The Perspectives on Developing a Conceptual Model for Exploring Emergent Behaviors in Complex Systems

Non-Commutativity, Incompatibility, Emergent Behavior and Decision Support Systems

This manuscript critically examines existing research on cyber battle management systems (CBMS) and underscores the importance of advancing complex structure thinking, cybernetics, wicked problem-solving, and emerging behavior analysis. It advocates for a systems-thinking approach to solving complex problems by identifying and understanding associated systems, predicting their behavior, and managing changes. The manuscript explores the integration of cybernetics meta-methodology and the viable system model with metasystems reductionism to address negative emergent behavior in complex systems. The study highlights the roles of individual systems, systems of systems, and metasystems, emphasizing the deterministic nature of single systems and the stochastic characteristics of systems of systems. By integrating cybernetics, viable system models, and meta-metasystems, the manuscript explores key parameters for building intelligent systems, revealing that meta-metasystems offer superior capabilities for coordinating and integrating multiple systems. The research results demonstrate the successful development of a meta-metasystem tailored for CBMS, providing a strategic framework for the future of cyber battle management.

The evaluation of the emergent behaviour in complex systems requires an analytical framework which allows the observation of different phenomena that take place at different levels. In order to observe the dynamics of complex systems, it is necessary to perform simulations so that both local and the emergent behaviour can be observed. To this end, the way in which complex system simulators are built must be examined so that it will be feasible to model large scale scenarios. In this paper, the use of Model Driven Engineering methodology is proposed to deal with this issue. Among other benefits, it is shown that this methodology allows the representation and simulation of a complex system providing support for the analysis. This analysis is supported by a metamodel which describes the system components that are under study. The application of this methodology to the development of large scale simulators is explored through a case study. This case study analyses a complex socio-technical system: a power grid.

This article summarizes a network and complex systems science model for research on whole systems of complementary and alternative medicine (CAM) such as homeopathy and traditional Chinese medicine. The holistic concepts of networks and nonlinear dynamical complex systems are well matched to the global and interactive perspectives of whole systems of CAM, whereas the reductionistic science model is well matched to the isolated local organ, cell, and molecular mechanistic perspectives of pharmaceutically based biomedicine. Whole systems of CAM are not drugs with specific actions. The diagnostic and therapeutic approaches of whole systems of CAM produce effects that involve global and patterned shifts across multiple subsystems of the person as a whole. For homeopathy, several characteristics of complex systems, including the probabilistic nature of attractor patterns, variable sensitivity of complex systems to initial conditions, and emergent behaviors in the evolution of a system in its full environmental context over time, could help account for the mixed basic science and controlled clinical trial research findings, in contrast with the consistently positive outcomes of observational studies in the literature. Application of theories and methods from complex systems and network science can open a new era of advances in understanding factors that lead to good versus poor individual global outcome patterns and to rational triage of patients to one type of care over another. The growing reliance on complex systems thinking and systems biology for cancer research affords a unique opportunity to bridge between the CAM and conventional medical worlds with some common language and conceptual models.

Agent-based models (ABM) and their simulations have been used to study complex systems with interacting entities and to model multi-agent systems. Simulations are used to explore the dynamic consequences of these models. In many cases, the behaviours that are of interest are emergent ones that arise as a result of interactions between agents rather than the actions of any individual agent. In this paper, we propose a formalism for describing emergent behaviours at any level of abstraction based on the idea that event types can be defined that characterise sets of behavioural 'motifs'. This provides the basis for a method for studying the associations between multi-level behaviours in simulations. There are two categories of hypotheses that we seek to address with respect to an ABM and its simulations: - Hypotheses concerned with associations between emergent behaviours defined at various levels of abstraction. - Hypotheses concerned with the links between parameter sensitivity / initial conditions and emergent behaviours e.g. the ABM is sensitive to a parameter x because x predisposes the system or part of the system to exhibit a particular (emergent) behaviour.

Complex Systems and Emergent Behavior : Engaging with Computer Games to Enrich Organization Studies

Engineering projects for complex technical systems such as automobiles demand extensive requirement specifications and corresponding hierarchy levels in system architectures. Especially when considering emergent phenomena, such as total weight or aerodynamics, a closely networked collaboration of discipline‐specific and cross‐disciplinary roles is required. Further, in large organizations with a group structure, resulting functional and non‐functional contents need to be managed by distinct Systems Engineering roles. For example, the role “property manager” takes care of achieving overarching product properties, such as weight or aerodynamics, which cannot be directly fixed, but result from many specifications. This paper proposes new Systems Engineering roles and their application in a German Original Equipment Manufacturer (OEM). For validation, the roles have already been applied in everyday engineering projects at the OEM. The concept proved to be indispensable and transferable to other Systems Engineering projects.

The multi-domain model established based on Modelica can effectively describe the dynamics and randomicity of failure behavior in complex systems. Firstly, the characteristics of multi-domain model are described, and the modeling methodology of failure behavior in complex systems based on multi-domain language is introduced; then the multi-domain model of actuator is established based on the components behavioral model library; finally, the actuator system is simulated and results are analyzed. The results show that not only the dynamic failure behaviors of complex systems, transfer process of different behaviors and performance fluctuations caused by failure behavior, but also the stochastic time when fault happens and uncertainty of parameters can be described by the proposed modeling methodology. It is validated that the methodology is useful for engineering application.

We present a method to eliminate redundancy in the transition tables of Boolean automata: schema redescription with two symbols. One symbol is used to capture redundancy of individual input variables, and another to capture permutability in sets of input variables: fully characterizing the canalization present in Boolean functions. Two-symbol schemata explain aspects of the behaviour of automata networks that the characterization of their emergent patterns does not capture. We use our method to compare two well-known cellular automata rules for the density classification task [1]: GKL [2], [3] and GP [4]. We show that despite having very different emergent behaviour, these rules are very similar. Indeed, GKL is a special case of GP. Therefore, we demonstrate that it is more feasible to compare cellular automata via schema redescriptions of their rules, than by looking at their emergent behaviour, leading us to question the tendency in complexity research to pay much more attention to emergent patterns than to local (micro-level) interactions.

The ‘top‐down’ system design process of functional decomposition is increasingly being criticised as simplistic, outmoded, and (in particular) too ‘reductionist’. Its use on large, complex systems is frequently portrayed as naive and impractical. There is much comment on the need to concentrate, instead, on the ‘emergent behaviours’ of systems, and to adopt a ‘holistic’ design approach in which a system solution is ‘evolved’ through a process of system design synthesis.This paper presents a counter‐argument that top‐down design decomposition can, in fact, be a highly efficient way to produce good (optimal) system solutions .Many of the fundamentals of function based system design are addressed: the central role of system function in the design process; the characterisation of system complexity and complication; the identification and specification of component requirements; and the determination of optimal design solutions.An established mathematical theory of systems design, using formal constructs and set‐theory notation, is used as the basis for the presentation of ideas.

Large-scale computational molecular models provide scientists a means to investigate the effect of microscopic details on emergent mesoscopic behavior. Elucidating the relationship between variations on the molecular scale and macroscopic observable properties facilitates an understanding of the molecular interactions driving the properties of real world materials and complex systems (e.g., those found in biology, chemistry, and materials science). As a result, discovering an explicit, systematic connection between microscopic nature and emergent mesoscopic behavior is a fundamental goal for this type of investigation. The molecular forces critical to driving the behavior of complex heterogeneous systems are often unclear. More problematically, simulations of representative model systems are often prohibitively expensive from both spatial and temporal perspectives, impeding straightforward investigations over possible hypotheses characterizing molecular behavior. While the reduction in resolution of a study, such as moving from an atomistic simulation to that of the resolution of large coarse-grained (CG) groups of atoms, can partially ameliorate the cost of individual simulations, the relationship between the proposed microscopic details and this intermediate resolution is nontrivial and presents new obstacles to study. Small portions of these complex systems can be realistically simulated. Alone, these smaller simulations likely do not provide insight into collectively emergent behavior. However, by proposing that the driving forces in both smaller and larger systems (containing many related copies of the smaller system) have an explicit connection, systematic bottom-up CG techniques can be used to transfer CG hypotheses discovered using a smaller scale system to a larger system of primary interest. The proposed connection between different CG systems is prescribed by (i) the CG representation (mapping) and (ii) the functional form and parameters used to represent the CG energetics, which approximate potentials of mean force (PMFs). As a result, the design of CG methods that facilitate a variety of physically relevant representations, approximations, and force fields is critical to moving the frontier of systematic CG forward. Crucially, the proposed connection between the system used for parametrization and the system of interest is orthogonal to the optimization used to approximate the potential of mean force present in all systematic CG methods. The empirical efficacy of machine learning techniques on a variety of tasks provides strong motivation to consider these approaches for approximating the PMF and analyzing these approximations.

The data deluge is changing the operating environment of many sensing systems from data-poor to data-rich-so data-rich that we are in jeopardy of being overwhelmed. Managing and exploiting the data deluge require a reinvention of sensor system design and signal processing theory. The potential pay-offs are huge, as the resulting sensor systems will enable radically new information technologies and powerful new tools for scientific discovery.

: LONG-TERM GOALS. The long-term goal of this research is to develop and test predictive models for nearshore processes. This is to be accomplished within the framework of the nearshore as a hierarchical complex system, wherein, at discretely ordered space and time scales, a small number of variables emerge as the dominant influences on the dynamics of this nonlinear, open system and the interactions between these dominant variables give rise to complex, emergent behavior. OBJECTIVES. Attractors, states to which a system evolves from a broad range of initial conditions, are robust features of systems characterized both by significant nonlinearity and high rates of dissipation, such as the nearshore. The evolution of a system to an attractor selects a temporal hierarchy of dominant dynamical variables, ordered so that variables characterized by a particular time scale enslave faster variables and evolve within a context determined by slower variables. Motivated by a hierarchical, complex systems framework for nearshore processes, the specific objectives of this research project are (i) to identify the dominant variables and processes operative in the nearshore; (ii) to formulate and develop predictive, complex systems models for nearshore processes and features, including sand bars, megaripples, breaking waves, infragravity wave generation, surf zone currents, and swash zone flow and morphology; (iii) to test these models with existing field data; (iv) to relate complex systems models to measurements acquired through remote sensing; (v) to propose and design new field experiments capable of refuting these models.

Complex systems such as cities, energy grids, or the global climate have many plausible futures. Scenarios, or structured narratives of decision-relevant futures, are a common decision support tool for making the complexity and uncertainties of complex systems humanly interpretable. However, the effectiveness of scenario-based decision support depends in part on the usefulness of the selected scenarios. Here we show an optimization-based approach for generating scenarios that are specifically designed to be diverse, plausible, and comprehensive. We establish the advantages of our method by evaluating it against three previously proposed methods: scenario matrices, generic archetypes, and clustering. Our case study is Schelling’s segregation model, a tractable yet behaviorally rich simulation of a complex system. Our results show the proposed optimization-based approach can generate more diverse, plausible, and comprehensive scenarios than existing approaches. The resulting scenarios may provide a more insightful and robust basis for policy decisions, especially for complex systems with emergent behavior or where substantial uncertainties are present.

Understanding the behavior of complex systems is becoming a crucial issue as systems grow in size, and the interconnection and geographical distribution of their components diversifies. The interaction over time of many components often leads to emergent behavior, which can be harmful to the system. Despite this, very few practical approaches for the identification of emergent behavior exist, and many are unfeasible to implement. Approaches using interaction as a measure of emergence have the potential to alleviate this problem. In this paper, we analyse absolute and relative methods that use interaction as a measure of emergence. Absolute methods compute a degree of interaction that characterizes a system state as being emergent. Relative methods compare interaction graphs of the system state with interaction graphs of systems that have been shown previously to exhibit emergence. We present these approaches and discuss their advantages and limitations using theoretical and experimental analysis.