Great question. Short answer - I dont know.
Long-ish answer: This is what governance should be focused on. Drawing up verifiable and measurable success metrics. Creating props from the community to the validators and to the team. Not the other way around (unless it upgrades, of course).
I have theories. We can measure user cost before and after. We can measure mau/wau/dau before and after. We can measure and try to find similarities in it all: price relation, user relation, social activity, dev activity, etc.
We can go more fundamental, I guess, and propose success rate as something more abstract. i.e., chain A suggests lowering inflation by 5% due to the theory that the prices will rise in the next 365 days. Has that happened? Yes, success here is restricted to each case. But…
I thought this was what governance groups could be working on. Right now, there is (nearly) 0 analytics involved in such things, beyond pure subjective interest and “look, but ada/polygon, etc got away with it.”
I dont know the answer to this. We are trying to approach the question from a validator angle and the chain angle in validatorinfo.com, So far, one thing that is defo verifiable and cross chain is the fact that each prop can be described in USD value that is voted for it. USD values can easily be compared. This kinda gives me an overview of which props were more important than others as life events. Which leads me to know who participates in keychain events. This, compared later on to % of voting power used, gives even more of an interesting picture. Right now, we are on the stage of trying to figure out which accounts can sway decisions.
Anyway, this is just an angle. I’m trying to say that if we can do this on our budget and resources, a network should be able to learn to measure and work with this for sure.