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, C.; 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 Engineering maintenance decision-making with unsupported judgement under operational constraints(Elsevier, 2022-05-10) Green, Richard N.; McNaught, Ken R.; Saddington, A. 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 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 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, A. 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 Embargo 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.