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I run some Master's projects on Chip-Firing games, using the Holly Krieger's Numberphile video on the topic as an initial motivation, and going on to prove the main theorem stated there (that you can always win the game if the total amount of money is at least the genus of the curve). This main theorem is an outgrowth of Matthew Baker's work on tropical Riemann-Roch and there are a lot of great sources about it.

However, a current student has gotten interested in a side question that the video claims not much is known about: if you can win the game, what is the minimal number of moves required to do so? I can't seem to find any information about this question at all. In fact, all I can find at all is a very brief mention of this question in a Games for Gardner presentation Matthew Baker gave on the topic (in "Surprise #2").

Are you aware of any resources that discuss this question?

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    $\begingroup$ here is code to find a minimal solution. $\endgroup$ Commented Jun 21, 2021 at 12:40
  • $\begingroup$ This is great, thanks! @CarloBeenakker $\endgroup$ Commented Jun 21, 2021 at 12:57
  • $\begingroup$ What kind of answer are you looking for? It is clearly a function of the initial configuration. So do you want bounds? An efficient algorithm for finding the smallest number of moves? A simple exact answer seems too much to hope for… $\endgroup$ Commented Jun 21, 2021 at 14:41
  • $\begingroup$ @SamHopkins Mostly whether anything had been written at all. One question is: how efficient are the algorithms for this. The code Carlo linked to reduces it to an integer linear programming problem, which is well studied (and so has lots of standard methods that the code can just use), but in general is NP-hard. An obvious question is whether or not there's something special about our situation to make a better algorithm possible $\endgroup$ Commented Jun 21, 2021 at 15:07
  • $\begingroup$ The somewhat related (in fact, in a certain sense "dual") question of how long does it take for a chip configuration to stabilize is well-studied, going all the way back to the original papers of Björner, Lovasz, and Shor. See for instance "Polynomial Bound for a Chip Firing Game on Graphs" by Tardos (doi.org/10.1137/0401039) and "No polynomial bound for the chip firing game on directed graphs" by Eriksson (doi.org/10.1090/S0002-9939-1991-1065944-3). $\endgroup$ Commented Jun 21, 2021 at 15:12

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Not a direct answer, but too long for a comment, I think.

A first step in thinking about this question is: what are some procedures which always either: 1) solve the dollar game, or 2) show the dollar game is not solvable for the given configuration.

For convenience, assume we are working with a (connected) undirected graph; things get more complicated with directed graphs.

In the Riemann-Roch context, Baker and Norine developed the following algorithm for this problem. First, choose an arbitrary "sink vertex" $q$. The sink vertex serves as the "bank" or "government" and goes heavily into debt, firing many many times to give all its neighbors a lot of chips, and then the neighbors spread their excess chips to their neighbors, and so on, and it's easy to see that (since the graph is connected) by making $q$ very negative we can make everyone else positive. Then, we try to get $q$ itself out of debt, and the way we do this is to "superstabilize" the non-sink vertices: this means we repeatedly look for nonempty subsets of the non-sink vertices that have more chips than edges leaving the subset, and we fire such subsets as long as we can. If this superstabilization process leaves $q$ with a nonnegative number of chips, then we win; otherwise the game was not winnable. Note that checking whether a configuration (on the non-sink vertices) is superstable a priori involves exponentially many checks (we have to consider all subsets), but in fact Dhar's burning algorithm gives a linear time algorithm for checking superstability.

So that's one way to win the dollar game, but actually via the "dual" process I hinted about in the comments above, the original Björner, Lovasz, and Shor paper gives a completely different way for winning the dollar game. Namely, repeatedly have vertices with a negative number of chips borrow a chip from all their neighbors. If you don't want to allow borrowing moves, then note that a vertex borrowing from all its neighbors is the same as all the other vertices firing. If the game is winnable, then having repeatedly vertices with a negative number of chips borrow a chip from all their neighbors will win the game. If the game is not winnable, then eventually every vertex will try to borrow in this way. So we know to stop, and that the game is not winnable, if every vertex tries to borrow.

So, we have two pretty different strategies for always winning the dollar game. Of course, there is no guarantee that either of these strategies is always the 'fastest' one for winning. But giving (upper) bounds for the time taken by these strategies evidently gives bounds for the best strategy. And as mentioned in comments above, at least for the 2nd strategy many things are known with respect to time bounds (e.g. there is a polynomial bound).

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  • $\begingroup$ I now see that some of this is repeating stuff explained in the article of Baker you linked to. Still, as I mentioned it is a starting point... $\endgroup$ Commented Jun 21, 2021 at 19:23
  • $\begingroup$ No worries -- this duality wasn't something I was aware of and you explained it usefully! We mostly used Corry and Perkinson's text people.reed.edu/~davidp/divisors_and_sandpiles so new about the borrowing algorithm; interesting that the duality means the uniqueness of the winning state produced by algorithm is "the same" as the uniqueness of stable state produced by toppling. $\endgroup$ Commented Jun 22, 2021 at 9:10
  • $\begingroup$ The quickly produced upper bounds will be a good thing to have in mind. But the uniquness limits thing in another way -- the difficulty of figuring out the optimal number of moves is that in general there will be many winning states, and it's figuring out which of these is the "closest" to the starting state that's the hard part. $\endgroup$ Commented Jun 22, 2021 at 9:13

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