Many investment decisions involve multiple decision stages that have termination options. Information is revealed over time, and the ability to modify behaviour leads to a decision “tree”. Once uncertain outcomes and a changing environment over time are factored in, solving these types of problems becomes very complex. Stochastic Dynamic Programming provides a solution method that will solve these problems.
These models can be used in applications such as mining or oil exploration where testing can reveal better information at some cost.
PRODUCT STUDY
A problem with real-world complexity is likely to involve some or all of the following aspects:
Stochastic Dynamic Programming is a very efficient method for solving large decision trees in a short period of time. At each combination of decision nexus and possible future state the SDP solver tells us the optimal decision to make.
Our custom-written solver creates the optimal solution for this type of solution in a very short period of time. As well as the optimal “roadmap” of decisions, outputs include such things as expected return, and expected cash flow.