Modeling Complex Systems

Abstract Empirical observations suggest that linear dynamics are not an adequate representa- tion of ecological systems and that a realistic representation would require adoption of complex nonlinear dynamical systems with characteristics encountered in complex adaptive systems (CAS). Adequate modelling should include and combine, among others, strategic interactions among economic agents, nonconvexities induced by non-linear feedbacks, separate spatial and temporal scales and modeling of spatiotempo-ral dynamics, and allowance of alternative time scales. Ignoring these characteristics might obscure very important features that we observe in reality such as bifurcations and irreversibilities or hysteresis. As a consequence, the design of policies that do not take CAS characteristics into account might lead to erroneous results and undesirable states of managed economic-ecological systems.Complex adaptive systems, differential games, spatiotemporal dynamics, fast-slow variables.

The innovation network as a complex adaptive system: flexible multi-agent based modeling, simulation and evolutionary decision making

The literature rarely considers an innovation network as a complex adaptive system. In this paper, theories of complex adaptive systems research are employed to model and analyze intra-organization networks, inter-organization networks as well as their interaction mechanisms in the whole innovation context, with a conceptual framework proposed and presented. Flexible multi-agent based modeling, smart simulation, self-survival and adaptive intelligent software agents, expert systems, analytic hierarchy process, hybrid decision support approach, and statistical methods are integrated to deal with the innovation network problem and support evolutionary decision making in the open and dynamic environments

Complex Adaptive Systems, Agent-Based Modeling and Information Assurance

Management of information security issues can be viewed as a complex adaptive system in that hackers are constantly developing new means of trying to penetrate security systems and access information assets. Organizational must adapt too these threats by updating security procedures and systems and by training employees in what must be done to counteract new threats. We present agent-based models that illustrate “phishing” problems, and General Deterrence Theory and Protection Motivation Theory and their application to IA problems. These models are written in Netlogo, an open-source agent-based modeling system, and are freely available for education and training in IA

Modeling forward base camps as complex adaptive sociotechnical systems

Work for this thesis focuses on managing complexity within complex adaptive sociotechnical systems by using model based systems engineering and virtual engineering tools. The hypothesis of the work is that integrated virtual models can be used to increase the understanding of these complex adaptive sociotechnical systems, resulting in a reduction in the perceived complexity. This was tested by the use of a two factor survey given to experts of a system (the customer and members of the model design team) and to a target user-group. This group received a demonstration and had hands on experience with a preliminary model of the same system. Results of the survey show that new system designers using an integrated virtual modeling tool view the system as less complex than experts involved with designing the same system without using a tool. Further data is required to support this conclusion, and a plan for gathering more data is described. The application of this method to an emergency response system is then discussed to show how it can be applied to other complex sociotechnical systems and guidelines for applying this methodology are proposed

Simulation-Based Optimization: Implications of Complex Adaptive Systems and Deep Uncertainty

Within the modeling and simulation community, simulation-based optimization has often been successfully used to improve productivity and business processes. However, the increased importance of using simulation to better understand complex adaptive systems and address operations research questions characterized by deep uncertainty, such as the need for policy support within socio-technical systems, leads to the necessity to revisit the way simulation can be applied in this new area. Similar observations can be made for complex adaptive systems that constantly change their behavior, which is reflected in a continually changing solution space. Deep uncertainty describes problems with inadequate or incomplete information about the system and the outcomes of interest. Complex adaptive systems under deep uncertainty must integrate the search for robust solutions by conducting exploratory modeling and analysis. This article visits both domains, shows what the new challenges are, and provides a framework to apply methods from operational research and complexity science to address them. With such extensions, simulation-based approaches will be able to support these new areas as well, although optimal solutions may no longer be obtainable. Instead, robust and sufficient solutions will become the objective of optimization processes

Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system

Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS): A conceptual framework

In recent years there has been increasing interest in applying the computer based problem solving techniques of Artificial Intelligence (AI), Operations Research (OR), and Decision Support Systems (DSS) to analyze extremely complex problems. A conceptual framework is developed for successfully integrating these three techniques. First, the fields of AI, OR, and DSS are defined and the relationships among the three fields are explored. Next, a comprehensive adaptive design methodology for AI and OR modeling within the context of a DSS is described. These observations are made: (1) the solution of extremely complex knowledge problems with ill-defined, changing requirements can benefit greatly from the use of the adaptive design process, (2) the field of DSS provides the focus on the decision making process essential for tailoring solutions to these complex problems, (3) the characteristics of AI, OR, and DSS tools appears to be converging rapidly, and (4) there is a growing need for an interdisciplinary AI/OR/DSS education