Information systems development projects as complex adaptive systems

This research considers information systems development (ISD) projects as complex adaptive systems. We investigate the question whether complex adaptive systems (CAS) theory is relevant as a theoretical foundation for understanding ISD, and if so, which kind of understanding can be achieved by utilizing the theory? We introduce key concepts of CAS theory such as interaction, emergence, interconnected autonomous agents, selforganization, co-evolution, poise at the edge of chaos, time pacing, and poise at the edge of time to analyse and understand ISD in practice. We demonstrate the strength of such a CAS approach through an empirical case study presentation and analysis. While our work contributes to a complexity theory of ISD, the case examination also provides practical advice derived from this perspective to successfully cope with complexity in ISD in an adaptive manner.<br /

Nonlinear time-series analysis revisited

In 1980 and 1981, two pioneering papers laid the foundation for what became known as nonlinear time-series analysis: the analysis of observed data---typically univariate---via dynamical systems theory. Based on the concept of state-space reconstruction, this set of methods allows us to compute characteristic quantities such as Lyapunov exponents and fractal dimensions, to predict the future course of the time series, and even to reconstruct the equations of motion in some cases. In practice, however, there are a number of issues that restrict the power of this approach: whether the signal accurately and thoroughly samples the dynamics, for instance, and whether it contains noise. Moreover, the numerical algorithms that we use to instantiate these ideas are not perfect; they involve approximations, scale parameters, and finite-precision arithmetic, among other things. Even so, nonlinear time-series analysis has been used to great advantage on thousands of real and synthetic data sets from a wide variety of systems ranging from roulette wheels to lasers to the human heart. Even in cases where the data do not meet the mathematical or algorithmic requirements to assure full topological conjugacy, the results of nonlinear time-series analysis can be helpful in understanding, characterizing, and predicting dynamical systems

Systems Theory-Based Construct for Identifying Metasystem Pathologies for Complex System Governance

The purpose of this research was to develop a systems theory-based construct for metasystem pathologies identification in support of the problem formulation phase of systems-based methodologies using an inductive research design. Problem formulation has been identified as one of the most critical stages in complex system development since it influences later stages in complex system understanding. In modern society where the operating landscape is characteristically ambiguous, mired by complexity, emergence. interdependence, and uncertainty, the concept of problem formulation is used to ensure right issues affecting complex systems surface and addressed to meet expected system performance and viability. In this research, this role of problem formulation is examined in systems-based methodologies in connection with systems theory. While the literature indicates the importance of problem formulation phase in systems-based methodologies. the conceptual foundations of systems theory that form the basis for \u27systemic\u27 thinking in these methodologies is not clearly inculcated into the problem formulation phase. This research addresses this gap by providing the necessary detailed discussion linking systems theory to problem formulation. The research focused on the lack of explicit use of systems theory in problem formulation and metasystemic issues of a higher logical order beyond single system of interest. A rigorous approach employing grounded theory method was used to analyze systems theory (laws, principles, and theorems) in terms of problem formulation to develop a construct – Metasystem Pathologies Identification and derived systems theory-based pathologies (circumstances. conditions, factors, or patterns) that act to limit system performance. A case study was then undertaken to face validate the applicability of emerging systems-theory pathologies in an operational setting were possible utility were developed. Fundamentally, this research presents a new approach to problem formulation where systemic thinking is at the foundation of identifying systemic issues affecting system performance. A significant promise for those interested in problem formulation is the inclusion of systems theory-based pathologies during problem formulation phase of systems-based approaches

Planning For Chaos: Cluster Strategies Of Economic Development

With this thesis I will attempt to demonstrate the value of using chaos theory as a framework for understanding the emergence and development of clusters as a strategy for economic development. The application of the principles of chaos theory will be used to judge the relative success of specific clusters through a case study approach and historical analysis to determine the change agents and other significant factors influential to the growth of economic clusters. Because clusters are turbulent, non-linear systems that are sensitive to endogenous and exogenous triggers, chaos theory may provide the conceptual foundation appropriate to the study of economic clusters. A more thorough understanding of the emergence and development of economic clusters may illuminate policies and practices for regional planners, economic development professionals and policy makers

Quantitative Characterization of Complex Systems—An Information Theoretic Approach

A significant increase in System-of-Systems (SoS) is currently observed in the social and technical domains. As a result of the increasing number of constituent system components, Systems of Systems are becoming larger and more complex. Recent research efforts have highlighted the importance of identifying innovative statistical and theoretical approaches for analyzing complex systems to better understand how they work. This paper portrays the use of an agnostic twostage examination structure for complex systems aimed towards developing an information theorybased approach to analyze complex technical and socio-technical systems. Towards the goal of characterizing system complexity with information entropy, work was carried out in exploring the potential application of entropy to a simulated case study to illustrate its applicability and to establish the use of information theory within the broad horizon of complex systems. Although previous efforts have been made to use entropy for understanding complexity, this paper provides a basic foundation for identifying a framework to characterize complexity, in order to analyze and assess complex systems in different operational domains