A conceptual system design and managerial complexity competency model

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Complex adaptive systems are usually difficult to design and control. There are several particular methods for coping with complexity, but there is no general approach to build complex adaptive systems. The challenges of designing complex adaptive systems in a highly dynamic world drive the need for anticipatory capacity within engineering organizations, with a goal of enabling the design of systems that can cope with an unpredictable environment. This thesis explores this question of enhancing anticipatory capacity through the study of a complex adaptive system design methodology and complexity management competencies. A general introduction to challenges and issues in complex adaptive systems design is given, since a good understanding of the industrial context is considered necessary in order to avoid oversimplification of the problem, neglecting certain important factors and being unaware of important influences and relationships. In addition, a general introduction to complex thinking is given, since designing complex adaptive systems requires a non-classical thought, while practical notions of complexity theory and design are put forward. Building on these, the research proposes a Complex Systems Life-Cycle Understanding and Design (CXLUD) methodology to aid system architects and engineers in the design and control of complex adaptive systems. Starting from a creative anticipation construct - a loosening mechanism to allow for more options to be considered, the methodology proposes a conceptual framework and a series of stages to follow to find proper mechanisms that will promote elements to desired solutions by actively interacting among themselves. To illustrate the methodology, a financial systemic risks infrastructure systems architecture development case study is presented. The final part of this thesis develops a conceptual model to analyse managerial complexity competency model from a qualitative phenomenological study perspective. The model developed in this research is called Understanding-Perception-Action (UPA) managerial complexity competency model. The results of this competency model can be used to help ease project manager’s transition into complex adaptive projects, as well as serve as a foundation to launch qualitative and quantitative research into this area of project complexity management

3D-in-2D Displays for ATC.

This paper reports on the efforts and accomplishments of the 3D-in-2D Displays for ATC project at the end of Year 1. We describe the invention of 10 novel 3D/2D visualisations that were mostly implemented in the Augmented Reality ARToolkit. These prototype implementations of visualisation and interaction elements can be viewed on the accompanying video. We have identified six candidate design concepts which we will further research and develop. These designs correspond with the early feasibility studies stage of maturity as defined by the NASA Technology Readiness Level framework. We developed the Combination Display Framework from a review of the literature, and used it for analysing display designs in terms of display technique used and how they are combined. The insights we gained from this framework then guided our inventions and the human-centered innovation process we use to iteratively invent. Our designs are based on an understanding of user work practices. We also developed a simple ATC simulator that we used for rapid experimentation and evaluation of design ideas. We expect that if this project continues, the effort in Year 2 and 3 will be focus on maturing the concepts and employment in a operational laboratory settings

Responsible Governance of Artificial Intelligence: An Assessment, Theoretical Framework, and Exploration

abstract: While artificial intelligence (AI) has seen enormous technical progress in recent years, less progress has occurred in understanding the governance issues raised by AI. In this dissertation, I make four contributions to the study and practice of AI governance. First, I connect AI to the literature and practices of responsible research and innovation (RRI) and explore their applicability to AI governance. I focus in particular on AI’s status as a general purpose technology (GPT), and suggest some of the distinctive challenges for RRI in this context such as the critical importance of publication norms in AI and the need for coordination. Second, I provide an assessment of existing AI governance efforts from an RRI perspective, synthesizing for the first time a wide range of literatures on AI governance and highlighting several limitations of extant efforts. This assessment helps identify areas for methodological exploration. Third, I explore, through several short case studies, the value of three different RRI-inspired methods for making AI governance more anticipatory and reflexive: expert elicitation, scenario planning, and formal modeling. In each case, I explain why these particular methods were deployed, what they produced, and what lessons can be learned for improving the governance of AI in the future. I find that RRI-inspired methods have substantial potential in the context of AI, and early utility to the GPT-oriented perspective on what RRI in AI entails. Finally, I describe several areas for future work that would put RRI in AI on a sounder footing.Dissertation/ThesisDoctoral Dissertation Human and Social Dimensions of Science and Technology 201

System Dynamics in Transition Management : Participative modeling for transitioning towards a circular construction material industry

Climate change and biodiversity degradation are only two of humanity's major social and environmental issues. Scientists, global policy experts, and the general public are increasingly concluding that traditional interventions to reduce un-sustainability are inadequate and that change in all sectors of society is needed. Change processes of societal innovations are complex, non-linear, and dynamic transitions, for which scientific research increased in recent years. However, the concept of transitions and the proper role of science in promoting change is still debated. In this dissertation, I am especially interested in using scientific methods to understand drivers and barriers of societal innovation, engaging with societal actors, and increasing the effectiveness of interventions. To test the adequacy of System Dynamics modeling as a tool to support transition management, I conduct a case study in the construction material industry in Switzerland. The construction material industry is a traditional industry sector that faces public pressure to change dominant practices towards more sustainability. Yet recycling activities stagnate, and the potential of secondary resources is not utilized. I use six participative modeling workshops with public policy experts and seven interviews with extraction, disposal, recycling companies to develop a quantitative simulation model. This simulation model allows for virtual experiments to accelerate the transition of Switzerland's mineral construction material industry towards a circular economy. In this simulation model, I explain how the dynamic interaction between public policy and industry actors complicates the management of natural resource stocks. The co-production of extraction and disposal policies emerges as the central structure that forms a barrier to a circular economy. These spatial planning policies increase the incentive for companies to extract resources to generate volume for waste disposal. The resulting oversupply of primary resources locks out the use of secondary resources. I suggest experimenting with cooperative spatial planning between urban resource consumers and the hinterlands as a resource supplier to overcome this barrier. This cooperative spatial planning format is a leverage point for the local utilization of secondary resources without increasing material transports between regions. Based on this case study, I discuss integrating system dynamics in applied research for sustainability transitions, providing an empirical perspective on the intersection of System Dynamics (SD) and Transition Management (TM). Beyond the empirical findings for the governance of the transition of the industry sector in the case study, I focus on the methodological contribution of SD for TM. The findings are twofold. Firstly, by documenting participants' mental models during the participative modeling workshops, I gain insights into their learning process. These insights are essential to understand common misperceptions about the governance of the industry sector. For example, identifying the informal policy of extending gravel licenses rather than foreclosing after the expiration of the licensed duration was a critical insight. Furthermore, the discussion surrounding this policy clarified the role of adaptive expectations for the uptake of secondary resources. If new licensing processes do not consider the potential of secondary resources, a structural oversupply of primary resources results. Secondly, SD modeling adds operational guidance to the identification of fields for governance experimentation. These fields for governance experimentation are presented as more than just policy recommendations. They intend to induce more systemic changes, e.g., move from local spatial planning towards interregional spatial planning concepts. The insight that such systemic changes are necessary results from a formal model that clarified the scale of the problem (e.g., interregional arbitrage inhibits local recycling initiatives) and scope for required solutions (interregional spatial planning instead of local policy adjustments). I conclude that SD adds to the orientation phase of TM processes by providing an operational toolbox to engage with policy-relevant actors in a learning process and point at fields for experimentation. However, I also identify that the formal SD perspective in parts inhibited more daring and radical propositions for experimentation. While some might argue this is a weakness, I respond that SD modeling provides feasible recommendations based on identifying leverage points for long-term change.Doktorgradsavhandlin

Human Machine Interaction

In this book, the reader will find a set of papers divided into two sections. The first section presents different proposals focused on the human-machine interaction development process. The second section is devoted to different aspects of interaction, with a special emphasis on the physical interaction

Transboundary supply chain risk management: A consolidation of transboundary and supply chain risk management

Supply chains are becoming increasingly vulnerable to disruptions and face greater exposure from the dynamics of global interconnectivity. The growing complexities of modern societies have prompted renewed focus towards supply chain risk management (SCRM) research over the last decade. However, research related to transboundary risk issues has yet to be given substantial attention in recent years. Contributing to the developments in the field of SCRM, this thesis proposes an approach for managing global supply chain risk which modifies current SCRM processes to account for the dynamic nature of transboundary risks. This work extends current literary contributions and aims to compensate for the lack of transboundary risk focus in SCRM. Introducing the taxonomy of transboundary supply chain risk management (TSCRM), the present paper conceptualises a holistic integrative framework that incorporates resilience principles to adaptively manage the transboundary risk environment of global supply chains. In line with this framework, additional templates, tables, and a TSCRM planning process are proposed to facilitate the navigation through the TSCRM process, in particular the risk identification, and risk response selection and implementation phases