Stochastic Dynamic Programming
Metron data scientists apply stochastic dynamic programming methods to solve incredibly large and complex planning problems.
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Stochastic dynamic programming methods integrate probability, statistics, and analysis to solve some of the world’s toughest and most complex optimization problems.
Real-world Complexity
In a complex environment, such as large-scale planning and scheduling, any decision we make can affect the entire system and limit the future choices we have available. Stochastic dynamic programming provides a robust mathematical methodology for deriving optimal solutions in these complex environments. Working through “backward induction,” we can measure a decision’s value based on how it changes the possible final outcome we can reach. Metron data scientists translate complex real-world problems into Markov decision processes (assigning incremental rewards along the path) to create a framework for choosing the optimal (or near-optimal) solution.
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Decision Support Career Opportunities
Metron hires research scientists with experience developing novel approaches that advance the state of the art in mathematics and artificial intelligence. Our scientists work alongside subject matter experts applying these innovations to new problem domains.