ABSTRACT
Decision Network as introduced in this chapter aims to provide a tool for planning and making multiple, linked decisions. The potential applications of Decision Network are not limited to urban planning. It can be a useful planning tool if the decision maker is faced with more than one decision. I have depicted here a conceptual framework with a numerical example of an analytical tool for planning that takes into account contexts, relationships, and sequences of decisions. To illustrate this, I have shown how Decision Network can be applied to solving the inventory control problem by telling a general story of managing UGBs. In reformulating a Decision Network problem for an AI planning tool in this chapter, I showed that there are numerous decision nodes in the city. It is impractical to model all such decision nodes in the Graphplan formulation. However, the planner can identify the scope of the planning problem under consideration. With this scope, the Graphplan formulation becomes a useful planning tool for making multiple, linked decisions. The theoretical framework for urbanization presented in this chapter attempts to apply complexity-based simulation techniques to policy making in the real world. Coupled with a planning analysis tool, Decision Network, the system can help policy makers to predict the future trends of urban development in China or elsewhere in the world and assess various scenarios of national development regarding infrastructure investment, plans for urban and regional development, and national land and space policies.
