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The Distributed Cooperative Modeling System (DCMS) solves complex decisionproblems involving a lot of participants with different viewpoints by networkbased distributed modeling and multi-template aggregation.  This thesis aims at extending the system with support for dynamic decisionmaking process. First, the thesis presents a discussion of characteristics andoptimal policy finding Markov Decision Process as well as a brief introductionto dynamic Bayesian decision network, which is inherently equal to MDP. Afterthat, discussion and implementation of prediction in Markov process for bothdiscrete and continuous random variable are given, as well as several differentkinds of correlation analysis among multiple indices which could helpdecision-makers to realize the interaction of indices and design appropriatepolicy.  Appending history data of Macau industry, as the foundation of extendingDCMS, is introduced. Additional works include rearrangement of graphical classhierarchy in DCMS, which in turn allows convenient implementation of curverelation-line, which makes template modeling clearer and friendlier.

Dynamic Decision Process Modeling and Relation-line Handling in Distributed Cooperative Modeling System