Complex interventions or complex systems? Implications for health economic evaluation

Health researchers commonly use the notion of complexity to indicate the problems faced in evaluating the effectiveness of many non-drug interventions.^1-3 However, although it is rarely delineated, complexity has two meanings. In the first it is a property of the intervention, and in the second it is a property of the system in which the intervention is implemented. We examine the implications of these two views for economic evaluation.

Co-evolution path model : how enterprises as complex systems survive on the edge of chaos

In this theoretical paper, we introduce and describe a model, and demonstrate its origins from the disciplines of Enterprise Architecture, cybernetics and systems theory. We use cybernetic thinking to develop a ‘Co-evolution Path Model’ that describes how enterprises as complex systems co-evolve with their complex environments. The model re-interprets Stafford Beer’s Viable System Model, and also uses the theorem of the ‘good regulator’ of Conant and Ashby, exemplifying how various complexity management theories could be synthesised into a cybernetic theory of Enterprise Architecture, using concepts from the generalisation of EA frameworks.

Sliding mode-like learning control for SISO complex systems with T-S fuzzy models

In this paper, a sliding mode-like learning control scheme is developed for a class of single input single output (SISO) complex systems. First, the Takagi-Sugeno (T-S) fuzzy modelling technique is employed to model the uncertain complex dynamical systems. Second, a sliding mode-like learning control is designed to drive the sliding variable to converge to the sliding surface, and the system states can then asymptotically converge to zero on the sliding surface. The advantages of this scheme are that: 1) the information about the uncertain system dynamics and the system model structure is not required for the design of the learning controller; 2) the closed-loop system behaves with a strong robustness with respect to uncertainties; 3) the control input is chattering-free. The sufficient conditions for the sliding mode-like learning control to stabilise the global fuzzy model are discussed in detail. A simulation example for the control of an inverted pendulum cart is presented to demonstrate the effectiveness of the proposed control scheme.

A community-based complex systems approach to high school completion

High school completion, like many educational phenomena, is the result of processes that, when taken together, constitute a complex system. In this paper, we describe the innovative use of group model building (GMB) as an entry point for complex systems analysis of educational processes that collectively determine high school completion. GMB exercises were conducted in a community in the state of Victoria in Australia. GMB brought together stakeholders from around the community and encouraged them to view high school completion from a complex systems perspective. Not only were participants able to use their experience to create an action plan to increase high school graduation rates but they also provided researchers with valuable information that can be used as input into rigorous, quantitative models of high school completion.

Building agent-based hybrid intelligent systems

Many complex problems including financial investment planning, foreign exchange trading, knowledge discovery from large/multiple databases require hybrid intelligent systems that integrate many intelligent techniques including expert systems, fuzzy logic, neural networks, and genetic algorithms. However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. In this paper, it is argued that agent technology is well snited for constructing hybrid intelligent systems (especially loosely coupled hybrid intelligent systems) through a successful case study. A great number of heterogeneous computing techniques/packages are easily integlated into the experimental system under a unifying agent framework, which implies that agent technology can greatly facilitate the construction of hybrid intelligent systems.

Building agent-based hybrid intelligent systems : a case study

Many complex problems (e.g., financial investment planning, foreign exchange trading, data mining from large/multiple databases) require hybrid intelligent systems that integrate many intelligent techniques (e.g., fuzzy logic, neural networks, and genetic algorithms). However, hybrid intelligent systems are difficult to develop because they have a large number of parts or components that have many interactions. On the other hand, agents offer a new and often more appropriate route to the development of complex systems, especially in open and dynamic environments. Thus, this paper discusses the development of an agent-based hybrid intelligent system for financial investment planning, in which a great number of heterogeneous computing techniques/packages are easily integrated into a unifying agent framework. This shows that agent technology can indeed facilitate the development of hybrid intelligent systems.

Adaptive intelligent systems for energy management of vehicles

The thesis demonstrated the architecture of adaptive intelligent systems for energy management that is capable of interacting with complex systems including the vehicle, environment, and driver components, as well as the interrelationships between these variables, to deliver fuel consumption improvements.

A fault detection scheme for time-delay systems using minimum-order functional observers

This paper presents a method for designing residual generators using minimum-order functional observers to detect actuator and component faults in time-delay systems. Existence conditions of the residual generators and functional observers are first derived, and then based on a parametric approach to the solution of a generalized Sylvester matrix equation, we develop systematic procedures for designing minimum-order functional observers to detect faults in the system. The advantages of having minimum-order observers are obvious from the economical and practical points of view as cost saving and simplicity can be achieved, particularly when dealing with high-order complex systems. Extensive numerical examples are given to illustrate the proposed fault detection scheme. In all the numerical examples, we design minimum-order residual generators and functional observers to detect faults in the system.

Prediction interval-based control of nonlinear systems using neural networks

Prediction interval (PI) is a promising tool for quantifying uncertainties associated with point predictions. Despite its informativeness, the design and deployment of PI-based controller for complex systems is very rare. As a pioneering work, this paper proposes a framework for design and implementation of PI-based controller (PIC) for nonlinear systems. Neural network (NN)-based inverse model within internal model control structure is used to develop the PIC. Firstly, a PI-based model is developed to construct PIs for the system output. This model is then used as an online estimator for PIs. The PIs from this model are fed to the NN inverse model along with other traditional inputs to generate the control signal. The performance of the proposed PIC is examined for two case studies. This includes a nonlinear batch polymerization reactor and a numerical nonlinear plant. Simulation results demonstrated that the proposed PIC tracking performance is better than the traditional NN-based controller.

Minimal-order functional observer-based residual generators for fault detection and isolation of dynamical systems

This paper examines the design of minimal-order residual generators for the purpose of detecting and isolating actuator and/or component faults in dynamical systems. We first derive existence conditions and design residual generators using only first-order observers to detect and identify the faults. When the first-order functional observers do not exist, then based on a parametric approach to the solution of a generalized Sylvester matrix equation, we develop systematic procedures for designing residual generators utilizing minimal-order functional observers. Our design approach gives lower-order residual generators than existing results in the literature. The advantages for having such lower-order residual generators are obvious from the economical and practical points of view as cost saving and simplicity in implementation can be achieved, particularly when dealing with high-order complex systems. Numerical examples are given to illustrate the proposed fault detection and isolation schemes. In all of the numerical examples, we design minimum-order residual generators to effectively detect and isolate actuator and/or component faults in the system.

Investigating combination HIV prevention: isolated interventions or complex system

Treatment as prevention has mobilized new opportunities in preventing HIV transmission and has led to bold new UNAIDS targets in testing, treatment coverage and transmission reduction. These will require not only an increase in investment but also a deeper understanding of the dynamics of combining behavioural, biomedical and structural HIV prevention interventions. High-income countries are making substantial investments in combination HIV prevention, but is this investment leading to a deeper understanding of how to combine interventions? The combining of interventions involves complexity, with many strategies interacting with non-linear and multiplying rather than additive effects. Discussion: Drawing on a recent scoping study of the published research evidence in HIV prevention in high-income countries, this paper argues that there is a gap between the evidence currently available and the evidence needed to guide the achieving of these bold targets. The emphasis of HIV prevention intervention research continues to look at one intervention at a time in isolation from its interactions with other interventions, the community and the socio-political context of their implementation. To understand and evaluate the role of a combination of interventions, we need to understand not only what works, but in what circumstances, what role the parts need to play in their relationship with each other, when the combination needs to adapt and identify emergent effects of any resulting synergies. There is little development of evidence-based indicators on how interventions in combination should achieve that strategic advantage and synergy. This commentary discusses the implications of this ongoing situation for future research and the required investment in partnership. We suggest that systems science approaches, which are being increasingly applied in other areas of public health, could provide an expanded vocabulary and analytic tools for understanding these complex interactions, relationships and emergent effects. Conclusions: Relying on the current linear but disconnected approaches to intervention research and evidence we will miss the potential to achieve and understand system-level synergies. Given the challenges in sustaining public health and HIV prevention investment, meeting the bold UNAIDS targets that have been set is likely to be dependent on achieving systems level synergies.

Complexity theory and organizing form dualities

Purpose – The purpose of this paper is to describe how order-generated rules applied to organizing form dualities can assist in creating the conditions for emergent, self-organized behavior in organizations, thereby offering an operational deployment of complexity theory.

Design/methodology/approach – The paper begins by showing that the concept of dualities is consistent with complexity-thinking. In addition, when applied to organizing forms, dualities represent a practical way of affecting an organization's balance between chaos and order. Thus, when augmented with order-generating rules, organizing form dualities provide an access point for the practical instigation of edge of chaos conditions and the potential for emergence.

Findings – The paper maintains that many attempts to “manage” complexity have been associated with changes to organizing forms, specifically toward new forms of organizing. It is suggested that organizing form dualities provide some management guidance for encouraging the “edge of chaos” conditions advocated in complexity theory, although the details of self-organization cannot be prescribed given the assumptions of non-linearity associated with complexity theory perspectives. Finally, it is proposed that organizing dualities can elucidate the nature and application of order-generating rules in non-linear complex systems.

Practical implications – Dualities offer some guidance toward the practical implementation of complexity theory as they represent an accessible sub-system where the forces for order and chaos – traditional and new forms of organizing respectively – are accessible and subject to manipulation.

Originality/value – The commonalities between dualities and complexity theory are intuitive, but little conceptual work has shown how the former can be employed as a guide to managing organizing forms. Moreover, this approach demonstrates that managers may be able to stimulate “edge of chaos” conditions in a practical way, without making positivistic assumptions about the causality associated with their efforts.