Browsing by Author "McNaught, Ken R."
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Item Open Access Comparing a simulation model with various analytic models of the international diffusion of consumer technology(Elsevier, 2015-09-28) Swinerd, Chris; McNaught, Ken R.In this paper we propose and evaluate a method for studying technology adoption at the national level using hybrid simulation. A hybrid simulation model is developed which combines elements of system dynamics and agent-based modelling, and treats nations as adopting agents. International diffusion is modelled as a social system where the adoption of an innovation, or even just growing pressure to adopt an innovation, in one nation can then influence its adoption in others. The model is used to investigate nine different technological innovations for which sufficient international data are available. Using the available empirical data, the method of differential evolution is used to configure the model which allows the parameter space to be explored in an efficient manner, without bias or subjective disagreement. Good agreement is found between the parameters derived in this way and those reported to configure analytic models. For each of the nine innovations, we report the rank order correlation between the actual order of adoption of the innovations by nations and the order predicted by the simulation model. We also report the rank order correlations between the actual order and the order predicted by a much simpler statistical model. Improvements in the rank order correlation are shown when some form of social influence between nations is included, although there is no significant difference in results between the four different types of social influence considered by the simulation. The nine technologies investigated also appear to fall into two groups with significantly different uptake speeds. Advantages and limitations of the approach are discussed along with suggested implications for practice.Item Open Access Design classes for hybrid simulations involving agent-based and system dynamics models(Elsevier Science B.V., Amsterdam., 2012-06-30T00:00:00Z) Swinerd, Chris; McNaught, Ken R.Hybrid simulation involves the use of multiple simulation paradigms, and is becoming an increasingly common approach to modelling modern, complex systems. Despite growing interest in its use, little guidance exists for modellers regarding the nature and variety of hybrid simulation models. Here, we concentrate on one particular hybrid – that involving agent-based and system dynamics models. Based on an up-to-date review of the literature, we propose three basic types of hybrid agent-based system dynamics simulations, referred to here as interfaced, integrated and sequential hybrid designs. We speculate that the classification presented may also be useful for other classes of hybrid simulations.Item Open Access Developing a Decision Analytic Framework Based on Influence Diagrams in Relation to Mass Evacuations(2013-07-03) Zagorecki, A.; McNaught, Ken R.In this paper, we examine the role which decision analysis can play in a situation requiring a mass evacuation. In particular, we focus on the influence diagram as a tool for reasoning and supporting decision-makers under conditions of risk and uncertainty. This powerful modelling tool can help to bridge multiple specialist domains and provide a common framework for supporting decision-makers in different agencies. An influence diagram is also referred to as a decision network and can be considered as an extension of a Bayesian network. Like a Bayesian network, it contains chance nodes which represent random variables and deterministic nodes which represent deterministic functions of input variables. However, in addition, an influence diagram contains decision nodes which represent decisions under local control and utility nodes which can represent a variety of costs and benefits. These might be measured in several dimensions including casualties and monetary units. Advantages of Bayesian networks and influence diagrams over more traditional risk and safety modelling approaches such as event trees and fault trees are discussed - in particular, the ease with which they represent dependencies between many factors and the different types of reasoning supported at the same time, e.g. predictive reasoning and diagnostic reasoning. An illustrative, generic influence diagram is presented of a situation corresponding to a CBRNE attack. We then consider how this generic model can be applied to a more specific scenario such as an attack at a sporting event. A variety of potential uses of the model are identified and discussed, along with problems which are likely to be encountered in model development. We argue that this modelling approach provides a useful framework to support cost-effectiveness studies and high-level trade-offs between alternative possible security measures and other resources impacting on response and recovery operations.Item Open Access Development of an intelligent system for operator support during wireless infrastructure system testing(2009-12-02T16:36:46Z) Chan, A. K. W.; McNaught, Ken R.Intense competition and the requirement to continually drive down costs within a mature mobile telephone infrastructure market calls for new and innovative solutions to process improvement. One particular challenge is to improve the quality and reliability of the diagnostic process for systems testing of GSM and UMTS products. In this thesis, we concentrate on a particularly important equipment type – the Base Transceiver Station (BTS). The BTS manages the radio channels and transfers signalling information to and from mobile stations (i.e. mobile phones). Most of the diagnostic processes are manually operated and rely heavily on individual operators and technicians' knowledge for their performance. Hence, there is a high cost associated with trouble-shooting in terms of time and manpower. To address this issue, we employ Bayesian networks (BNs) to model the domain knowledge that comprises the operations of the System Under Test (SUT), Automated Test Equipment (ATE) and the diagnostic skill of experienced engineers, in an attempt to enhance the efficiency and reliability of the diagnostic process. The proposed automated diagnostic tool (known as Wisdom) consists of several modules. An intelligent user interface will provide possible solutions to test operators / technicians; to capture their responses, and to activate the automated test programme. Server and client software architecture will be used to integrate Wisdom with the ATE seamlessly and to maintain Wisdom as an independent module. A local area network will provide the infrastructure for managing and deploying the multimedia and text information in real time. We describe how a diagnostic model can be developed and implemented using a Bayesian network approach. We also describe how the resulting process of diagnosis following failure, advice generation and subsequent actions by the operator are handled interactively by the prototype system.Item Open Access Engineering maintenance decision-making with unsupported judgement under operational constraints(Elsevier, 2022-05-10) Green, Richard N.; McNaught, Ken R.; Saddington, Alistair J.In operational engineering maintenance situations, limitations on time, resource or the information available often inhibit rigorous analysis on complex decision problems. Decision-makers who are compelled to act in such circumstances, may be informed by some level of analysis if available, or else may have to rely on their unsupported judgement. This paper presents three engineering risk decision-making case studies across a 20 year span from the rail, aerospace, and military aviation contexts, highlighting the fallibilities of using unsupported judgements in an unstructured manner. To help situate this type of decision situation, we provide a descriptive model of the decision space which extends an existing description from the discipline of decision analysis. Furthermore, to help make and describe the distinction between unsupported and supported thinking, we provide another descriptive model, this time drawing parallels with the distinction made between Type 1 and Type 2 reasoning. This model is an extension of the default-interventionist model from cognitive psychology. The paper concludes that there is a pressing need to provide some form of support to engineering decision-makers facing operational decisions under severe time pressure. While the ultimate aim must be to improve the quality of decision-making, improved transparency is an important additional benefit. Increased emphasis on decision justification and self-awareness are suggested as potential ways of improving this situation. A further contribution of this paper is to identify and strengthen linkages between safety science and two other relevant disciplines, decision analysis and psychology. Such linkages make it easier to communicate across traditional disciplinary boundaries and may provide opportunities for interdisciplinary learning or suggest future directions for collaborative research.Item Open Access Evaluation of soft tissue simulant performance against economic and environmental impact(Royal Society of Chemistry, 2024-02-22) Read, James; McNaught, Ken R.; Hazael, Rachael; Critchley, RichardSoft tissue simulants are traditionally used to provide a post impact medium suitable for replicating human anatomy. Performance of materials is therefore paramount, and the analysis of such experimentation relies on responses that mimic the various tissue, bone and muscle groups contained within the human body. However, with an increasing global push to reduce carbon emissions and increase sustainability, current materials require examination to ensure research establishments remain at the forefront of environmentally friendly practices. To date, the literature contains little in relation to how environmentally friendly the use and supply of soft tissue simulants is. The aim of the research is to provide researchers with primary data to support decisions on material selection for ballistic simulation research. The need arises due to the high cost and environmental impact of existing materials. To explore this research gap, a series of 5.5 mm ball bearings were fired from a gas gun at velocity ranges between 122 and 526 m s−1 to examine the performance characteristics of six commercially available soft tissue simulants and a foodstuffs grade gelatine that represented a more cost effective environmentally friendly alternative. A structured multi-criteria decision analysis approach was employed to compare the overall effectiveness of the alternative materials. It was found that whilst PermaGel, 20 and 10% ballistic gelatine performed the most advantageously respectively during experimental testing, qualitative environmental assessment showed ballistic soap, PermaGel and foodstuffs gelatine to be most advantageous. The information provided within this study will enable researchers to make more informed decisions on both economic and environmental implications when sourcing materials for use within survivability assessment, whilst further work would increase awareness and viability of alternative materials.Item Open Access Introducing Bayesian belief updating as a method to counter improvised explosive devices: a qualitative case study on identifying human behaviours associated with explosive chemical precursor diversion(Springer, 2023-08-21) Collett, Gareth; Ladyman, Melissa; Temple, Tracey; Hazael, Rachael; McNaught, Ken R.Countering improvised explosive devices (C-IED) is a significant theme of the twenty-first century, particularly in regions with limited governance and a fragile rule of law. Many strands of activity are involved, with human interaction proving difficult to predict. However, Bayesian belief updating (used across several academic fields to provide insight into human behaviours) has never been considered. Given the breadth of C-IED, this research focusses on a state affected by conflict, and where illicit diversion of explosive chemical precursors (ECP) for IED manufacture is supported by the population. It aims to represent (both visually and probabilistically) a methodology by which human relationships could be better understood, thereby promoting belief updating as new evidence becomes available. Such belief updating would refine focus and improve resource mobilisation.Item Open Access Investigating the applicability of Bayesian networks to demand forecasting during the final phase of support operations(2019-03) Boutselis, Petros; McNaught, Ken R.; Zagorecki, AdamA challenge faced by businesses that provide logistical support to systems is when the provision of those support services is no longer required. A typical example of such a situation is when military operations come to an end. In such cases, those companies that have a contract with the Armed Forces to provide maintenance support for the deployed systems, need to maintain those systems at minimum cost during that final phase, that is from the time the decision to stop the operations is announced until their very end. During the final phase, a challenging problem is forecasting the demand for spare parts, corresponding to equipment failures within the system. This is because the support context, the number of supported systems, the support equipment or even the operational demand can change during that period, and also because there can be very limited opportunities to place orders to cover demand. This thesis suggests that these types of problems can take advantage of the data that have been collected during the support operations prior to the initiation of the closing down process. Moreover, the thesis investigates the exploitation of these data by the use of Bayesian Networks to forecast the demand for spares that will be required for the provision of maintenance during the final phase. The research uses stochastically simulated Support Chain scenarios to generate data and also to evaluate different methods of constructing Bayesian Networks. The simulated scenarios differ in the demand context as well as in the complexity of the Equipment Breakdown Structure of the supported systems. The Bayesian Networks’ structure development methods that are tested include unsupervised machine learning, eliciting the structure from Subject Matter Experts, and two hybrid approaches that combine expert elicitation and machine learning. These models are compared to respective logistic regression models, as well as subject matter experts-adjusted single exponential smoothing forecasts. The comparison of the models is made using both accuracy metrics and accuracy implication metrics. These forecast models’ comparison methods are analysed in order to evaluate their appropriateness. The analyses have provided a number of novel outputs. The algebraic analysis of the accuracy metrics theoretically proves empirical problems that have been discussed in the literature but also reveals others. Regarding the accuracy implication metrics, the analysis shows that for the particular type of problems examined in this thesis –final phase problems – the accuracy implication metrics commonly applied are not enough to inform decision making, and a number of additional ones are required.The research shows that for the scenarios examined, the Bayesian Networks that had their structure learned using an unsupervised algorithm performed better in the accuracy metric than any of the other models. However, even though these Bayesian Networks also did well with the accuracy implication metrics, neither they, nor any of the others was consistently dominant. The reason for the discrepancy in the results between the accuracy and the accuracy implication metrics is that the latter are not only driven by how accurate the forecast model’s prediction is, but also by the model of the residual error and the bias.Item Open Access Investigating the applicability of bayesian networks to the analysis of military intelligence(Cranfield University, 2008-08-04T13:43:47Z) Carr, Sophie; McNaught, Ken R.Intelligence failures have been attributed to an inability to correlate many small pieces of data into a larger picture. This thesis has sought to investigate how the fusion and analysis of uncertain or incomplete data through the use of Bayesian Belief Networks (BBN) compares with people’s intuitive judgements. These flexible, robust, graphical probabilistic networks are able to incorporate values from a wide range of sources including empirical values, experimental data and subjective values. Using the latter, elicited from a number of serving military officers, BBNs provide a logical framework to combine each individual’s set of one-at-a-time judgements, allowing comparisons with the same individuals’ many-at-a-time, direct intuitive judgements. This was achieved through a serie s of fictitious and historical case studies. Building upon this work, another area of interest was the extent to which different elicitation techniques lead to equivalent or differing judgements. The techniques compared were: direct ranking of the variables’ perceived importance for discriminating between given hypotheses, likelihood ratios and conditional probabilities. The experimental results showed that individuals were unable to correctly manipulate the dependencies between information as evidence accumulated. The results also showed varying beliefs about the importance of information depending upon the elicitation technique used. Little evidence was found of a high correlation between direct normative rankings of variables’ importance and those obtained from the BBNs’ combination of one-at-a-time judgements. Likelihood values should only be used as an elicitation technique by those who either regularly manipulate uncertain information or use ratios. Overall, conditional probability distributions provided the least troublesome elicitation technique of subjective preferences. In conclusion, Bayesian Belief Networks developed through the use of subjective probability distributions offer a flexible, robust methodology for the development of a normative model for the basis of a decision support system for the quantitative analysis of intelligence data.Item Open Access On the design of hybrid simulation models, focussing on the agent-based system dynamics combination(2014-08-15) Swinerd, Chris; McNaught, Ken R.There is a growing body of literature reporting the application of hybrid simulations to inform decision making. However, guidance for the design of such models, where the output depends upon more than one modelling paradigm, is limited. The benefits of realising this guidance include facilitating efficiencies in the general modelling process and reduction in project risk (both across measures of time, cost and quality). Focussing on the least well researched modelling combination of agent-based simulation with system dynamics, a combination potentially suited to modelling complex adaptive systems, the research contribution presented here looks to address this shortfall. Within a modelling process, conceptual modelling is linked to model specification via the design transition. Using standards for systems engineering to formally define this transition, a critical review of the published literature reveals that it is frequently documented. However, coverage is inconsistent and consequently it is difficult to draw general conclusions and establish best practice. Therefore, methods for extracting this information, whilst covering a diverse range of application domains, are investigated. A general framework is proposed to consistently represent the content of conceptual models; characterising the key elements of the content and interfaces between them. Integrating this content in an architectural design, design classes are then defined. Building on this analysis, a decision process is introduced that can be used to determine the utility of these design classes. This research is benchmarked against reported design studies considering system dynamics and discrete-event simulation and demonstrated in a case study where each design archetype is implemented. Finally, the potential for future research to extend this guidance to other modelling combinations is discussed.Item Open Access Prognostic Modelling with Dynamic Bayesian Networks(2009-11-04T00:00:00Z) McNaught, Ken R.; Zagorecki, A.In this paper, we review the application of dynamic Bayesian networks to prognostic modelling. An example is provided for illustration. With this example, we show how the equipment’s reliability decays over time in the situation where repair is not possible and then how a simple change to the model allows us to represent different maintenance policies for repairable equipmenItem Open Access Seabed Protection Systems to prevent Scour from High-Speed Ships(2010-11-04) Evans, G.; Jolly, C. K.; McNaught, Ken R.This document reviews the scour protection systems required around port structures where these are to be used for the berthing of vessels powered by water jet systems. The development of a scour protection system at Poole Harbour in Dorset has been documented and reviewed and a series of laboratory investigations were then undertaken. This has enabled a greater understanding of the scour mechanisms from the water jet propulsion systems of High Speed Ships. This work has shown that current design guidance on scour protection is not appropriate for use on berths used by High Speed Ships, that failure of these systems can occur rapidly and catastrophically, and secondary effects from water jets may promote the failure of quay walls. The scour protection system should comprise two individual elements, a filter layer and an armour layer. It has been found that systems involving individual isolated armour units are inappropriate and prone to failure and that shaped linked armour blocks need to be used. The loads on the armour layer were also found to be oscillatory and the materials used for both the armour and filter layers need to be designed for cyclic fatigue loading and fretting. Water jets are also capable of reducing the strength of permeable, seabed strata.Item Open Access Simulating the diffusion of technological innovation with an integrated hybrid agent-based system dynamics model(Taylor and Francis, 2014-02-28) Swinerd, Chris; McNaught, Ken R.The potential of hybrid models to enhance simulations of the real world is explored. While the scope for design of such models is large, the focus here brings together agent-based (AB) and system dynamics (SD) modelling within a defined architectural framework. Comprising a number of modules, each of which is implemented in a single modelling paradigm, the design of hybrid models looks to exploit the potential from a range of approaches and tools. Coded within a single programming environment, the international diffusion of technological innovation is used as a case study to highlight hybrid simulation model design and implementation. An integrated hybrid simulation design that incorporates feedback between modules in a continuous, fluid, process is employed to develop a model comprising two SD modules and one AB module. The predictions from the hybrid model are compared to known outcomes regarding the national adoption of mobile telephony, fixed internet and fixed broadband. We conclude with some thoughts on the design of hybrid simulation models.Item Open Access Supporting operational decision making concerning aircraft structural integrity damage identified during maintenance.(2021-06) Green, Richard N.; McNaught, Ken R.; Saddington, Alistair J.Military aircraft operations balance delivery pressures and engineering risks. Aircraft structural damage incurred in-service creates complex risk decision problems for managers deliberating maintenance activity such as delaying rectification to continue operations, or grounding an aircraft or entire fleet. In many operational settings, aircraft availability demands restrict the time, information, or resources to analyse structural risks, making formal risk or decision analysis intractable. Exact solutions are information intensive and require specialist knowledge or machinery beyond the capabilities of generalist engineering managers, often compelling decision-makers to use their subjective judgement in an unsupported way. For actors deliberating aircraft maintenance structural risks in such circumstances, a novel approach based upon heuristics, argument and bounded rationality is proposed, which was informed by the results from a survey of engineering practitioners and case study analyses. Testing of the approach was carried-out with 21 aircraft engineering decision-makers with experience of structural integrity risks, split into three groups, using realistic but fictional textual simulations of aircraft maintenance. One group used existing decision justification approaches and were compared with a second group who provided decision justifications using the novel approach. Users of the novel approach felt supported and were very confident in their justifications. The third group of raters comparing the two sets of decision justifications indicated preferences using Likert scales against the criteria: which is easier to understand, which is more transparent, and which gives the better justification. Analysis of the comparative results iii ABSTRACT iv using ANOVA provided evidence that the novel approach enabled better decision justification and transparency compared to existing approaches. The novel approach aids decision-makers compelled to use their unsupported subjective judgement, improving organisational resilience by improving robustness and stretching system process to handle surprises, and providing a clear record of the decision basis for post hoc reviewItem Open Access Supporting operational decision making concerning aircraft structural integrity damage identified during maintenance.(2021-06-10) Green, Richard; McNaught, Ken R.; Saddington, Alistair J.Military aircraft operations balance delivery pressures and engineering risks. Aircraft structural damage incurred in-service creates complex risk decision problems for managers deliberating maintenance activity such as delaying rectification to continue operations, or grounding an aircraft or entire fleet. In many operational settings, aircraft availability demands restrict the time, information, or resources to analyse structural risks, making formal risk or decision analysis intractable. Exact solutions are information intensive and require specialist knowledge or machinery beyond the capabilities of generalist engineering managers, often compelling decision-makers to use their subjective judgement in an unsupported way. For actors deliberating aircraft maintenance structural risks in such circumstances, a novel approach based upon heuristics, argument and bounded rationality is proposed, which was informed by the results from a survey of engineering practitioners and case study analyses. Testing of the approach was carried-out with 21 aircraft engineering decision-makers with experience of structural integrity risks, split into three groups, using realistic but fictional textual simulations of aircraft maintenance. One group used existing decision justification approaches and were compared with a second group who provided decision justifications using the novel approach. Users of the novel approach felt supported and were very confident in their justifications. The third group of raters comparing the two sets of decision justifications indicated preferences using Likert scales against the criteria: which is easier to understand, which is more transparent, and which gives the better justification. Analysis of the comparative results using ANOVA provided evidence that the novel approach enabled better decision justification and transparency compared to existing approaches. The novel approach aids decision-makers compelled to use their unsupported subjective judgement, improving organisational resilience by improving robustness and stretching system process to handle surprises, and providing a clear record of the decision basis for post hoc review.Item Open Access Towards a better framework for estimative intelligence – addressing quality through a systematic approach to uncertainty handling(Taylor and Francis, 2023-06-22) Isaksen, Bjorn G. M.; McNaught, Ken R.The analytic standards governing the production of intelligence are outlined in a number of Intelligence Community Directives (ICDs). In this paper, we are concerned with ICDs 203, 206 and 208 and, in particular, how these relate to the handling of uncertainty in estimative intelligence. An inductive thematic analysis is employed which identifies several recurring themes. In addition, a conceptual map is developed which highlights relationships and the level of inter-connectedness between the standards. Requirements for improved operationalization of uncertainty handling are also discussed. The question of analytic feasibility is then examined in relation to the five themes extracted from the earlier analysis. The paper concludes that a new framework for uncertainty handling is required and suggests that such a framework should contain a process to assess analytic feasibility from the outset of a study.Item Open Access Uncertainty handling in estimative intelligence–challenges and requirements from both analyst and consumer perspectives.(Taylor and Francis, 2019-02-22) Bg, Isaksen; McNaught, Ken R.Important assessments of events and activities relating to military, terrorist and hybrid adversaries and the intentions of foreign governments, are made every day, usually involving subjective or ‘estimative’ probabilities and an associated level of confidence. The way in which these uncertainties are accessed and communicated can potentially have enormous impact and consequences. Challenges are reinforced by increasingly complex intelligence problems for which the contemporary analytic paradigm is not tailored to cope. It is important to better understand how defence intelligence analysts and consumers handle uncertainty in their assessment and decision support activities and what challenges and requirements they face in doing so. This is mainly achieved by the use of semi-structured interviews with a sample of very senior consumers of military intelligence (mostly Flag Officers of the Norwegian Armed Forces) and focus group interviews with groups of Norwegian intelligence analysts. In general, respondents found it difficult or challenging to conceptualize uncertainty analytically. This has implications for the communication of uncertainty and its use in decision-making within the current framework. Secondly, respondents were receptive to suggested potential improvements to the existing framework. One such suggestion involved a differentiated framework, offering different levels of uncertainty resolution in different situations, although none of the respondents had any experience of such a framework for assessing or communicating uncertainty. We conclude with some recommendations to improve the process of uncertainty and risk communication in this important and consequential application area. Having particular implications for policy, we recommend that analysts follow a differentiated approach in handling different situations and problems comprising uncertainty, rather than pursuing a standard solution as is current practice.Item Open Access Using Bayesian Networks to forecast spares demand from equipment failures in a changing service logistics context(Elsevier, 2018-06-28) Boutselis, Petros; McNaught, Ken R.A problem faced by some Logistic Support Organisations (LSOs) is that of forecasting the demand for spare parts, corresponding to equipment failures within the system. Here we are particularly concerned with a final phase of operations and the opportunity to place only a single order to cover demand during this phase. The problem is further complicated when the service logistics context can change during this final phase, e.g. as the number of systems supported or the LSO's resources change. Such a problem is typical of the final phase of many military operations. The LSO operates the recovery and repair loop for the equipment in question. By developing a simulation of the LSO, we can generate synthetic operational data regarding equipment breakdowns, etc. We then split that data into a training set and a test set in order to compare several approaches to forecasting demand in the final operational phase. We are particularly interested in the application of Bayesian network models for this type of forecasting since these offer a way of combining hard observational data with subjective expert opinion. Different LSO configurations were simulated to create a test dataset and the simulation results were compared with the various forecasts. The BN that learned from training data performed best, followed by a hybrid BN design combining expert elicitation and machine learning, and then a logistic regression model. An expert-adjusted exponential smoothing model was the poorest performer and these differences were statistically significant. The paper concludes with a discussion of the results, some implications for practice and suggestions for future work.