellen R. A. de Bruijn

Different theoretical accounts have attempted to integrate anterior cingulate cortex involvement in relation to conflict detection, error-likelihood predictions, and error monitoring. Regarding the latter, event-related potential studies have identified the feedback-related negativity (FRN) component in relation to processing feedback which indicates that a particular outcome was worse than expected. According to the conflict-monitoring theory the stimulus-locked N2 reflects pre-response conflict. Assumptions of these theories have been made on the basis of relatively simple response-mapping tasks, rather than more complex decision-making processes associated with everyday situations. The question remains whether expectancies and conflicts induced by everyday knowledge similarly affect decision-making processes. To answer this question, electroencephalogram and behavioral measurements were obtained while participants performed a simulated traffic task that varied high and low ambiguous situations at an intersection by presenting multiple varying traffic light combinations. Although feedback was kept constant for the different conditions, the tendency to cross was more pronounced for traffic light combinations that in reallife are associated with proceeding, as opposed to more ambiguous traffic light combinations not uniquely associated with a specific response. On a neurophysiological level, the stimulus-locked N2 was enhanced on trials that induced experience-based conflict and the FRN was more pronounced for negative as compared to positive feedback, but did not differ as a function of everyday expectancies related to traffic rules. The current study shows that well-learned everyday rules may influence decision-making processes in situations that are associated with the application of these rules, even if responding accordingly does not lead to the intended outcomes.


Author(s):  
Steven Walczak ◽  
Deborah L. Kellogg ◽  
Dawn G. Gregg

Purchase processes often require complex decision making and consumers frequently use Web information sources to support these decisions. However, increasing amounts of information can make finding appropriate information problematic. This information overload, coupled with decision complexity, can increase time required to make a decision and reduce decision quality. This creates a need for tools that support these decision-making processes. Online tools that bring together data and partial solutions are one option to improve decision making in complex, multi-criteria environments. An experiment using a prototype mashup application indicates that these types of applications may significantly decrease time spent and improve overall quality of complex retail decisions.


Author(s):  
Robert McLaughlan ◽  
Denise Kirkpatrick

Decision-making processes in relation to complex natural resources require recognition and accommodation of diverse and competing perspectives in a decision context that is frequently ill defined and fraught with value judgements. Online environments can be used to develop students’ skills and understanding of these issues. The focus of this chapter is the learning design of an online roleplay-simulation (Mekong e-Sim) which was created to develop learning experiences about these types of issues across multiple institutions with students from the disciplines of engineering and the humanities. The key stages of interaction within the e-Sim are described and linked to student tasks, resources, and supports. The evolution and adaptation of the learning design used in the Mekong e-Sim has been described. Eight key challenges in the design and implementation of online roleplay-simulations have been identified. In this chapter, we have tried to address a gap in the online role-based collaborative learning literature about the design of these activities, linkages between pedagogy and information and communication technology, and how to exploit these linkages for effective learning.


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