Abstract
Dynamic decision models are frameworks for modeling and solving decision problems that take into explicit account the effects of time. These formalisms are based on structural and semantical extensions of conventional decision models, e.g., decision trees and influence diagrams, with the mathematical definitions of finite-state semi-Markov processes. This paper identifies the common theoretical basis of existing dynamic decision modeling formalisms, and compares and contrasts their applicability and efficiency. It also argues that a subclass of such dynamic decision problems can be formulated and solved more effectively with non-graphical techniques. Some insights gained from this exercise on automating the dynamic decision making process are summarized.
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Selected References
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