Supervisors are essentially in charge of one thing, and that's process lifecycle management; starting up processes, restarting them when they die, and bailing out if something goes wrong.
I have an algorithm for an absolute occupancy grid that can handle multicore parallelism. Basically, the grid is subdivided into smaller parts that just do their thing independently, but if they detect that a move takes a player out of their boundaries, they queue it up locally for the global grid. Once all of the subgrid threads are done, they wait while the global grid does its thing. (Aggregating all the local subgrid queues, then processing those moves requiring global coordination.)
I don't see how I can do that in Erlang. The closest thing I'm aware of (and I know almost nothing about Erlang) is that moves are are done optimistically, then collisions can be detected after the fact and rolled back. Maybe that's what I'll need to do: port to Erlang and use rollbacks.
Supervisors are essentially in charge of one thing, and that's process lifecycle management
I thought they could do more than just that, maybe.
Imagine how you'd implement your system if each part of the system had to communicate with the others over a network socket, and you're most of the way to implementing it in Erlang.
It's probably not optimal to do things involving lots of calculations in Erlang, though, as the VM is fairly slow. Akka implements a similar system, but for the JVM (Scala).
Basically, you are proposing that the world actor aggregate the entire world's data? That wouldn't scale. Or, maybe the world actor just becomes responsible for adjudicating moves between subgrids. Maybe.
Couldn't you have X players moving on a global grid? I have built systems with literally tens of millions of players (Erlang processes) moving on a grid (and doing way more, localized threat detection, decision making, moving away or towards, and coordination with other units in X range).
One complication is that my grid is for a procedurally generated world with 2^120 locations in it. This is why I generate subgrids. A degenerate case is one subgrid per user. However, these subgrids are organized in load-balanced groups, each of which has their own thread pool, caches, and locks.
Also, rollbacks are problematic, though the real problems are arguably corner cases.
Erlang might be a win because each garbage collector only has to deal with its own local memory.
EDIT: It turns out my algorithm is somewhat similar to Pikko:
http://www.erlang-factory.com/upload/presentations/297/Pikko...
One big difference, is that my algorithm doesn't move or reconfigure masts, instead it dynamically creates subgrids, which are then grouped into "workgroups" each of which is supposed to be processed by a different CPU socket. Instead of there being an API, it's more that the subgrids stop what they are doing and their information is briefly managed by the global grid code. (The procedurally generated map is rigged, so that there are many opportunities for crossing from one subgrid to another.)
Our units actually would report to intermediaries their maximum interaction boundries, which would then be passed to processes to create something somewhat like a mast, someone like a subgrid -- a dynamic interaction zone. All our units had hard constraints (max speed, etc) and worked in global ticks that represented real time. Then, we would talk to global to stretch all the interaction zones to fill empty space and report back boundaries. Then, our units would work in little worlds until they crossed a threshold, we caused a rezoning among them and their neighbors. So initially, the everything would have to be parsed out into interaction zones, but then they could ignore each other for periods of times until a unit strayed across an edge, and then rezoning took place.
Not sure how well it would work with amped up movement (making it have to go all the way up to global more) and not certain how it would work at the scale of 1 undecillion 329 decillion 227 nonillion 995 octillion 784 septillion 915 sextillion 872 quintillion 903 quadrillion 807 trillion 60 billion 280 million 344 thousand 576 points!
I think I can make my subgrids algorithm work, but it will have to be using mutable data structures.