Traditional computer simulation terminology includes taxonomic divisions with terms such as "discrete event," "continuous," and "process oriented." Even though such terms have become familiar to simulation researchers, the terminology is distinct from other disciplines ---such as artificial intelligence and software engineering--- which have similar goals relating specifically to modelling dynamic systems. There is a need to unify terminology among these disciplines so that system modelling is formalized in a common framework. We present a perspective that serves to characterize simulation models in terms of their procedural versus declarative orientations since these two orientations are prevalent throughout most modelling disciplines that we have encountered. We used a sample dynamic system (e.g., two jug problem) found in artificial intelligence to highlight the connecting threads in system modelling within each discipline. Moreover, in teaching simulation students using this perspe...

The large number of components, nonlinear interactions, time delays and feedbacks, and spatial heterogeneity together often make ecological systems overwhelmingly complex. This complexity must be effectively dealt with for understanding and scaling. Hierarchy theory suggests that ecological systems are nearly completely decomposable (or nearly decomposable) systems because of their loose vertical and horizontal coupling in structure and function. Such systems can thus be simplified based on the principle of time-space decomposition. Patch dynamics provides a powerful way of dealing explicitly with spatial heterogeneity, and has emerged as a unifying

Recent trends in software engineering, especially within the object-oriented community, reflect a clear trend toward systems engineering methods. Object oriented designs, meant for programming design, often take on the distinct appearance of system models of physical networks and devices. It is not immediately apparent how the areas of "modeling" and "programming" relate to one another, and why the convergence is taking place. To explore the convergence in depth, we discuss common concepts between models and programs, and discuss future trends in computer science which are forging a steady convergence between models and programs. The convergence is spawned by increased emphasis on object oriented design, distributed systems, complex systems, new forms of analog computation, and abstraction methodology. We close with a discussion of MOOSE, which provides a comprehensive modeling environment for both programmers and modelers.

The concept of topology-aware grid applications is derived from parallelized computational models of complex systems that are executed on heterogeneous resources, either because they require specialized hardware for certain calculations, or because their parallelization is flexible enough to exploit such resources. Here we describe two such applications, a multi-body simulation of stellar evolution, and an evolutionary algorithm that is used for reverse-engineering gene regulatory networks. We then describe the topology-aware middleware we have developed to facilitate the “modeling-implementing-executing ” cycle of complex systems applications. The developed middleware allows topology-aware simulations to run on geographically distributed clusters with or without firewalls between them. Additionally, we describe advanced coallocation and scheduling techniques that take into account the applications topologies. Results are given based on running the topology-aware applications on the Grid’5000 infrastructure. 1.

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The ultimate goal of grid technologies is to materialize the vision of grids as virtual supercomputers of unprecedented power, through utilization of geographically disperse distributively owned resources. Despite the overwhelming success of grids in running pleasantly parallel tasks, there still exists a large set of demanding applications considered the exclusive prerogative of real supercomputers. A few examples include complex systems and weather simulations, computational fluid dynamics and other tightly coupled parallel applications. These rely on a static execution environment with predictable performance, provided through efficient co-allocation of a large number of reliable homogeneously interconnected resources. In this paper, we describe a novel quasiopportunistic supercomputer system that enables execution of demanding parallel applications in grids through identification and implementation of the set of key technologies required to bridge the gap between grids and supercomputers. These technologies include an economic incentive-based framework for establishing and maintaining grid-wise allocation agreements; a co-allocation subsystem that is integrated with the economic framework and enhanced by communication topology-aware allocation mechanisms; a fault tolerant message passing library that hides the failures of the underlying resources; and data pre-staging orchestration.

Complex, large-scale, integrated, open systems (CLIOS) are a class of systems of special interest in the socio-technical domain. Because of the many sub-systems, the uncertainty in subsystem behavior and interaction and the degree of human agency involved, the emergent behavior of CLIOS is difficult to predict and often counterintuitive, even when subsystem behavior is readily predictable. These attributes make it difficult to represent and study CLIOS. We have developed a CLIOS Process for studying systems of interest. The CLIOS Process can be used as an organizing mechanism for understanding a system’s underlying structure and behavior, identifying strategic options for improving the system’s performance, and deploying and monitoring those strategic options. A key motivation behind the need for a CLIOS Process is the presence of “nested complexity”, which results when a physical system is nested inside a policy system, where both are complex and interdependent. The study of the CLIOS will require that a variety of tools and processes be employed, with quantitative engineering and economic models being used for the physical system and more qualitative institutional, organizational and stakeholder frameworks being used for the policy system. An important aspect of the CLIOS Process is the integration of physical and policy system analysis.

Physics and chemistry underlie the nature of all the world around us, including human brains. Consequently some suggest that in causal terms, physics is all there is. However we live in an environment dominated by objects embodying the outcomes of intentional design (buildings, computers, teaspoons). The present-day subject of physics has nothing to say about the intentionality resulting in existence of such objects, even though this intentionality is clearly causally effective. This paper examines the claim that the underlying physics uniquely causally determines what happens, even though we can’t predict the outcome. It suggests that what occurs is the contextual emergence of complexity: the higher levels in the hierarchy of complexity have autonomous causal powers, functionally independent of lower-level processes. This is possible because top-down causation takes place as well as bottom-up action, with higher-level contexts determining the outcome of lower level functioning and even modifying the nature of lower level constituents. Stored information plays a key role, resulting in non-linear dynamics that is nonlocal in space and time. Brain functioning is causally affected by abstractions such as the value of money and the theory of the laser. These are realised as brain states in individuals, but are not equivalent to them. Consequently physics per se can't causally determine the outcome of human creativity, rather it creates the possibility space allowing human intelligence to function autonomously. The challenge to physics is to develop a realistic description of causality in truly complex hierarchical structures, with top-down causation and memory effects allowing autonomous higher levels of order to emerge with genuine causal powers.

Firms faced with a portfolio of inconsistent design tasks often fail to get the best out of their investment in product design efforts. We present the concept of design focus as a strategic alternative for gaining competitive advantage through product design. The concept is based on the premise that design task homogeneity facilitates effective information processing and efficient utilization of R&D resources. It induces faster learning, which leads to substantial gain in an organization’s competence in dealing with product development challenges. In addition, a focused design strategy benefits from the advantages of managing a smaller portfolio. Design focus is an extension of the concept of focus from the business strategy literature to the new product development literature and it is complementary to the concept of manufacturing and marketing focus. A cross sectional study of forty-four new product development projects provides preliminary empirical evidence supporting the proposed concept, and emphasizes the need for further investigation design focus as a strategic alternative for managing product design. The concept provides a fresh perspective on the management of product design and opens up new directions for future research.

This paper is part of a focused paper panel discussion addressing the results of an invited workshop conducted in Orlando, Florida in February, 1997. The workshop addressed the question: What makes a modeling and simulation professional? The workshop's results included a consensus ideal modeling and simulation professional characterized by eight categories and subsequent elements. Each of the eight categories and elements are presented as well as contextual narrative. The author provides an interpretation of the workshop results and implications for modeling and simulation professionals. Panel members were asked to respond to the goals and results of the workshop, as well as, the author's interpretations and comments of this paper. The paper and panel discussion are an attempt to spark debate on the modeling and simulation profession. 1