I’ve been thinking that there’s a nice generalisable analogy between bayesian updating and forecasting. (It is quite no shit when you think about it but it feels like people aren’t exploiting it?)
I’m doing a project on simulating a version of this idea but in a way that utilizes democratic decision making called Predictive Liquid Democracy (PLD) and I would love to hear if you have any thoughts on the general setup. It is model parameterization but within a specific democratic framing.
PLD is basically saying the following:
What if we could set up a trust based meritocratic voting network based on the predictions about how well a candidate will perform? It is futarchy with some twists.
Now for the generalised framing in terms of graphs that I’m thinking of:
As an example, if we look at a research network we can say that they’re trying to optimise for a certain set of outcomes (citations, new research) and they’re trying to make predictions that are going to work. P(U|A)
From a system perspective it is hard to influence the nodes even though it is possible. We therfore say that the edges of the graph that is the research network is what we’ll optimise. We can then set up a graph that has the signals and graph connections optimised to reach the truth.
Since we don’t care about the nodes we can also use AIs to help in a combination with human experts.
I’m writing a paper on setting up the variational mathematics behind this right now. I’m also writing a paper on some more specific simulations of this to run so I’m very grateful for any thoughts you might have of this setup!
This is very nice!
I’ve been thinking that there’s a nice generalisable analogy between bayesian updating and forecasting. (It is quite no shit when you think about it but it feels like people aren’t exploiting it?)
I’m doing a project on simulating a version of this idea but in a way that utilizes democratic decision making called Predictive Liquid Democracy (PLD) and I would love to hear if you have any thoughts on the general setup. It is model parameterization but within a specific democratic framing.
PLD is basically saying the following:
What if we could set up a trust based meritocratic voting network based on the predictions about how well a candidate will perform? It is futarchy with some twists.
Now for the generalised framing in terms of graphs that I’m thinking of:
As an example, if we look at a research network we can say that they’re trying to optimise for a certain set of outcomes (citations, new research) and they’re trying to make predictions that are going to work. P(U|A)
From a system perspective it is hard to influence the nodes even though it is possible. We therfore say that the edges of the graph that is the research network is what we’ll optimise. We can then set up a graph that has the signals and graph connections optimised to reach the truth.
Since we don’t care about the nodes we can also use AIs to help in a combination with human experts.
I’m writing a paper on setting up the variational mathematics behind this right now. I’m also writing a paper on some more specific simulations of this to run so I’m very grateful for any thoughts you might have of this setup!