How does complex adaptive system theory inform innovation in complex project based organisations?

Innovation enables organisations to endure by responding to emergence and to improve efficiency. Innovation in a complex organisation can be difficult due to complexities contributing to slow decision-making. Complex projects fail due to an inability to respond to emergence which consumes finances and impacts on resources and organisational success. Therefore, for complex organisations to improve on performance and resilience, it would be advantageous to understand how to improve the management of innovation and thus, the ability to respond to emergence. The benefits to managers are an increase in the number of successful projects and improved productivity. This study will explore innovation management in a complex project based organisation. The contribution to the academic literature will be an in-depth, qualitative exploration of innovation in a complex project based organisation using a comparative case study approach.

Adaptive cache aware multiprocessor scheduling framework

Computer resource allocation represents a significant challenge particularly for multiprocessor systems, which consist of shared computing resources to be allocated among co-runner processes and threads. While an efficient resource allocation would result in a highly efficient and stable overall multiprocessor system and individual thread performance, ineffective poor resource allocation causes significant performance bottlenecks even for the system with high computing resources. This thesis proposes a cache aware adaptive closed loop scheduling framework as an efficient resource allocation strategy for the highly dynamic resource management problem, which requires instant estimation of highly uncertain and unpredictable resource patterns. Many different approaches to this highly dynamic resource allocation problem have been developed but neither the dynamic nature nor the time-varying and uncertain characteristics of the resource allocation problem is well considered. These approaches facilitate either static and dynamic optimization methods or advanced scheduling algorithms such as the Proportional Fair (PFair) scheduling algorithm. Some of these approaches, which consider the dynamic nature of multiprocessor systems, apply only a basic closed loop system; hence, they fail to take the time-varying and uncertainty of the system into account. Therefore, further research into the multiprocessor resource allocation is required. Our closed loop cache aware adaptive scheduling framework takes the resource availability and the resource usage patterns into account by measuring time-varying factors such as cache miss counts, stalls and instruction counts. More specifically, the cache usage pattern of the thread is identified using QR recursive least square algorithm (RLS) and cache miss count time series statistics. For the identified cache resource dynamics, our closed loop cache aware adaptive scheduling framework enforces instruction fairness for the threads. Fairness in the context of our research project is defined as a resource allocation equity, which reduces corunner thread dependence in a shared resource environment. In this way, instruction count degradation due to shared cache resource conflicts is overcome. In this respect, our closed loop cache aware adaptive scheduling framework contributes to the research field in two major and three minor aspects. The two major contributions lead to the cache aware scheduling system. The first major contribution is the development of the execution fairness algorithm, which degrades the co-runner cache impact on the thread performance. The second contribution is the development of relevant mathematical models, such as thread execution pattern and cache access pattern models, which in fact formulate the execution fairness algorithm in terms of mathematical quantities. Following the development of the cache aware scheduling system, our adaptive self-tuning control framework is constructed to add an adaptive closed loop aspect to the cache aware scheduling system. This control framework in fact consists of two main components: the parameter estimator, and the controller design module. The first minor contribution is the development of the parameter estimators; the QR Recursive Least Square(RLS) algorithm is applied into our closed loop cache aware adaptive scheduling framework to estimate highly uncertain and time-varying cache resource patterns of threads. The second minor contribution is the designing of a controller design module; the algebraic controller design algorithm, Pole Placement, is utilized to design the relevant controller, which is able to provide desired timevarying control action. The adaptive self-tuning control framework and cache aware scheduling system in fact constitute our final framework, closed loop cache aware adaptive scheduling framework. The third minor contribution is to validate this cache aware adaptive closed loop scheduling framework efficiency in overwhelming the co-runner cache dependency. The timeseries statistical counters are developed for M-Sim Multi-Core Simulator; and the theoretical findings and mathematical formulations are applied as MATLAB m-file software codes. In this way, the overall framework is tested and experiment outcomes are analyzed. According to our experiment outcomes, it is concluded that our closed loop cache aware adaptive scheduling framework successfully drives co-runner cache dependent thread instruction count to co-runner independent instruction count with an error margin up to 25% in case cache is highly utilized. In addition, thread cache access pattern is also estimated with 75% accuracy.

Infrastructure transitions toward sustainability : a complex adaptive systems perspective

To ensure infrastructure assets are procured and maintained by government on behalf of citizens, appropriate policy and institutional architecture is needed, particularly if a fundamental shift to more sustainable infrastructure is the goal. The shift in recent years from competitive and resource-intensive procurement to more collaborative and sustainable approaches to infrastructure governance is considered a major transition in infrastructure procurement systems. In order to better understand this transition in infrastructure procurement arrangements, the concept of emergence from Complex Adaptive Systems (CAS) theory is offered as a key construct. Emergence holds that micro interactions can result in emergent macro order. Applying the concept of emergence to infrastructure procurement, this research examines how interaction of agents in individual projects can result in different industry structural characteristics. The paper concludes that CAS theory, and particularly the concept of ‘emergence’, provides a useful construct to understand infrastructure procurement dynamics and progress towards sustainability.

Understanding IT value : a multilevel, complex, and adaptive system perspective

One remaining difficulty in the Information Technology (IT) business value evaluation domain is the direct linkage between IT value and the underlying determinants of IT value or surrogates of IT value. This paper proposes a research that examines the interacting effects of the determinants of IT value, and their influences on IT value. The overarching research question is how those determinants interact with each other and affect the IT value at organizational value. To achieve this, this research embraces a multilevel, complex, and adaptive system view, where the IT value emerges from the interacting of underlying determinants. This research is theoretically grounded on three organizational theories – multilevel theory, complex adaptive systems theory, and adaptive structuration theory. By integrating those theoretical paradigms, this research proposes a conceptual model that focuses on the process where IT value is created from interactions of those determinants. To answer the research question, agent-based modeling technique is used in this research to build a computational representation based on the conceptual model. Computational experimentation will be conducted based on the computational representation. Validation procedures will be applied to consolidate the validity of this model. In the end, hypotheses will be tested using computational experimentation data.

Adaptive compliance control of a multi‐legged stair‐climbing robot based on proprioceptive data

Purpose – The purpose of this paper is to describe an innovative compliance control architecture for hybrid multi‐legged robots. The approach was verified on the hybrid legged‐wheeled robot ASGUARD, which was inspired by quadruped animals. The adaptive compliance controller allows the system to cope with a variety of stairs, very rough terrain, and is also able to move with high velocity on flat ground without changing the control parameters. Design/methodology/approach – The paper shows how this adaptivity results in a versatile controller for hybrid legged‐wheeled robots. For the locomotion control we use an adaptive model of motion pattern generators. The control approach takes into account the proprioceptive information of the torques, which are applied on the legs. The controller itself is embedded on a FPGA‐based, custom designed motor control board. An additional proprioceptive inclination feedback is used to make the same controller more robust in terms of stair‐climbing capabilities. Findings – The robot is well suited for disaster mitigation as well as for urban search and rescue missions, where it is often necessary to place sensors or cameras into dangerous or inaccessible areas to get a better situation awareness for the rescue personnel, before they enter a possibly dangerous area. A rugged, waterproof and dust‐proof corpus and the ability to swim are additional features of the robot. Originality/value – Contrary to existing approaches, a pre‐defined walking pattern for stair‐climbing was not used, but an adaptive approach based only on internal sensor information. In contrast to many other walking pattern based robots, the direct proprioceptive feedback was used in order to modify the internal control loop, thus adapting the compliance of each leg on‐line.

Identifying the knowledge management structures in enterprise systems projects

Enterprise System (ES) implementation and management are knowledge intensive tasks that inevitably draw upon the experience of a wide range of people with diverse knowledge capabilities. Knowledge Management (KM) has been identified as a critical success factor in ES projects. Despite the recognized importance of managing knowledge for ES benefits realization, systematic attempts to conceptualize KM-structures have been few. Where the adequacy of KM-structures is assessed, the process and measures are typically idiosyncratic and lack credibility. Using the ‘KM-process’, itself based in sociology of knowledge, this paper conceptualizes four main constructs to measure the adequacy of KM-structures. The SEM model is tested using 310 responses gathered from 27 ES installations that had implemented SAP R/3. The findings reveal six constructs for KM-structure. Furthermore, the paper demonstrates the application of KM-structures in the context of ES using the Adaptive Structuration Theory. The results demonstrate that having adequate KM-structures in place, while necessary, is not sufficient. These rules and resources must be appropriated to have greater positive influence on the Enterprise System. Furthermore, the study provides empirical support for knowledge-based theory by illustrating the importance of knowledge use/re-use (vs. knowledge creation) as the most important driver in the process of KM.

A Marxian model of technology appropriation

This paper explores the philosophical roots of appropriation within Marx's theories and socio-cultural studies in an attempt to seek common ground among existing theories of technology appropriation in IS research. Drawing on appropriation perspectives from Adaptive Structuration Theory, the Model of Technology Appropriation and the Structurational Model of Technology for comparison, we aim to generate a Marxian model that provides a starting point toward a general causal model of technology appropriation. This paper opens a philosophical discussion on the phenomenon of appropriation in the IS community, directing attention to foundational concepts in the human-technology nexus using ideas conceived by Marx.

Integrating organisational design with IT design : the Queensland Health payroll case

Most existing requirements engineering approaches focus on the modelling and specification of the IT artefacts ignoring the environment where the application is deployed. Although some requirements engineering approaches consider the stakeholder’s goals, they still focus on the IT artefacts’ specification. However, IT artefacts are embedded in a dynamic organisational environment and their design and specification cannot be separated from the environment’s constant evolution. Therefore, during the initial stages of a requirements engineering process it is advantageous to consider the integration of IT design with organisational design. We proposed the ADMITO (Analysis, Design and Management of IT and Organisations) approach to represent the dynamic relations between social and material entities, where the latter are divided into technological and organisational entities. In this paper we show how by using ADMITO in a concrete case, the Queensland Health Payroll (QHP) case, it is possible to have an integrated representation of IT and organisational design supporting organisational change and IT requirements specification.

Action regulation across the adult lifespan (ARAL): A metatheory of work and aging

The goals of this article are to integrate action regulation theory (ART) with the lifespan developmental perspective and to outline tenets of a new metatheory of work and aging. The action regulation across the adult lifespan (ARAL) theory explains how workers influence, and are influenced by, their environment across different time spans. First, the basic concepts of ART are described, including the sequential and hierarchical structure of actions, complete tasks and actions, foci of action regulation, and the action-regulating mental model. Second, principles of the lifespan developmental perspective are delineated, including development as a lifelong and multidirectional process, the joint occurrence of gains and losses, intraindividual plasticity, historical embeddedness, and contextualism. Third, propositions of ARAL theory are derived by analyzing workers’ action regulation from a lifespan developmental perspective (i.e., effects of aging on action regulation), and by analyzing aging and development in the work context from an ART perspective (i.e., effects of action regulation on age-related changes in cognition and personality). Fourth, we develop further propositions to integrate ART with lifespan theories of motivation and socioemotional experience. Finally, we discuss implications for future research and practice based on ARAL theory.

Adaptive dual control of topic-based information retrieval

Information Retrieval is an important albeit imperfect component of information technologies. A problem of insufficient diversity of retrieved documents is one of the primary issues studied in this research. This study shows that this problem leads to a decrease of precision and recall, traditional measures of information retrieval effectiveness. This thesis presents an adaptive IR system based on the theory of adaptive dual control. The aim of the approach is the optimization of retrieval precision after all feedback has been issued. This is done by increasing the diversity of retrieved documents. This study shows that the value of recall reflects this diversity. The Probability Ranking Principle is viewed in the literature as the “bedrock” of current probabilistic Information Retrieval theory. Neither the proposed approach nor other methods of diversification of retrieved documents from the literature conform to this principle. This study shows by counterexample that the Probability Ranking Principle does not in general lead to optimal precision in a search session with feedback (for which it may not have been designed but is actively used). Retrieval precision of the search session should be optimized with a multistage stochastic programming model to accomplish the aim. However, such models are computationally intractable. Therefore, approximate linear multistage stochastic programming models are derived in this study, where the multistage improvement of the probability distribution is modelled using the proposed feedback correctness method. The proposed optimization models are based on several assumptions, starting with the assumption that Information Retrieval is conducted in units of topics. The use of clusters is the primary reasons why a new method of probability estimation is proposed. The adaptive dual control of topic-based IR system was evaluated in a series of experiments conducted on the Reuters, Wikipedia and TREC collections of documents. The Wikipedia experiment revealed that the dual control feedback mechanism improves precision and S-recall when all the underlying assumptions are satisfied. In the TREC experiment, this feedback mechanism was compared to a state-of-the-art adaptive IR system based on BM-25 term weighting and the Rocchio relevance feedback algorithm. The baseline system exhibited better effectiveness than the cluster-based optimization model of ADTIR. The main reason for this was insufficient quality of the generated clusters in the TREC collection that violated the underlying assumption.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Adaptive dynamic path re-planning RRT algorithms with game theory for UAVs

The main aim of this paper is to describe an adaptive re-planning algorithm based on a RRT and Game Theory to produce an efficient collision free obstacle adaptive Mission Path Planner for Search and Rescue (SAR) missions. This will provide UAV autopilots and flight computers with the capability to autonomously avoid static obstacles and No Fly Zones (NFZs) through dynamic adaptive path replanning. The methods and algorithms produce optimal collision free paths and can be integrated on a decision aid tool and UAV autopilots.