Very interesting. I looked at stability in learning agents in artificial markets back in the late 90s for my PhD and concluded that at least the systems I worked with weren't stable - they were prone to bubbles and crashes.
Very interesting to see that there is a class of stable systems that force high prices.
Would be interesting to understand if the no swap regret systems studied also give stable results when it is an N player game rather than a 2 player game
I had the same question about N-player settings. My intuition is the more players, the more competition and the more chaotic the dynamics, and the harder it would be for any strategy like they describe to emerge. But intuitions can be wrong.
In any event, it would be interesting to know how the dynamics change with increases in the number of players — I wondered if it might provide some kind of rationale for having a certain number of competitors in a market.
Intuitively, stability might also be easier to achieve here since there is a human check in the loop, oftentimes someone with considerable experience and knowledge of the current market state.
That sounds actually really cool. Do you have a link to any of your papers?