^ same thing at spaceX with landing rockets.
The applications of dynamic programming outside of econ
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Rust had a whole article on this
https://icare.hse.ru/data/2018/10/24/1142422445/Rust.pdf
Dynamic programming (DP) is an extremely powerful tool for solving a wide class of sequential decision making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision to take in any given situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence, with the comparative paucity of real world applications where DP is actually used to improve decision making. I discuss the literature on numerical solution of DPs and its connection to the literature on reinforcement learning (RL) and artificial intelligence (AI).
Despite amazing, highly publicized successess of these algorithms that result in superhuman levels of performance in board games such as chess or Go, I am not aware of comparably successful applications of DP for helping individuals and firms to solve real-world problems. I point to the fuzziness of many real world decision problems and the difficulty in mathematically formulating and modeling them as key obstacles to wider application of DP to improve decision making. Nevertheless, I provide several success stories where DP has demonstrably improved decision making and discuss a number of other examples where it seems likely that the application of DP could have significant value. I conclude that “applied DP” offers substantial promise for economic policy making if economists can let go of the empirically untenable assumption of unbounded rationality and try to tackle the challenging decision problems faced every day by individuals and firms.What a load of bull. Robotics/mechatronics use optimal control and dynamic programming all the time to make those cool robots work as they do. All those Boston Dynamics robots, drones, etc. rely on feedback mechanisms using these methods to perform complex movements and adjust things on the fly.
I agree and I’m an economist I don’t know what Rust was on when he write that he’s normally on top of things.
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Rust had a whole article on this
https://icare.hse.ru/data/2018/10/24/1142422445/Rust.pdf
Dynamic programming (DP) is an extremely powerful tool for solving a wide class of sequential decision making problems under uncertainty. In principle, it enables us to compute optimal decision rules that specify the best possible decision to take in any given situation. This article reviews developments in DP and contrasts its revolutionary impact on economics, operations research, engineering, and artificial intelligence, with the comparative paucity of real world applications where DP is actually used to improve decision making. I discuss the literature on numerical solution of DPs and its connection to the literature on reinforcement learning (RL) and artificial intelligence (AI).
Despite amazing, highly publicized successess of these algorithms that result in superhuman levels of performance in board games such as chess or Go, I am not aware of comparably successful applications of DP for helping individuals and firms to solve real-world problems. I point to the fuzziness of many real world decision problems and the difficulty in mathematically formulating and modeling them as key obstacles to wider application of DP to improve decision making. Nevertheless, I provide several success stories where DP has demonstrably improved decision making and discuss a number of other examples where it seems likely that the application of DP could have significant value. I conclude that “applied DP” offers substantial promise for economic policy making if economists can let go of the empirically untenable assumption of unbounded rationality and try to tackle the challenging decision problems faced every day by individuals and firms.What a load of bull. Robotics/mechatronics use optimal control and dynamic programming all the time to make those cool robots work as they do. All those Boston Dynamics robots, drones, etc. rely on feedback mechanisms using these methods to perform complex movements and adjust things on the fly.
Your evidence is not evidence that the quote is bull. In fact, it is entirely in line with it.
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DP is often a part of solving a combinatorial optimization problem. It's hard to see how well they can be applied to economics (in the sense they're practical), but they are useful for stuff like traffic control, circuit board design and network planning.
Asset Pricing
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Column-generation routines for solving pricing problems frequently employ DP algorithms.
E.g., solving a cutting stock problem requires solving a pricing problem that is the knapsack problem that is solvable in pseudopolynomial time using DP
solving a vehicle routing problem requires solving a pricing problem that is frequently the constrained shortest path problem on a graph that needs a labelling-based DP algorithm.
-- OR Bro.
You had me up to "knapsack problem".