Dynamic Decision Process Modeling and Relation-line Handling in Distributed Cooperative Modeling System
This work incrementally improves a distributed cooperative modeling system for complex multi-participant decision problems, specifically applied to Macau industry data.
This thesis extends the Distributed Cooperative Modeling System (DCMS) to support dynamic decision-making processes by implementing Markov Decision Process (MDP) and dynamic Bayesian decision network approaches, with additional improvements including correlation analysis and graphical interface enhancements for clearer template modeling.
The Distributed Cooperative Modeling System (DCMS) solves complex decision problems involving a lot of participants with different viewpoints by network based distributed modeling and multi-template aggregation. This thesis aims at extending the system with support for dynamic decision making process. First, the thesis presents a discussion of characteristics and optimal policy finding Markov Decision Process as well as a brief introduction to dynamic Bayesian decision network, which is inherently equal to MDP. After that, discussion and implementation of prediction in Markov process for both discrete and continuous random variable are given, as well as several different kinds of correlation analysis among multiple indices which could help decision-makers to realize the interaction of indices and design appropriate policy. Appending history data of Macau industry, as the foundation of extending DCMS, is introduced. Additional works include rearrangement of graphical class hierarchy in DCMS, which in turn allows convenient implementation of curve relation-line, which makes template modeling clearer and friendlier.