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

Reliability analysis and micromechanics: A coupled approach for composite failure prediction

This work aims at associating two classical approaches for the design of composite materials: first, reliability methods that allow to account for the various uncertainties involved in the composite materials behaviour and lead to a rational estimation of their reliability level; on the other hand, micromechanics that derive macroscopic constitutive laws from micromechanical features. Such approach relies on the introduction of variabilities defined at the microscale and on the investigation of their consequences on the material macroscopic response through an homogenization scheme. Precisely, we propose here a systematic treatment of variability which involves a strong link between micro- and macroscales and provides a more exhaustive analysis of the influence of uncertainties. The paper intends to explain the main steps of such coupling and demonstrate its interests for material engineering, especially for constitutive modelling and composite materials optimization. An application case is developed throughout on the failure of unidirectional carbon fibre-reinforced composites with a comparative analysis between experimental data and simulation results

A Map of the Inorganic Ternary Metal Nitrides

Exploratory synthesis in novel chemical spaces is the essence of solid-state chemistry. However, uncharted chemical spaces can be difficult to navigate, especially when materials synthesis is challenging. Nitrides represent one such space, where stringent synthesis constraints have limited the exploration of this important class of functional materials. Here, we employ a suite of computational materials discovery and informatics tools to construct a large stability map of the inorganic ternary metal nitrides. Our map clusters the ternary nitrides into chemical families with distinct stability and metastability, and highlights hundreds of promising new ternary nitride spaces for experimental investigation--from which we experimentally realized 7 new Zn- and Mg-based ternary nitrides. By extracting the mixed metallicity, ionicity, and covalency of solid-state bonding from the DFT-computed electron density, we reveal the complex interplay between chemistry, composition, and electronic structure in governing large-scale stability trends in ternary nitride materials

Symbol Emergence in Robotics: A Survey

Humans can learn the use of language through physical interaction with their environment and semiotic communication with other people. It is very important to obtain a computational understanding of how humans can form a symbol system and obtain semiotic skills through their autonomous mental development. Recently, many studies have been conducted on the construction of robotic systems and machine-learning methods that can learn the use of language through embodied multimodal interaction with their environment and other systems. Understanding human social interactions and developing a robot that can smoothly communicate with human users in the long term, requires an understanding of the dynamics of symbol systems and is crucially important. The embodied cognition and social interaction of participants gradually change a symbol system in a constructive manner. In this paper, we introduce a field of research called symbol emergence in robotics (SER). SER is a constructive approach towards an emergent symbol system. The emergent symbol system is socially self-organized through both semiotic communications and physical interactions with autonomous cognitive developmental agents, i.e., humans and developmental robots. Specifically, we describe some state-of-art research topics concerning SER, e.g., multimodal categorization, word discovery, and a double articulation analysis, that enable a robot to obtain words and their embodied meanings from raw sensory--motor information, including visual information, haptic information, auditory information, and acoustic speech signals, in a totally unsupervised manner. Finally, we suggest future directions of research in SER.Comment: submitted to Advanced Robotic

Symmetry in Critical Random Boolean Network Dynamics

Using Boolean networks as prototypical examples, the role of symmetry in the dynamics of heterogeneous complex systems is explored. We show that symmetry of the dynamics, especially in critical states, is a controlling feature that can be used both to greatly simplify analysis and to characterize different types of dynamics. Symmetry in Boolean networks is found by determining the frequency at which the various Boolean output functions occur. There are classes of functions that consist of Boolean functions that behave similarly. These classes are orbits of the controlling symmetry group. We find that the symmetry that controls the critical random Boolean networks is expressed through the frequency by which output functions are utilized by nodes that remain active on dynamical attractors. This symmetry preserves canalization, a form of network robustness. We compare it to a different symmetry known to control the dynamics of an evolutionary process that allows Boolean networks to organize into a critical state. Our results demonstrate the usefulness and power of using the symmetry of the behavior of the nodes to characterize complex network dynamics, and introduce a novel approach to the analysis of heterogeneous complex systems