I agree. The methods that I am working on are focused on the signal processing concepts where you have a bunch of private preference signals, which are decentralized across participants, potentially weighted by community influence (measured in token holdings ~ think signal strength a la 'how much skin in the game'), those preference are noisy time varying distributed information, and we aim to accomplish a "sensor fusion" task using a discounted integral operator, and a neuron like "trigger function" that emits a discrete event when (and if) enough action potential accumulates. So the main goal was in fact to create an array of private continuous signals into a discrete event without forcibly time boxing it. My work is generally expected to be paired with a bonding curve but is not required to be. The point of the bonding curve and integral operations together are aimed to define or redefine certain kinds of attacks prevalent in time boxed, fixed influence voting, but a lot more analysis is due. I don't have enough to publish concrete finding at this time but i am setting up for a battery of numerical experiments using cadCAD. you can check out my progress here;
[https://github.com/BlockScience/conviction](https://github.com/BlockScience/conviction)
apologies in advance, the documentation is shit; I haven't really prepared it for an outside audience just yet.