The limits of phenomenology: From behaviorism to drug testing and engineering design

It is widely believed that theory is useful in physics because it describes simple systems and that strictly empirical phenomenological approaches are necessary for complex biological and social systems. Here, we prove based on an analysis of the information that can be obtained from experimental observations that theory is even more essential in the understanding of complex systems. Implications of this proof revise the general understanding of how we can understand complex systems including the behaviorist approach to human behavior, problems with testing engineered systems, and medical experimentation for evaluating treatments and the Food and Drug Administration approval of medications. Each of these approaches are inherently limited in their ability to characterize real world systems due to the large number of conditions that can affect their behavior. Models are necessary as they can help to characterize behavior without requiring observations for all possible conditions. The testing of models by empirical observations enhances the utility of those observations. For systems for which adequate models have not been developed, or are not practical, the limitations of empirical testing lead to uncertainty in our knowledge and risks in individual, organizational, and social policy decisions. These risks should be recognized and inform our decisions. © 2015 Wiley Periodicals, Inc. Complexity 21: 181–189, 2016     

Modelling for the planning and management of bed capacities in hospitals

The internal dynamics of a hospital represent a complex non-linear structure. Planning and management of bed capacities must be evaluated within an environment of uncertainty, variability and limited resources. A common approach is to plan and manage capacities based on simple deterministic spreadsheet calculations. This paper demonstrates that these calculations typically do not provide the appropriate information and result in underestimating true bed requirements. More sophisticated, flexible and necessarily detailed capacity models are needed. The development and use of such a simulation model is presented in this paper. The modelling work, in conjunction with a major UK NHS Trust, considers various types of patient flows, at the individual patient level, and resulting bed needs over time. The consequence of changes in capacity planning policies and management of existing capacities can be readily examined. The work has highlighted the need for evaluating hospital bed capacities in light of both bed occupancies and refused admission rates. The relationship between occupancy and refusals is complex and often overlooked by hospital managers.     

Waiting list behaviour and the consequences for NHS targets

The United Kingdom's National Health Service (NHS) is investing considerable resources in reducing patient waiting times for elective treatment. This paper describes the development of a waiting list model and its use in a simulation to assess management options. Simulation usually assumes that waiting is adequately described by simple queuing disciplines, typically first-in-first-out. However, waiting in the United Kingdom's NHS is a more complex phenomenon. The waiting list behaviour is explored through an analysis of the changes in waiting time distributions for elective orthopaedics in one Scottish Health Board, NHS Fife. The evolving distributions suggest that there have been substantial changes in priorities in response to the various NHS targets. However, in the short or medium term, the form of the distribution appears reasonably stable, providing a basis for estimating future waiting times in different scenarios. A model of the waiting behaviour and prioritization in the appointment allocations was embedded in a simulation of the complete elective orthopaedic patient journey from referral, through outpatients and diagnostics to surgery. The model has been used to explore the consequences of various management options in the context of the NHS target that no patient should wait more than 18 weeks between referral and treatment.     

A clone-based graphical modeler and mathematical model generator for optimal production planning in process industries

This paper outlines a visually interactive graphical modeling approach for process type production systems, with hidden generation of complex optimization models for production planning. The proposed system lets the users build a graphical model of the production system with one-to-one clones of its production units through its interactive visual interface, accepts production-specific data for its components, and finally, internally generates and solves its mathematical programming model without any interaction from the user. This “clone-based” modeling approach allows the continued use of optimization models with minimal mathematical programming understanding, as generation of mathematical model by clones is hidden and automatic, therefore maintenance-free: Updating graphical production system models is enough for renewing internal optimization models. The concept is demonstrated in this paper with a linear programming prototype developed for a petroleum refinery.     

Linear homeomorphisms of non-classical Hilbert spaces

Let K be a complete infinite rank valued field. In [4] we studied Norm Hilbert Spaces (NHS) over K i.e. K-Banach spaces for which closed subspaces admit projections of norm ≤ 1. In this paper we prove the following striking properties of continuous linear operators on NHS. Surjective endomorphisms are bijective, no NHS is linearly homeomorphic to a proper subspace (Theorem 3.7), each operator can be approximated, uniformly on bounded sets, by finite rank operators (Theorem 3.8). These properties together — in real or complex theory shared only by finite-dimensional spaces — show that NHS are more ‘rigid’ than classical Hilbert spaces.     

Models and people: An alternative view of the emergent properties of computational models

Computer models can help humans gain insight into the functioning of complex systems. Used for training, they can also help gain insight into the cognitive processes humans use to understand these systems. By influencing humans understanding (and consequent actions) computer models can thus generate an impact on both these actors and the very systems they are designed to simulate. When these systems also include humans, a number of self‐referential relations thus emerge which can lead to very complex dynamics. This is particularly true when we explicitly acknowledge and model the existence of multiple conflicting representations of reality among different individuals. Given the increasing availability of computational devices, the use of computer models to support individual and shared decision making could potentially have implications far wider than the ones often discussed within the Information and Communication Technologies community in terms of computational power and network communication. We discuss some theoretical implications and describe some initial numerical simulations. © 2015 Wiley Periodicals, Inc. Complexity 21: 202–213, 2016     

Modeling the complexity of genetic networks: Understanding multigenic and pleiotropic regulation

Molecular genetics presents an increasingly complex picture of the genome and biological function. Evidence is mounting for distributed function, redundancy, and combinatorial coding in the regulation of genes. Satisfactory explanation will require the concept of a parallel processing signaling network. Here we provide an introduction to Boolean networks and their relevance to present-day experimental research. Boolean network models exhibit global complex behavior, self-organization, stability, redundancy and periodicity, properties that deeply characterize biological systems. While the life sciences must inevitably face the issue of complexity, we may well look to cybernetics for a modeling language such as Boolean networks which can manageably describe parallel processing biological systems and provide a framework for the growing accumulation of data. We finally discuss experimental strategies and database systems that will enable mapping of genetic networks. The synthesis of these approaches holds an immense potential for new discoveries on the intimate nature of genetic networks, bringing us closer to an understanding of complex molecular physiological processes like brain development, and intractable medical problems of immediate importance, such as neurodegenerative disorders, cancer, and a variety of genetic diseases.     

Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty

Complex real-world systems consist of collections of interacting processes/events. These processes change over time in response to both internal and external stimuli as well as to the passage of time itself. Many domains such as real-time systems diagnosis, story understanding, and financial forecasting require the capability to model complex systems under a unified framework to deal with both time and uncertainty. Current models for uncertainty and current models for time already provide rich languages to capture uncertainty and temporal information, respectively. Unfortunately, these semantics have made it extremely difficult to unify time and uncertainty in a way which cleanly and adequately models the problem domains at hand. Existing approaches suffer from significant trade offs between strong semantics for uncertainty and strong semantics for time. In this paper, we explore a new model, the Probabilistic Temporal Network (PTN), for representing temporal and atemporal information while fully embracing probabilistic semantics. The model allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes.     

Probabilistic verification of a biodiesel production system using statistical model checking

Biochemical system designers are increasingly using formal modelling, simulation, and verification methods to improve the understanding of complex systems. Probabilistic models can incorporate realistic stochastic dynamics, but creating and analysing probabilistic models in a formal way is challenging. In this work, we present a stochastic model of biodiesel production that incorporates an inexpensive test of fuel quality, and we validate the model using statistical model checking, which can be used to evaluate simple or complex temporal properties efficiently. We also describe probabilistic simulation and analysis techniques for stochastic hybrid system (SHS) models to demonstrate the properties of our model. We introduce a variety of properties for various configurations of the reactor as well as results of testing our model against the properties.     

Case-based modeling and the SACS Toolkit: a mathematical outline

Researchers in the social sciences currently employ a variety of mathematical/computational models for studying complex systems. Despite the diversity of these models, the majority can be grouped into one of three types: agent (rule-based) modeling, dynamical (equation-based) modeling and statistical (aggregate-based) modeling. The purpose of the current paper is to offer a fourth type: case-based modeling. To do so, we review the SACS Toolkit: a new method for quantitatively modeling complex social systems, based on a case-based, computational approach to data analysis. The SACS Toolkit is comprised of three main components: a theoretical blueprint of the major components of a complex system (social complexity theory); a set of case-based instructions for modeling complex systems from the ground up (assemblage); and a recommended list of case-friendly computational modeling techniques (case-based toolset). Developed as a variation on Byrne (in Sage Handbook of Case-Based Methods, pp.?260?C268, 2009), the SACS Toolkit models a complex system as a set of k-dimensional vectors (cases), which it compares and contrasts, and then condenses and clusters to create a low-dimensional model (map) of a complex system??s structure and dynamics over time/space. The assembled nature of the SACS Toolkit is its primary strength. While grounded in a defined mathematical framework, the SACS Toolkit is methodologically open-ended and therefore adaptable and amenable, allowing researchers to employ and bring together a wide variety of modeling techniques. Researchers can even develop and modify the SACS Toolkit for their own purposes. The other strength of the SACS Toolkit, which makes it a very effective technique for modeling large databases, is its ability to compress data matrices while preserving the most important aspects of a complex system??s structure and dynamics across time/space. To date, while the SACS Toolkit has been used to study several topics, a mathematical outline of its case-based approach to quantitative analysis (along with a case study) has yet to be written?Chence the purpose of the current paper.     

A survey on piecewise-linear models of regulatory dynamical systems

Recent developments in understanding the various regulatory systems, especially the developments in biology and genomics, stimulated an interest in modelling such systems. Hybrid systems, originally developed for process control applications, provide advances in modelling such systems. A particular class of hybrid systems which are relatively simpler to analyze mathematically but still capable of demonstrating the essential features of many non-linear dynamical systems is piecewise-linear systems. Implementation of piecewise-linear systems for modelling of regulatory dynamical systems requires different considerations depending on the status of the problem. In this work we considered three different cases. Firstly, we consider the inferential modelling problem based on the empirical observations and study the discrete piecewise-linear system, whose inverse problem is solvable under some assumptions. Secondly, we considered the problem of obtaining some complex regulatory systems by tractable piecewise-linear formulations and study the qualitative dynamic features of the systems and their piecewise-linear models. Finally, we considered Boolean delay equations for building abstract models of regulatory systems, which might be the simplest models demonstrating the essential qualitative features of our interest underlying adaption, learning and memorization.     

Utilizing buffer management to improve performance in a healthcare environment

For over 50 years, the United Kingdom’s National Health Service has aspired to provide universal access to quality health care. However, as evidenced by huge backlogs and lengthy waiting times for many services, the NHS is showing the strain of accommodating rising contemporary expectations within a constrained budget. This paper describes the concept of buffer management, its origins in manufacturing applications, and how it can be applied to generate improvements in health care systems. The successful implementation of buffer management is illustrated with recent applications in the Accident and Emergency departments and the hospital admissions process of three NHS facilities.     

A characteristic line based method to build finite-dimensional medoels of heat exchangers

surface heat exchngers are typical simulated with simplified models obtained through segmentation of the heat exchanging fluid path in a number of consecutive lumps In order to aviod major drawbacks of this approach, which may be very misleading for control design purpose, we propose a method, based on the intergration of the PDE system by the method of characteristic lines, for the construction of numerical heat exchangers models. It can be proved that the time response of such new models is indeed rid of parasitic oscillation and suitable for the understanding of complex dynamic phenomena occurring and suitable for the understanding of complex dynamic phenomena occurring in long residence time heat exchangers, both with one- and two- phase flow. In this paper, particular attention is paid to the problem of generating finite dimensional dynamic system by application of the characteristic lines method and computing the frequency responce of such models. Actually, since the characteristic lines method is not naturally is not straightforward to define Finally, the accuracy of CL models is compared with classical models of comparable complexity, with special reference to real application cases, taken from the power generation field.     

Qualitative response of interaction networks: application to the validation of biological models

We advocate the use of qualitative models for the analysis of shift equilibria in large biological systems. We present a mathematical method, allowing qualitative predictions to be made of the behaviour of a biological system. These predictions are not dependent on specific values of the kinetic constants. We show how these methods can be used to improve understanding of a complex regulatory system. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)     

Employing integrated reference models to represent interdependency in a complex enterprise

Successful implementation of change in a complex enterprise requires a shared understanding of system interdependency. Otherwise, the architecture of those changes risk the emergence of otherwise unforeseen obstacles. A fundamental element is understanding how the system responds to stimuli. Given the complexity of that system, no single model would adequately represent the totality of the enterprise. As such, we have employed a structured approach based on soft systems methodology and reference models to develop common pictures. These heuristic models act as anchor points for achieving a shared understanding and as a basis for the development of more detailed models. The approach has been applied to defence preparedness; a system containing many levels of inter-dependency, contested by a range of differing viewpoints, multilayered with decisions and activity at a number of levels, and often seeking to rapidly transition to solutions. We present some examples of distinct but inter-related reference models for defence preparedness.     

The SEXTANT software: A tool for automating the comparative analysis of mental models of dynamic systems

The comparison of mental models of dynamic systems improves our understanding of how people comprehend, interpret, and subsequently influence dynamic management tasks. Approaches to comparing mental models currently used in managerial and organizational cognition research, especially the distance-ratio and the closeness-approaches, have been criticized for not considering essential characteristics of dynamic managerial situations. This paper builds on a recent analysis method developed to compare mental models of dynamics systems, and introduces this mathematical approach to management and organizational researchers by means of the SEXTANT software. It presents the process of mental model elicitation, analysis, comparison, and interpretation. An example with four elicited mental models illustrates the software’s features to analyze and present the results. Then, the software is compared with existing software to map and compare mental models. Our conclusion is that SEXTANT marks a significant step in enabling large-scale studies about mental models of dynamic systems.     

Optimal p--cyclic SOR

Summary. Our purpose in this paper is to extend --cyclic SOR theory to consistent singular systems and to apply the results to the solution of large scale systems arising, {\em e.g.,} in queueing network problems in Markov analysis. Markov chains and queueing models lead to structured singular linear systems and are playing an increasing role in the understanding of complex phenomena arising in computer, communication and transportation systems. For certain important classes of singular problems, we develop a convergence theory for --cyclic SOR, and show how to repartition for optimal convergence. Results by Kontovasilis, Plemmons and Stewart on the concept of convergence of SOR in an {\em extended} sense are rigorously analyzed and applied to the solution of periodic Markov chains with period . Received October 20, 1992 / Revised version received September 14, 1993     

Analysis,modeling, emergence & integration in complex systems: A modeling and integration framework & system biology

Analysis and Modeling is the first “phase” of understanding or developing a system. It is also, maybe more importantly, the foundation of understanding a natural science or system. It's abstract and conceptually difficult but, being foundational, contributes the most to the quality of understanding of (designed or natural) systems. Complex Systems have a natural hierarchy of levels and multiple subsystems. The character and functionality of each level or subsystem “emerges” across its boundaries. Both sides of these boundaries must be understood within that side's natural thought patterns. Integrated interdisciplinary collaboration is essential for making sense of complex systems; but collaboration among disciplines is difficult, because of their different ways of thinking. This creates a dilemma, “understanding complex systems” is one horn; “integrated interdisciplinary collaboration” is the other. This dilemma in complex system analysis/modeling and interdiscipline collaboration, is currently addressed by “grabbing the bull by the horns.” This takes on this doubly complex problem, by painstakingly building up abstract “bull wrestling” skills in and across domains and disciplines. There's another wrinkle; complexity requires interdisciplinary collaboration at deeper, more dissimilar, levels. The usual approach, finding a way to “pass between the horns of the dilemma” will not work here, due to this cross coupling. Rather than trying to pass between the horns, by abstracting away the coupling, we overtly organizing this coupling. We weave a semantic unification space of conceptual connections linking each side of a boundary to its appropriate way of thinking. This allows us to abstracting away the dilemma and iron out the wrinkle. The threads of common image schemas, cognitive metaphors and conceptual interfaces, weave a bridge between the semantics foundations and organizations of each problem. These allow addressing the problems synergistically. This paper presents and explores a naturally valid way for discipline specific and discipline integrating addressing complex systems. We start with the methodological insights from analysis and modeling from the perspective of object orientation, with its ontologies, organizing lexical semantics. We advance from there by integrating in imagistic, imaginative semantics and affordance based interaction methodology, as the keys to addressing complex systems analysis, modeling and integration. © 2007 Wiley Periodicals, Inc. Complexity, 2007