It feels like making "Where the tokens land was checked against the table published in Scientific American in 1996 (Illinois Avenue: 3.18% published, 3.19% in my simulation)." a key point of the investigation doesn't see like a human choice
Meta, but one rule I was surprised to learn: if someone doesn't buy a property when they land on it, it is immediately auctioned off by the bank.
> I seem to remember a similar study from years ago saying the optimal strategy was to always buy if you can.
The optimal rule is a little different: don't sit on cash until a housing crisis.
Just like in a real-life business, sitting on cash that could be working for you is a bad idea.
It really works when playing with kids, but I noticed that the consequence of it in most games is that the player who goes first usually wins.
Not a bad model it seems, curious to test against a « I never want to die strategy », where everyone saves enough to keep playing, which is what mostly happens in real life too.
In the end, it aligns with YC and the startup model to try the all in for a project that will make you win disproportionately.
Has anyone done something similar to that with Monopoly? (by contrast, the technique used in this video is much more basic; it comes up with some specific rules a player will use, then has those bots play eachother)
One: it would offer no human assumptions.
Two: more importantly it should and could include the trading part. (the op's video had some, but it felt really really limited from what I gathered)
It is really rare to see trading to be implemented. Yeah, it is the hard part but in competetive monopoly, it is big part.
I am yet to figure both input/output spaces for it. As soon as I figure them, I probably will do proper zero-style ai for many games.
Any other exchange becomes pointless as you won't be completing a color anyways.