For the last part about economic optimization, I would not approach it with Dynamic Programming. As evidenced by go and many games, doing the "local" best move does not guarantee the best result in the end. If brute-forcing is untractable, the state-of-the-art is using Monte-Carlo Tree Search as evidenced by its dominance in board games, Magic the Gathering, Poker, robots competitions, RTS, etc ...
Constraint solving works pretty well in these domains too, and AFAIK, top-level artificial players in board games is a mix of constraint solving and MCTS.
Comments
For the last part about economic optimization, I would not approach it with Dynamic Programming. As evidenced by go and many games, doing the "local" best move does not guarantee the best result in the end. If brute-forcing is untractable, the state-of-the-art is using Monte-Carlo Tree Search as evidenced by its dominance in board games, Magic the Gathering, Poker, robots competitions, RTS, etc ...
Constraint solving works pretty well in these domains too, and AFAIK, top-level artificial players in board games is a mix of constraint solving and MCTS.