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Modeling the Forest or Modeling the Trees A Comparison of System Dynamics and AgentBased Simulation
 in Proceedings of the 21st International Conference of the System Dynamics Society
, 2003
"... System Dynamics and Agentbased Simulation are two approaches that use computer simulation for investigating nonlinear social and socioeconomic systems with a focus on the understanding and qualitative prediction of a system’s behavior. Although the two schools have a broad overlap in research top ..."
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System Dynamics and Agentbased Simulation are two approaches that use computer simulation for investigating nonlinear social and socioeconomic systems with a focus on the understanding and qualitative prediction of a system’s behavior. Although the two schools have a broad overlap in research topics they have been relatively unnoticed by each other so far. This paper contributes to the crossstudy of System Dynamics and AgentBased Simulation. It uncovers and contrasts the primary conceptual predispositions underlying the two approaches. Moreover, ideas about how the approaches could be integrated are presented. Key words: System Dynamics, Agentbased Simulation
Emergent Structures in Supply Chains  A Study Integrating AgentBased and System Dynamics Modeling
, 2003
"... complicated task due to its broad scope and the strong connectedness of its objects and issues. In order to make theoretical investigations of supply chains feasible and to support decisionmaking in real world supply chains, simulation models are used. An integration of system dynamics and discrete ..."
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complicated task due to its broad scope and the strong connectedness of its objects and issues. In order to make theoretical investigations of supply chains feasible and to support decisionmaking in real world supply chains, simulation models are used. An integration of system dynamics and discrete agentbased modeling is a promising combination of methods for reducing the a priori complexity of the model. The paper discusses the strengths and weaknesses of system dynamics and discrete agentbased modeling. An approach for integration of the two modeling methodologies is presented. Issues concerning the practical coupling of software environments and a simple, prototypical supply chain model are discussed. Experiments for which the integrated simulation solution is applied are described. Insights in emergent structures in supply chains are derived from these simulation analyses.
Integrating System Dynamics and AgentBased Modeling
"... This paper presents an approach to integrate the system dynamics and the agentbased modeling techniques. After reviewing the fundamental principles of the two modeling approaches, an agentbased supply chain simulation model is developed. The model consists of two levels of aggregation; on the macro ..."
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This paper presents an approach to integrate the system dynamics and the agentbased modeling techniques. After reviewing the fundamental principles of the two modeling approaches, an agentbased supply chain simulation model is developed. The model consists of two levels of aggregation; on the macro level a discrete agentbased modeling approach is applied, on the micro, the agent level system dynamics is used to model the agents ’ internal cognitive structure. The paper concludes with first preliminary simulation results and aspects of future research. Coming from the field of complexity science, the agentbased modeling approach gains growing popularity. At the core of this perspective is the assumption that complexity arises from the interaction of individuals (Phelan, 2001). The behavior of these individuals, also called agents, is dictated by their schemata. According to Anderson, a schema is “a cognitive structure that determines what action the agent takes at time t, given its perception of the environment ” (Anderson, 1999). An agent’s schema can evolve over time what allows it to adapt to its environment. From a modeling perspective, this adaptation can be achieved by the use of feedback and learning algorithms (Phelan, 2001). In agentbased modeling schemata are mostly modeled as sets of simple generative rules. The above mention of structure and feedback already points at the use of system dynamics for modeling an agent’s schema. To evaluate the feasibility of this approach, the agentbased and the system dynamics modeling techniques have to be compared. Some of the basic differences between the two approaches are summarized in Table 1. Principle System Dynamics AgentBased Modeling Building block Feedback loop connecting behavioral variables Individual agents connected by feedback loop Object of interest Structure of the system Agents ’ rules Research approach Development of object of interest over time Deductive: infer from structure to behavior Structure is fixed Inductive: infer from individual agents ’ behavior to system behavior Agents ’ rules can be adaptive
Applying a FuzzyMorphological approach to Complexity within management decisionmaking
"... Purpose: Noting the scarcity of complexity techniques applied to modelling social systems, this paper attempts to formulate a conceptual model of decisionmaking behaviour within the Information Systems Evaluation (ISE) task, against the backdrop of complexity theory. Methodology/Approach: Complexit ..."
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Purpose: Noting the scarcity of complexity techniques applied to modelling social systems, this paper attempts to formulate a conceptual model of decisionmaking behaviour within the Information Systems Evaluation (ISE) task, against the backdrop of complexity theory. Methodology/Approach: Complexity Theory places an emphasis on addressing how dynamic nonlinear systems can be represented and modelled utilising computational tools and techniques to draw out inherent system dynamics. In doing so, the use of Fuzzy Cognitive Mapping (FCM) and Morphological Analysis (MA) (hence a FuzzyMorphological approach), is applied to empirical case study data, to elucidate the inherent behavioural and systems issues involved in ISE decisionmaking within a British manufacturing organisation. Findings: The paper presents results of applying a combined Fuzzy Cognitive Mapping and Morphological Analysis approach to modelling complexity within management decisionmaking in the ISE task: both in terms of a cognitive map of the key decision criteria; a matrix of constraint criteria; and a synthesised model that provides an indication of the linkages between technology management factors and organisational imperatives and goals. These findings show the usefulness of viewing the topic in complexity science terms (emergent behaviour, nonlinearity and chaotic response).