Emergence in stigmergic and complex adaptive systems: A formal discrete event systems perspective

ArticleinCognitive Systems Research 21:22–39 · March 2013with 130 Reads 
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Abstract
Complex systems have been studied by researchers from every discipline: biology, chemistry, physics, sociology, mathematics and economics and more. Depending upon the discipline, complex systems theory has accrued many flavors. We are after a formal representation, a model that can predict the outcome of a complex adaptive system (CAS). In this article, we look at the nature of complexity, then provide a perspective based on discrete event systems (DEVS) theory. We pin down many of the shared features between CAS and artificial systems. We begin with an overview of network science showing how adaptive behavior in these scale-free networks can lead to emergence through stigmergy in CAS. We also address how both self-organization and emergence interplay in a CAS. We then build a case for the view that stigmergic systems are a special case of CAS. We then discuss DEVS levels of systems specifications and present the dynamic structure extensions of DEVS formalism that lends itself to a study of CAS and in turn, stigmergy. Finally, we address the shortcomings and the limitation of current DEVS extensions and propose the required augmentation to model stigmergy and CAS.

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  • ... stigmergy refers to a theory combining complex adaptive systems theory, chaos theory, and distributed cognition to explain behaviors of humans(Christensen, 2013;Marsh & Onof, 2008;Dipple, Raymond, & Docherty, 2014;Mittal, 2013;Ricci, Omicini, Viroli, & Gardelli, 2006). Unlike simpler forms of entomology (insect) coordination of work(Dipple et al., 2014;Grassé, 1959;Marsh & Onof, 2008) which have been applied to the coordination of human work in Wikipedia(Elliott, 2016) and open-source software development(Bolici, Howison, & Crowston, 2016), cognitive stigmergy extends substantially beyond by considering human cognition. ...
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  • ... When investigating emergence (i.e., the result of the process self-organization), scholars often rely on the theory of complex adaptive systems (CAS) (Alaa and Fitzgerald, 2013;Mittal, 2013). Non-linearity, emergence, and selforganization are major characteristics of CAS. ...
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  • ... Emergence of hierarchical structures from bottom-up phenomena occurs in natural complex systems and emergence of clusters and hubs appears to be aided by top-down phenomena [1]. Such systems are self-similar or fractal in nature and often studied as complex adaptive systems [11]. When viewed from systems perspective, the decision making is goal-oriented and follows the top-down approach while the information flows according to the bottom-up approach. ...
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  • ... The new paradigm on the CS in social science is presented in a recent research [10]. There is a substantial volume of publications in the field of complex systems (see [11,12] for a complete review). ...
  • ... Emergent phenomenon is a system's behavior, defined by outcomes that cannot be reduced to the behavior of constituent agent-environment interactions (Deguet et.al., 2006). Put simply, emergent behavior is detectable only at a level above local interacting agents (Bass 1994, Banabeau and Dessalles 1997, Doyle and Kalish 2004, Mittal 2012) situated in their environment. This is the case because objects and environments contain perceptual information. ...
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  • ... This implied parallels between neuronal activity and social behavior, grounded in the characteristics of emergent behavior. This suggests that stigmergistic (indirect coor- dination) interactions of different entities in a system can result in a complex adaptive system (Mittal, 2012), which is characteristic of intelligence or cognition (Doyle and Marsh, 2012). Gabora and DiPaola (2012) had used artifi- cial neural networks and genetic algorithms in DOs to demonstrate that chaining previously emergent tech- niques in generative art is capable of producing human- perceived creative art works. ...
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  • ... 26,27 The adaptive capacity of CAS gives them a natural resilience to potential disturbances. 28 Since we aim to improve the agility of C2 networks, the inherent resilience of CAS is of great interest. CAS that focus on cyber systems and threats can be defined as CyCAS. ...
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  • ... Mittal [5] and various others [3,6] argue that it is largely an Observer phenomenon, implying that one has to acknowledge the existence of emergent behavior from an external vantage point of the SoS as a whole. To understand this concept, the easiest example is trail-formation in the ant foraging exercise. ...
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  • ... The way and algorithms to control structure changes are leaved to users. Comparatively, Barros [6] defined the dynamic structure system network using the DEVS formalism, but the work based on the vision of an executive that resides as a kind of all-mighty atomic model in the coupled model [42], showed limited capability to deal with complex systems that consist of autonomy entities and is essentially a centralized mechanism of controlling structure changes [43]. ...
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  • ... This results from the emergence that can only be detected at levels higher than the agents. In addition, CSs can have multiple spatial and temporal scales (Elsner et al., 2015;Lichtenstein, 2014;Mittal, 2013;Nicolet, 2010).  Distributed Decision-making: In a CS, the highest level of the hierarchy does not manage and guide the system. ...
  • ... Emergence has been characterized as taking place in strong and week forms [5,12]. Mittal [6] pointed out that strong emergent behavior results in generation of new knowledge about the system representing previously unperceived complex interactions. This can occur in the form of one or more of new abstraction levels and linguistic descriptions, new hierarchical structures and couplings, new component behaviors, and new feedback loops. ...
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  • ... Emergence has been characterized as taking place in strong and week forms. Mittal [1] pointed out that strong emergent behavior results in generation of new knowledge about the system representing previously unperceived complex interactions. This can occur in the form of one or more of new abstraction levels and linguistic descriptions, new hierarchical structures and couplings, new component behaviors, and new feedback loops. ...
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  • ... The concept of emergence first appeared in philosophy in the time of Socrates and has been studied in a variety of fields since [Di Marzo Serugendo et al. 2006]. It is a fundamental concept in complex systems and a necessary part of what qualifies complex systems as complex [Flake 1998;Mittal 2013]. An extensive body of literature exists that attempts to characterise emergence but, despite this, there is no widely accepted definition of emergence, though a number of characteristics are commonly discussed Dessalles and Phan 2001;Fromm 2005a]. ...
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  • ... Emergent behavior, a macro behavior that is irreducible at the micro level, is a characteristic property of any CAS, along with many other properties like selforganization, non-linearity and order/chaos dynamics (Mittal 2013a). Reproducing emergent behavior in an artificial environment, such as through M&S endeavor is a non-trivial problem. ...
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  • ... Emergent phenomenon is a system's behavior, defined by outcomes that cannot be reduced to the behavior of constituent agent-environment interactions (Deguet et.al., 2006). Put simply, emergent behavior is detectable only at a level above local interacting agents (Bass 1994, Banabeau and Dessalles 1997, Doyle and Kalish 2004, Mittal 2012) situated in their environment. This is the case because objects and environments contain perceptual information. ...
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  • ... This robustness in both structure and behavior M&S ensures that the unwanted holistic behaviors, also known as negative emergent behaviors are explicitly avoided, along with the guaranteed manifestation of the desired (or positive) emergent behaviors. Mittal (2013) advocates the use of DEVS formalism for developing a CAS M&S infrastructure. Further, the entire simulation experiment, the model and the simulation infrastructure must be automated through a model-based repository (e.g. a model base) and transparent simulation framework based on the theory of modeling and simulation (Mittal, Martin and Zeigler 2007, Mittal and Martin 2013, Zeigler and Nutaro 2016, Zeigler, Praehofer and Kim 2000, Zeigler, Muzy and Kofman 2018. ...
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  • ... In the sociotechnical era of Internet of Things (IoT), wherein multiple domains (for example, cyber, physical, and computational across various societal sectors) are involved, experimenting with the model to understand the model's functionality and engineer the resulting complex system is a challenging task. The existing toolsets lack the needed simulation analysis and experimentation capabilities leading to epistemological emergent behaviors, which is a characteristic defining property of any complex system [6]. These emergent behaviors can be both positive and negative. ...
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  • ... Based on the research conducted in support of [Tolk et al., 2018a], this contribution gives examples of natural and computational complex adaptive systems and observable emergence, introduces a critical review of the categories of emergence from the philosophy of science perspective, and concludes that there are significant epistemological constraints when computational methods are used for evaluating emergence. It uses these insights as extended in [Tolk et al., 2018b], and furthermore shaped by ideas exchanged with Dr. Bernie Zeigler, based on his observations on closure under coupling presented in [Zeigler, 2018] as well as discussions with Saurabh Mittal and his ideas about emergence as discussed in [Mittal, 2013a, Mittal andRainey, 2015]. ...
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  • ... Consequently, per the Law of Requisite Variety, it is impossible to develop a controller for such a complex system [6]. This incomplete information and uncertainty in developing control mechanisms lead to emergent behaviors which is the hallmark of any complex system [7,3]. Consequently, methodologies are needed that embrace emergent behaviors as features of such a complex system. ...
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  • ... Nearly three decades later, In Emergence in Stigmergic and Complex Adaptive Systems: A Formal Discrete Event Systems Perspective, Mittal (2013) analyzed two classes of complex systems: stigmergic systems and complex adaptive system. A stigmergic system is a multi-agent system where agents interact through a persistent environment. ...
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    Model-Based Systems Engineering (MBSE) employs model-based technologies and established systems engineering practices. Model-Driven Engineering (MDE) provides various concepts to automate model based practices using metamodeling. We describe the DEVS Unified Process (DUNIP) that aims to bring together MBSE and MDE as Model-driven Systems Engineering (MDSE) and apply it in a netcentric en-vironment. We historically look at various model-based and model-driven flavors and suggest MDSE/DUNIP as one of the derived methodologies. We describe essential elements in DUNIP that facili-tate integration with architecture solutions like Service Oriented Architecture (SOA), Event Driven Archi-tectures (EDA), Systems Entity Structures (SES) ontology, and frameworks like Department of Defense Architecture Framework (DoDAF 2.0). We discuss systems requirement specifications, verification and validation, metamodeling, Domain Specific Languages (DSLs), and model transformation technologies as applicable in DUNIP. In this article, we discuss the features and contributions of DUNIP in netcentric system of systems engineering.
  • Conference Paper
    Full-text available
    Sociotechnical systems pervade every facet of our life today. These IT systems interact with live users that result in emergent behaviors leading to their classification as complex adaptive systems (CAS). Cyber-CAS (CyCAS) exists in contemporary society when such systems have Internet as their platform. Modeling and Simulation (M&S) for CyCAS is extremely difficult: partly due to inherent complex nature of enterprise IT architectures and partly due to lack of sufficient formal processes used in enterprise systems architecting. Fundamentally, the interaction between various elements is hard to pin down as enterprise architecture components are often treated as black-box with limited information about their internal behaviors. In this article, I provide an overview of the M&S approach for enterprise frameworks, introduce CyCAS, the tools and metrics for various CyCAS Views and the sandbox requirements for CyCAS M&S.
  • Conference Paper
    System of Systems can be classified as virtual, collaborative, acknowledged and directed per the MITRE System Engineering Guide. Emergent behavior can be classified as simple, weak, strong and spooky. While simple and weak emergent behavior can be analyzed using modeling and simulation practices, strong emergent behavior cannot be modeled due to existing knowledge gaps within the model. Spooky emergent behavior cannot be explained with any knowledge input as the behavior is inconsistent with the known properties of the system. We describe various qualitative knowledge engineering methodologies that can be used to close the knowledge-gap for strong emergent behavior and add contextualization with system-of-systems overall purpose so that modeling and simulation can be applied in systems engineering life cycle for reproducible and predictable SoS emergent behavior.
  • Chapter
    The major concern in any engineering problem is to improve the ongoing system. While planning the improvement strategies, there are various applications to check for suggested implications. Usage of computers simplifies these applications by simulation techniques. Especially in industrial engineering, simulation is a popular modeling instrument. Simulation has various types which can be selected depending on the nature and objectives of the problem. Within a wide spectrum, discrete event and system dynamics simulation techniques are chosen for this study. The aim is to compare these two methods with two perspectives; existing work specifically on healthcare and basic simulation modeling steps. Comparison starts with the main modeling procedures are “adapted” for these two methods and used to clarify the differences between these two popular simulation techniques. In the following step, existing studies on healthcare are compared by classifying them according to nature of the problem.
  • Chapter
    This chapter proposes architecture to identify and analyze potential emergent behaviors in multi‐agent systems as they happen. It defines and implements three metrics, namely, Hausdorff distance, active Hausdorff distance, and statistical complexity, all of which can detect emergence. The chapter then considers weak emergence as being the macro‐level behavior that is a result of micro‐level component interactions and strong emergence as the macro‐level feedback or causation on the micro‐level. It studies the assumption that emergence is determined by the interactions that take place between the micro‐level agents and thus weak emergence occurs. The chapter further presents a brief overview of prototype implementation followed by an overview of the three models used for experiments: flock of birds, game of life, and predator‐prey. The current prototype implementation of the architecture consists of three separate components: Modeling and Simulation, the MetricAggregator, and the MetricSuite.
  • Article
    Full-text available
    Abstract: In this study, the effect of organizational citizenship behavior (OCB) and company image on productivity and customer loyalty in Kaleh Company as a case study have been reviewed. To this end, 200 managers and experts in this company enjoying a good deal of knowledge in the field of research were selected by purposive sampling and evaluated through questionnaire and interview. For analyzing the quantitative data and explaining the relationship between the variables in the model, the structural equation modeling by using LISREL software was used. This study is functional in terms of goal and in terms of method is a descriptivecorrelation research. The research findings show that organizational citizenship behavior (OCB) and company image directly and significantly affect company productivity and customer loyalty. In addition, Customer loyalty has a significant relationship with the company productivity. Keywords: organizational citizenship behavior (ocb), company image, customer loyalty, productivity, LISREL.
  • Article
    This paper studies how to determine task allocation schemes according to the status and requirements of various teams, to achieve optimal performance for a knowledge-intensive team (KIT), which is different from traditional task assignment. The way to allocate tasks to a team affects task processing and, in turn, influences the team itself after the task is processed. Considering the knowledge requirement of tasks as a driving force and that knowledge exchange is pivotal, we build a KIT system model based on complex adaptive system theory and agent modeling technology, design task allocation strategies (TASs) and a team performance measurement scale utilizing computational experiment, and analyze how different TASs impact the different performance indicators of KITs. The experimental results show the recommend TAS varies under different conditions, such as the knowledge levels of members, team structures, and tasks to be assigned, particularly when the requirements to the team are different. In conclusion, we put forward a new way of thinking and methodology for real task allocation problems and provide support for allocation decision makers.
  • Conference Paper
    We consider the ability of Discrete Event System Specification (DEVS) to provide the concepts and formalisms needed for modeling and simulation of emergent behavior. We show that DEVS provides systems components and coupling for models of systems of systems with emergent behavior. Further, DEVS coupling supports dynamic structure for adaptive and evolution, and the Experimental Frame supports Emergence Behavior Observation, and a recent extension, DEVS Markov models, supports prediction of emergence derived from such observation. Finally, we introduce a concept of interactive specification based on generators that has the potential to provide a system-theoretic characterization of emergence modeling using language concepts.
  • Chapter
    The previous chapters have elaborated the application of the concepts from systems theories to examples drawn from technical systems, biological systems and organisational systems; this chapter intends to have a further look at the applications. Beyond these three domains, there are also other domains that have benefited from systems approaches, such as psychology and communication. For example, applications of non-linear dynamic systems theory to psychology have led to advances in understanding neuromotor development and advances in theories of cognitive development [Metzger, 1997].
  • Chapter
    Undesired or unexpected properties are frequent, as large-scale complex systems with nonlinear interactions are being designed and implemented to answer real-life scenarios. Identifying these behaviors as they happen as well as determining whether these behaviors are beneficial for the system is crucial to highlight potential faults or undesired side effects early in the development of a system, thus promising significant cost reductions. Beyond the inherent challenges in identifying these behaviors, the problem of validating the observed emergent behavior remains challenging, as this behavior is, by definition, not expected or envisaged by system designers. This chapter presents an overview of existing work for the automated detection of emergent behavior and discusses some potential solutions to the challenge of validating emergent behavior. Building on the idea of comparing an identified emergent behavior with previously seen behaviors, we propose a two-step process for validating emergent behavior. Our initial experiments using a Flock of Birds model show the promise of this approach but also highlight future avenues of research.
  • Conference Paper
    The purpose of this study is to research the task allocation problem of the knowledge intensive team (abbreviated as KIT), which is different from the traditional task assignment. We built a KIT system model, designed task allocation strategies and team performance measurement scale, based on complex adaptive system (abbreviated as CAS) theory with regarding the knowledge requirement of tasks as a primer mover, additionally, took into consideration that knowledge exchange behaviors and processes would be contingent when different team members deal with different tasks. The computational experimental method was used to analyze how different allocation strategies impact KIT performance. The experimental results show that different allocation strategies variously influence KIT performance when the team members, team structures, and tasks to be assigned are different. We would be appreciated to help the decision maker, before the real tasks are executed, to apply the computational experiment method proposed in this paper to carry out the task allocation to provide with decision support.
  • Chapter
    This chapter introduces simple, complicated, and complex system definitions and shows how these system classes are related to simple, weak, strong, and spooky emergence, and which systems engineering methods can be applied to support the detection, understanding, and management of such emergence. It considers the purpose of systems engineering, as a discipline that allows us to apply scientific principles and engineering methods to cope with the challenges of complexity and emergences. The chapter also considers several simulation methods that can help to better understand and manage emergence, and the limits of such approaches. Mapping emergence categories to system properties to exclude unwanted emergence and to provide the ability to select the right system properties when certain emergence categories shall be observable for further research has been the focus of other recent work. Emergence has ontological, epistemological, and methodological perspectives, and they address different sets of challenges.
  • Chapter
    A recent paper laid a systems theoretic foundation for understanding how human language could have emerged from prelinguistic elements. The systems theoretic approach to incorporating emergence (as a construct) to understand a complex phe- nomenon first required the formulation of a system model of the phenomena. Having the correct formal system specification is a key to demonstrating that the obtained holistic behaviors denote the intended emergent behavior. The case study of human language is presented as an instance of a set of language-ready components that must be coupled to form a system with innovative inter-component information exchange. In this chapter, we first review the systems theoretic foundation for modeling accu- rate emergent behavior. We then review the activity-based monitoring paradigm and the emergent property of attention-focusing in resource-constrained activity-based complex dynamical intelligent systems (RCIDS): a class of systems that exhibit intel- ligent behaviors. We then pose the problem of shared attention among hominins within these paradigms. A fundamental property of stable systems may lie in their ability to obtain useful information from the world by suppressing activity unrelated to current goals and thereby to satisfy the requirements for well definition of self as system. This includes the case of nascent social systems containing humans as emerging linguistic agents.
  • Chapter
    Induced emergence is presented as a consequence of goal-directed steering of social systems. Multimodels offer a rich paradigm to model complex systems including complex social systems. Thirty types of multimodels are presented in an ontology-based dictionary where their definitions are given with their taxonomy. Formal modeling incorporating model structure and model coupling and especially dynamic coupling is emphasized with their relevance to multimodeling of social systems. Evaluation of the state-of-the-art tools that can implement the concept of induced emergence is presented.
  • Chapter
    In this chapter, we attempt to set the research agenda on emergence in the medium and long term. We first summarize the view of emergence from the authors in this book and conclude that almost all of them are focused on epistemological emergence, which results in better understand systems but fails in explaining the emergence of new categories as we seem to observe in the real world. Although the epistemological perspective is essential, the community must also focus on ontological emergence. We propose a simulation experience approach (SEA) based on the mix of live–virtual and constructive (LVC) simulation. We demonstrate that the research in employing emer- gence in complex systems (CSs) engineering must be transdisciplinary and propose a set of grand challenges that must be tackled to move forward.
  • Chapter
    This chapter recognizes that contemporary model-based system engineering must be robustly supported by modeling and simulation (M&S) professionals armed with theory, concepts, and tools up to the challenges of Cyber environments replete with multiple subject matter experts (SMEs) in any given scenario. It also aims to establish the need for formal methods and tools to build M&S workbenches to create reusable results, have a common, understandable, and reproducible way to solve problems with M&S. Next, the chapter provides an overview on Cyber Complex Adaptive Systems (CyCAS) framework and its various characteristics. It then describes the cyber and Cyber-Range environment and the concept of a Logical Range. The chapter further presents the fundamental M&S theory, introduces formal Discrete Event Systems (DEVS) and discusses Cyber M&S with DEVS at length. Finally, it deals with the verification and validation of cyber models with a simulation-based engineering process.
  • Article
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  • Article
    We study the small-world network model, which mimics the transition between regular-lattice and random-lattice behavior in social networks of increasing size. We contend that the model displays a critical point with a divergent characteristic length as the degree of randomness tends to zero. We propose a real-space renormalization group transformation for the model and demonstrate that the transformation is exact in the limit of large system size. We use this result to calculate the exact value of the single critical exponent for the system, and to derive the scaling form for the average number of `degrees of separation' between two nodes on the network as a function of the three independent variables. We confirm our results by extensive numerical simulation.
  • Conference Paper
    The goal of this paper is to explore the notion of complex system and, in particular the emergence phenomenon, in order to see which lessons could be learned for both understanding and designing complex software systems. Complex systems are described as sets of non-linearly interacting components making multi-agent systems particularly suitable for modelling and designing such systems. The notion of emergence is explicited and used to derive ways of understanding and designing such complex systems. We conclude by discussing the pros and cons of the emergentist approaches and the research perspectives.
  • Article
    Variable structure refers to the ability of a system to dynamically change its structure according to different situations. It provides component-based modeling and simulation environments with powerful modeling capability and the flexibility to design and analyze complex systems. In this article, the authors discuss variable structure—specifically, the structure change and interface change capability—in DEVS-based modeling and simulation environments. The operations of structure change and interface change are discussed, and their respective operation boundaries are defined. Three examples are given to illustrate the role of variable structure and how it can be used to model and design adaptive complex systems. Principles for the implementation of variable structure are also presented and illustrated in the DEVSJAVA modeling and simulation environment.
  • Article
    Keywords: DEVS, PDEVS, dynDEVS, dynPDEVS, dynNPDEVS, rho-DEVS, ML-DEVS, EPI-DEVS, paced, unpaced, abstract threaded, abstract sequential, hierarchical sequential, flat sequential, partitioning, load-balancing, event queues, requeue operation, JAMES II
  • Article
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    Stigmergy is a process by which local agents acting on the environment change the environment in such a way as to change another agent’s behavior. This form of indirect communication and local interactions between agents and the environment often leads to the self-organization of system structures. We present an experiment in which robot agents act independently to move objects (blocks) about in their environment, and inspect the outcome of these actions for clusters as an indication of increased organization. Results indicate that the agents did not act stigmergically, regardless of whether their behavior was fixed or random. However, the results indicate that the starting location of the blocks did have a significant effect on the final distributions. The effect initial conditions, types of emergence, and self-organization have on the global product of swarm behavior are discussed and future research is proposed.
  • Article
    The Kadanoff theory of scaling near the critical point for an Ising ferromagnet is cast in differential form. The resulting differential equations are an example of the differential equations of the renormalization group. It is shown that the Widom-Kadanoff scaling laws arise naturally from these differential equations if the coefficients in the equations are analytic at the critical point. A generalization of the Kadanoff scaling picture involving an "irrelevant" variable is considered; in this case the scaling laws result from the renormalization-group equations only if the solution of the equations goes asymptotically to a fixed point.
  • Article
    Full-text available
    As the number of flexible, adaptable systems grows so does the need for specification and analysis tools that support adaptable system structures. The increasing number of simulation tools that equip models with the capability of changing their behavior patterns, composition, and interactions raises the desire for a theoretical and methodological approach. A formalism is introduced based on DEVS which emphasizes the reflective nature of variable structure models. The proposed formalism and DEVS are shown to be bisimilar, which emphasizes the role of variable structure models as an agency of modularization. The formalism is used to reveal general problems and solutions in implementing variable structure models.
  • Article
    Full-text available
    Two different object-oriented modeling approaches, DEVS and EMSY, constitute the background to explore the area of variable structure modeling. Realizations of various kinds of structural changes are discussed in both approaches. Against the background of their prime application domains, both approaches deal with the problem of structural change differently. While DEVS emphasizes intelligent control of structural change, EMSY stresses the autonomous character of the system. Like autonomy and control, holism and reductionism play different roles in both approaches and affect the realization of structural changes. However, unlike the former which tend to transcend each other, the reductionistic and holistic view realized in the two modeling approaches prove to set a rigorous framework for variable structure modeling
  • Article
    Nature has been able to evolve (several times) natural systems which produce complex spatio-temporal patterns from agents with very simple behaviours by exploiting the interactions between the agents and their environment. Surprisingly, the systematic use of these prin-ciples have been mostly neglected within the field of collective robotics. We study the use of these biological principles in nature and their ap-plication in artificial systems and hypothesize on the role between such principles and evolution. We conclude with future directions towards collective intelligence in multi-agent robotics.
  • Article
    Computer modeling and simulation is recognized by John Holland and many others as the central tool with which to experiment on complex adaptive systems (CAS). Less well recognized is that in the last thirty years, advances in the theory of modeling and simulation have crystallized a new class of models suitable for the computational requirements of CAS studies. This article discusses the abstractions underlying the DEVS formalism, a system theoretic characterization of discrete event simulations, that has been widely adopted in recent years. Abstraction of events and time intervals from a continuous data stream is shown to carry information that can be efficiently employed, not only in simulation, but also in accounting for the real world constraints that shape the information processes within CAS. Indeed, an important paradigm is emerging in which discrete event abstraction is recognized as fundamental to modeling CAS phenomena at various levels of organization. Discrete event models of neurons, neural processing architectures, and "fast frugal" bounded rational decision making and shortest path solvers are discussed as examples. Such models capture ideas that are coming from various disparate directions and offer evidence that a new modeling and simulation paradigm is emerging.
  • A collective intelligence consists of a large number of quasi-independent, stochastic agents, interacting locally both among themselves as well as with an active environment, in the absence of hierarchical organization, and yet which is capable of adaptive behavior. The major concepts arising from our current understanding of collective intelligence are reviewed. These include stochastic determinism, interactive determinism, nondirected communication, nonrepresentational contextual dependency, stigmergy. These are illustrated using examples drawn from the literature on ant behavior. Several speculations into the dynamics of collective intelligence are presented, including nondispersive temporal evolution, broken ergodicity and broken symmetry. Several questions for future study are posed.
  • Conference Paper
    We present the DSDE (Dynamic Structure of Discrete Events) formalism, a methodology for representing discrete event systems that change structure dynamically. We prove that the DSDE formalism is closed under coupling and that it can be used to construct hierarchical and modular models. The abstract simulators which are necessary to execute dynamic structure models are also presented. These simulators allow a description of models independent of the actual simulation procedure, and thus encourage model reuse
  • Conference Paper
    Full-text available
    The goal of systems biology is to analyze the behavior and interrelationships between entities of entire functional biological systems. Discrete event approaches are of particular interest if small numbers of entities, like DNA molecules, shall be modeled. Two general approaches toward discrete event modeling and simulation are presented. They provide rather different perspectives on the system to be modeled, as is illustrated based on a model of the Trypophan Operon. Whereas in Devs distinctions are emphasized, e.g. between system and its environment, between structural and non structural changes, between properties attributed to a system and the system itself, these distinctions become fluent in the compact description of the π-calculus. However, both share the problem that in order to support a comfortable modeling, adaptations and extensions according to the concrete requirements of this challenging application area are needed.
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    A clear terminology is essential in every research discipline. In the context of ESOA, a lot of confusion exists about the meaning of the terms emergence and self-organisation. One of the sources of the confusion comes from the fact that a combination of both phenomena often occurs in dynamical systems. In this paper a historic overview of the use of each concept as well as a working definition, that is compatible with the historic and current meaning of the concepts, is given. Each definition is explained by supporting it with important characteristics found in the literature. We show that emergence and self-organisation each emphasise different properties of a system. Both phenomena can exist in isolation. The paper also outlines some examples of such systems and considers the combination of emergence and self-organisation as a promising approach in complex multi-agent systems.
  • Conference Paper
    The geographical threshold graph model is a random graph model with nodes distributed in a Euclidean space and edges assigned through a func- tion of distance and node weights. We study this model and give conditions for the absence and existence of the giant component, as well as for connectivity.
  • Article
    Full-text available
    Many structures built by social insects are the outcome of a process of self-organization, in which the repeated actions of the insects interact over time with the changing physical environment to produce a characteristic end state. A major mediating factor is stigmergy, the elicitation of specific environment-changing behaviors by the sensory effects of local environmental changes produced by previous behavior. A typical task involving stigmergic self-organization is brood sorting: Many ant species sort their brood so that items at similar stages of development are grouped together and separated from items at different stages of development. This article examines the operation of stigmergy and self-organization in a homogeneous group of physical robots, in the context of the task of clustering and sorting Frisbees of two different types. Using a behavioral rule set simpler than any yet proposed for ant sorting, and having no capacity for spatial orientation or memory, the robots are able to achieve effective clustering and sorting showing all the signs of self-organization. It is argued that the success of this demonstration is crucially dependent on the exploitation of real-world physics, and that the use of simulation alone to investigate stigmergy may fail to reveal its power as an evolutionary option for collective life forms.
  • Article
    Full-text available
    Variable structure refers to the ability of a system to dynamically change its structure according to different situations. It provides component-based modeling and simulation environments with powerful modeling capability and the flexibility to design and analyze complex systems. In this paper, we discuss variable structure, specifically the structure and interface change capability, in DEVS-based modeling and simulation environments. The operations of structure and interface changes are discussed and their respective operation boundaries are defined. Three examples are given to illustrate the role of variable structure and how it can be used to model and design adaptive complex systems. Principles for the implementation of variable structure are also presented and illustrated in the DEVSJAVA modeling and simulation environment.
  • Article
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    We present a new concept for a system network to represent systems that are able to undergo structural change. Change in structure is defined in general terms, and includes the addition and deletion of systems and the modification of the relations among components. The structure of a system network is stored in the network executive. Any change in structure-related information is mapped into modifications in the network structure.Based on these concepts, we derive three new system specifications that provide a shorthand notation to specify classes of dynamic structure systems. These new formalisms are: dynamic structure discrete time system, dynamic structure differential equation specified systems, and dynamic structure discrete event system specification. We demonstrate that these formalisms are closed under coupling, making hierarchical model construction possible. formalisms are described using set theoretic notation and general systems theory concepts.
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    Emergence, a concept that first appeared in philosophy, has been widely explored in the domain of complex systems and is sometimes considered to be the key ingredient that makes ‘complex systems’ ‘complex’. Our goal in this paper is to give a broad survey of emergence definitions, to extract a shared definition structure and to discuss some of the remaining issues. We do not know of any comparable surveys about the emergence concept. For this presentation, we start from a broadly applicable approach and finish with more specific propositions. We first present five selected works with a short analysis of each. We then propose a merged analysis in which we isolate a common structure through all definitions but also what we think needs further research. Finally, we briefly describe some perspectives about the emergence engine idea also referred to as emergent engineering.
  • Article
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    Two different conceptions of emergence are reconciled as two instances of the phenomenon of detection. In the process of comparing these two conceptions, we find that the notions of complexity and detection allow us to form a unified definition of emergence that clearly delineates the role of the observer.
  • Article
    Complex adaptive systems (cas) – systems that involve many components that adapt or learn as they interact – are at the heart of important contemporary problems. The study of cas poses unique challenges: Some of our most powerful mathematical tools, particularly methods involving fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas. Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/41486/1/11424_2006_Article_1.pdf
  • Article
    Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.
  • Article
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    In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.
  • Article
    Full-text available
    In this paper we present the first mathematical analysis of the protein interaction network found in the yeast, S. cerevisiae. We show that, (a) the identified protein network display a characteristic scale-free topology that demonstrate striking similarity to the inherent organization of metabolic networks in particular, and to that of robust and error-tolerant networks in general. (b) the likelihood that deletion of an individual gene product will prove lethal for the yeast cell clearly correlates with the number of interactions the protein has, meaning that highly-connected proteins are more likely to prove essential than proteins with low number of links to other proteins. These results suggest that a scale-free architecture is a generic property of cellular networks attributable to universal self-organizing principles of robust and error-tolerant networks and that will likely to represent a generic topology for protein-protein interactions. Comment: See also http:/www.nd.edu/~networks and http:/www.nd.edu/~networks/cell
  • Article
    We study empirically the time evolution of scientific collaboration networks in physics and biology. In these networks, two scientists are considered connected if they have coauthored one or more papers together. We show that the probability of a pair of scientists collaborating increases with the number of other collaborators they have in common, and that the probability of a particular scientist acquiring new collaborators increases with the number of his or her past collaborators. These results provide experimental evidence in favor of previously conjectured mechanisms for clustering and power-law degree distributions in networks.
  • Article
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    Topological properties of "scale-free" networks are investigated by determining their spectral dimensions d(S), which reflect a diffusion process in the corresponding graphs. Data bases for citation networks and metabolic networks together with simulation results from the growing network model [A.-L. Barabasi and R. Albert, Science 286, 509 (1999)] are probed. For completeness and comparisons lattice, random and small-world models are also investigated. We find that d(S) is around 3 for citation and metabolic networks, which is significantly different from the growing network model, for which d(S) is approximately 7.5. This signals a substantial difference in network topology despite the observed similarities in vertex-order distributions. In addition, the diffusion analysis indicates that the citation networks are treelike in structure, whereas the metabolic networks contain many loops.
  • Conference Paper
    Full-text available
    In computational biology there is an increasing need to combine micro and macro views of the system of interest. Therefore, explicit means to describe micro and macro level and the downward and upward causation that link both are required. Multi-Level-DEVS (or m^-DEVS) supports an explicit description of macro and micro level, information at macro level can be accessed from micro level and vice versa, micro models can be synchronously activated by the macro model and also the micro models can trigger the dynamics at macro level. To link both levels, different methods are combined, to those belong, value coupling, synchronous activations, variable ports, and invariants. The execution semantic of the formalism is given by an abstract simulator and its use is illustrated based on an small extract of the Wnt pathway.
  • Conference Paper
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    Open systems are part of a paradigm shift from algorithmic to interactive computation. Multiagent systems in nature that exhibit emergent behavior and stigmergy offer inspiration for research in open systems and enabling technologies for collaboration. This contribution distinguishes two types of interaction, directly via messages, and indirectly via persistent observable state changes. Models of collaboration are incomplete if they fail to explicitly represent indirect interaction; a richer set of system behaviors is possible when computational entities interact indirectly, including via analog media, such as the real world, than when interaction is exclusively direct. Indirect interaction is therefore a precondition for certain emergent behaviors.
  • Article
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    Computers and Thought are the two categories that together define Artificial Intelligence as a discipline. It is generally accepted that work in Artificial Intelligence over the last thirtyyears has had a strong influence on aspects of computer architectures. In this paper we also make the converse claim# that the state of computer architecture has been a strong influence on our models of thought. The Von Neumann model of computation has lead Artificial Intelligence in particular directions. Intelligence in biological systems is completely different. Recentwork in behavior-based Artificial Intelligence has produced new models of intelligence that are much closer in spirit to biological systems. The non-Von Neumann computational models they use share manycharacteristics with biological computation. Copyright c fl Massachusetts Institute of Technology, 1991 This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for this researchwas provided in part by the University Research Initiative under Office of Naval Research contract N00014--86--K--0685, in part bytheAdvanced Research Projects Agency under Office of Naval Researchcontract N00014-- 85--K--0124, in part by the Hughes Artificial Intelligence Center, in part by Siemens Corporation, and in part by Mazda Corporation. 1