Systemic environmental decision making: designing learning systems

Informed by first and second-order cybernetic understandings the case is made for the design of learning systems as a socially relevant form of praxis for situations of complexity, uncertainty and conflict. Two case studies of designing are considered. In the first, students of two versions of the Open University course 'Environmental decision making: a systems approach' use a 'learning system' heuristic designed to encourage them to start off systemically in environmental decision making (EDM). They do this by exploring decision-making situations before formulating problems, opportunities and systems of interest in situations of complexity. Learning from the design of learning systems for students can inform research practice. In the second case a systemic approach for managing water through social learning based on the design of a systemic inquiry is described. Drawing from these examples the authors explore the cybernetic and systemic nature of their design praxis making the case for first and second-order designing as well as systemic and systematic practice to be treated as a duality rather than a dualis

Energy and complexity: new ways forward

The purpose of this paper is to review the application of complexity science methods in understanding energy systems and system change. The challenge of moving to sustainable energy systems which provide secure, affordable and low-carbon energy services requires the application of methods which recognise the complexity of energy systems in relation to social, technological, economic and environmental aspects. Energy systems consist of many actors, interacting through networks, leading to emergent properties and adaptive and learning processes. Insights on these type of phenomena have been investigated in other contexts by complex systems theory. However, these insights are only recently beginning to be applied to understanding energy systems and systems transitions. The paper discusses the aspects of energy systems (in terms of technologies, ecosystems, users, institutions, business models) that lend themselves to the application of complexity science and its characteristics of emergence and coevolution. Complex-systems modelling differs from standard (e.g. economic) modelling and offers capabilities beyond those of conventional models, yet these methods are only beginning to realize anything like their full potential to address the most critical energy challenges. In particular there is significant potential for progress in understanding those challenges that reside at the interface of technology and behaviour. Some of the computational methods that are currently available are reviewed: agent-based and network modelling. The advantages and limitations of these modelling techniques are discussed. Finally, the paper considers the emerging themes of transport, energy behaviour and physical infrastructure systems in recent research from complex-systems energy modelling. Although complexity science is not well understood by practitioners in the energy domain (and is often difficult to communicate), models can be used to aid decision-making at multiple levels e.g. national and local, and to aid understanding and allow decision making. The techniques and tools of complexity science, therefore, offer a powerful means of understanding the complex decision-making processes that are needed to realise a low-carbon energy system. We conclude with recommendations for future areas of research and application

System Approach – Decision Support in the Management of Environmental Protection

Relationships between society and environment became an important subject for research in all domains of science. The emergency of problem solving and the incomplete knowledge of numerous elements, processes and interactions have created a number of problematic situations in managerial terms. Therefore, in the field of management researches pursued to cherish the accumulated experience and to formulate or reformulate a number of theories according to the requirements of environmental protection. Solving environmental problems at managerial level faced many difficulties due to insufficient information, unclear responsibilities, reduced predictability, large social implications, and increased social and political pressure. The system approach could bring in a useful perspective for decision making by supplying information on the possible evolution and characteristics of the ecosystems and socio-ecological systems that occur under the influence of economic, social and political factors at different spatial and temporal scales.human-nature relation; system; complexity; decision making; environmental protection.

Modeling Decision Making In Trauma Centers From The Standpoint Of Complex Adaptive Systems

This research examines complex clinical decision-making processes in trauma center units of hospitals in terms of the impact of complexity on the medical team involved in the trauma event. The science of complex adaptive systems together with human judgment theories provide important concepts and tools for responding to health care challenges in this century and beyond. Clinical decision-makers in trauma centers are placed in urgent and anxious situations that are increasingly complex, making decision-making and problem-solving processes multifaceted. Under stressful circumstances, physicians must derive their decision-making schemas (―internal models‖ or ―mental models‖) without the benefits of judicious identification, evaluation, and/or application of relevant medical information, and always using fragmentary data. This research developed a model of decision-making processes in trauma events that uses a Bayesian Classifier model jointly with Convolution and Deconvolution operators to study real-time observed trauma data for decision-making processes under stress. The objective was to explore and explain physicians‘ decision-making processes during actual trauma events while under the stress of time constraints and lack of data. The research addresses important operations that describe the behavior of a dynamic system resulting from stress placed on the physician‘s rational decision making processes by the conditions of the environment. Deconvolution, that is, determining the impulse response of the system, is used to understand how physicians clear out extraneous environmental noise in order to have a clearer picture of their mental models and reach a diagnosis or diagnostic course of action

Integration Readiness levels Evaluation and Systems Architecture: A Literature Review

The success of complex systems projects is strongly influenced by their architecture. A key role of a system architect is to decide whether and how to integrate new technologies in a system architecture. Technology readiness levels (TRL) scale has been used for decades to support decision making regarding the technology infusion in complex systems, but it still faces challenges related to the integration of technologies to a system architecture. Integration Readiness Levels (IRL) scale has been elaborated in the last decade to face these challenges, representing the integration maturity between the technological elements of a system. The aim of this theoretical article is to perform a literature review on IRL scale evaluation and on systems architecture, through bibliographic research. Results show the review organized in five topics that surrounds the research objective, presenting the IRL and TRL scales evolution, comparing their evaluation practices, and exploring the architecture complexity of systems. Suggestions for future research are proposed based on these results

The SIMRAND methodology: Theory and application for the simulation of research and development projects

A research and development (R&D) project often involves a number of decisions that must be made concerning which subset of systems or tasks are to be undertaken to achieve the goal of the R&D project. To help in this decision making, SIMRAND (SIMulation of Research ANd Development Projects) is a methodology for the selection of the optimal subset of systems or tasks to be undertaken on an R&D project. Using alternative networks, the SIMRAND methodology models the alternative subsets of systems or tasks under consideration. Each path through an alternative network represents one way of satisfying the project goals. Equations are developed that relate the system or task variables to the measure of reference. Uncertainty is incorporated by treating the variables of the equations probabilistically as random variables, with cumulative distribution functions assessed by technical experts. Analytical techniques of probability theory are used to reduce the complexity of the alternative networks. Cardinal utility functions over the measure of preference are assessed for the decision makers. A run of the SIMRAND Computer I Program combines, in a Monte Carlo simulation model, the network structure, the equations, the cumulative distribution functions, and the utility functions

Computational medicine, present and the future: obstetrics and gynecology perspective.

Medicine is, in its essence, decision making under uncertainty; the decisions are made about tests to be performed and treatments to be administered. Traditionally, the uncertainty in decision making was handled using expertise collected by individual providers and, more recently, systematic appraisal of research in the form of evidence-based medicine. The traditional approach has been used successfully in medicine for a very long time. However, it has substantial limitations because of the complexity of the system of the human body and healthcare. The complex systems are a network of highly coupled components intensely interacting with each other. These interactions give those systems redundancy and thus robustness to failure and, at the same time, equifinality, that is, many different causative pathways leading to the same outcome. The equifinality of the complex systems of the human body and healthcare system demand the individualization of medical care, medicine, and medical decision making. Computational models excel in modeling complex systems and, consequently, enabling individualization of medical decision making and medicine. Computational models are theory- or knowledge-based models, data-driven models, or models that combine both approaches. Data are essential, although to a different degree, for computational models to successfully represent complex systems. The individualized decision making, made possible by the computational modeling of complex systems, has the potential to revolutionize the entire spectrum of medicine from individual patient care to policymaking. This approach allows applying tests and treatments to individuals who receive a net benefit from them, for whom benefits outweigh the risk, rather than treating all individuals in a population because, on average, the population benefits. Thus, the computational modeling-enabled individualization of medical decision making has the potential to both improve health outcomes and decrease the costs of healthcare

Simulation: A Tool for System Design and Analysis

This research paper delves into the symbiotic relationship between simulation and systems theory, elucidating how simulation serves as a potent instrument for system design and analysis within the framework of systems theory. Systems theory offers a foundational perspective that accentuates holism, interdependence, emergence, feedback loops, hierarchy, adaptability, and boundary delineation in comprehending intricate systems. It encapsulates the complexity of real-world systems, rendering simulations invaluable for comprehensive analysis. Through the systems theory lens, simulation scrutinizes interdependencies, unveils emergent phenomena, and incorporates feedback loops, all while accommodating adaptability to evolving conditions. The pivotal concept of defining system boundaries, significant in both systems theory and simulation, ensures that researchers focus on the most pertinent facets of their subjects. This theoretical framework finds versatile applications across diverse domains, spanning manufacturing, healthcare, urban planning, and environmental science. In manufacturing, simulation models optimize processes by considering the holistic nature of production systems. In healthcare, systems theory and simulation facilitate evidence-based decision-making, leading to enhanced patient outcomes. In urban planning, simulation models navigate intricate traffic management interactions, while in environmental science, they assess ecosystem dynamics amid changing conditions. Ultimately, this research paper underscores the synergy between systems theory and simulation, showcasing how this alliance deepens our comprehension of complex systems and empowers informed decision-making in an ever-evolving milieu

The Process of Solving Complex Problems

This article is about Complex Problem Solving (CPS), its history in a variety of research domains (e.g., human problem solving, expertise, decision making, and intelligence), a formal definition and a process theory of CPS applicable to the interdisciplinary field. CPS is portrayed as (a) knowledge acquisition and (b) knowledge application concerning the goal-oriented control of systems that contain many highly interrelated elements (i.e., complex systems). The impact of implicit and explicit knowledge as well as systematic strategy selection on the solution process are discussed, emphasizing the importance of (1) information generation (due to the initial intransparency of the situation), (2) information reduction (due to the overcharging complexity of the problem’s structure), (3) model building (due to the interconnectedness of the variables), (4) dynamic decision making (due to the eigendynamics of the system), and (5) evaluation (due to many, interfering and/or ill-defined goals)

Risky Decisions and Decision Support - Does Stress Make a Difference?

Studies of human decision making have demonstrated that stress exacerbates risk taking. Since all decisions involve some element of risk, stress has critical impact on decision quality. Decisions are found to improve with stress up to an optimal threshold beyond which deterioration is observed. However, few studies have examined the psychological experiences underlying risk-taking behavior in conjunction with stress creators. In this paper we propose a research framework that integrates pre-conditions of stress (perceptions of high gain/loss, risk, complexity, and organizational pressure) with observed psychological experiences (time pressure, uncertainty, information overload, and dynamism) that potentially result in risky decision making. This framework suggests that decision support systems have the potential of mitigating or enhancing the psychological perceptions of stress and, hence, impacting decision quality. Empirical testing of a component of this framework provided interesting preliminary results. Subjects experiencing high stress indicated the same levels of perceived uncertainty and dynamism as subjects exposed to low stress, suggesting that use of a decision support system mitigated the perceptions of dynamism and uncertainty for the high stress group. Contrary to hypotheses, the use of a decision support system did not mitigate perceptions of information overload