Maier, Frank H., "Integration of Knowledge-Based Systems and System Dynamics Models for Decision Support in Innovation Management", 1994

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Traditional models of innovations diffusion ignore the complexity and dynamics underlying the process of diffusion. Usually these models consider only a single management decisions variable, e.g., price of advertising, but they assume these variables to be exogenous elements. The models seek for strategies to optimize the cumulative profits without consideration of the highly independent influencing elements. Their aim is normative decision support, but they use models, which do not appropriately represent the structural fundamentals of the problem. The use of the system dynamics methodology allows the development of more complex and detailed models to investigate the process of innovations diffusion. These models can enhance the insight in the problem structure and increase understanding of the complexity, the dynamics and the impact of management decisions. But the development of adequate system dynamics models requires expert knowledge and plenty of time. The paper presents a knowledge-based approach to System Dynamics Modeling to shorten the process model building and to make the knowledge about innovation management available for many users. It consists of two parts: A knowledge based system with the traditional components for dialog, explanation, knowledge representation and inference is used to configure System Dynamics Models, and a modelbase of different modules that represent the generic structures of typical innovation management problems. The knowledge about the innovation problems is included in the different modules and in the knowledge base of the knowledge-based system. In dialog with the user the knowledge based system analyses the problem structure, chooses the relevant modules and finally configures the model that then can be used for further analysis through the problem owner.

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  • 1994
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System Dynamic Society Records

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