I’m glad to see this proposal, thank you @FelixLts! The task to come up with the stake distribution mechanism is indeed very relevant and quite ambitious 
I’m very curious in how you will approach the issue of taking into account relevant parameters in the construction of such a mechanism (especially, modules’ fee structures). Also, I think that the list of parameters for modules does not look quite complete yet.
The way I see it, the mechanism should provide a stake distribution that will (at any point in time) drive the Lido validator set to a state that is closest to some “target state” that has been defined for it. Following this goal statement, the first things to determine are:
- How the parameters of this “target state” are set:
- who sets them? (DAO? Ethereum ecosystem/community? DAO-approved algorithm?)
- in what form are they set? (verbally described state? in the form of constraints? in the form of specific parameters’ values?)
- whether this target state is static or dynamic (e.g., if this target state can change depending on the current state of the entire validator set on Ethereum)
- What parameters of the modules should be taken into account in order to estimate how the current state of the validator set corresponds to the target state (e.g., we need to know about each module its performance, number of operators, current % of stake, +1000 other parameters)
At first glance it seems that one of the possible options of the distribution mechanism can be based on a kind of a multi-objective optimization task.
Again, very abstractly speaking, let’s imagine that a set of functions and constraints act as the target state of the validator set:
Functions:
- A function to maximize the diversification of the validator set.
This should be a function that takes into account a lot of other parameters (both dynamic and static) that will have dependencies beyond the Lido validator set. And some of these parameters in general will be almost impossible to estimate objectively and in the moment (such as geographic and jurisdictional distribution, for example) - Reward maximization function, which depends on the performance of each module and its risks and and and…
- Risk minimization function …
Constraints:
- the total DAO commission from all modules must not be below a certain value to cover the costs of maintaining and developing the protocol (taking into account the ETH/USD exchange rate dependency)
- And and and…
With the addition of each next condition, the task becomes more and more complicated
More details on what specific parameters can be taken into account to estimate diversification and efficiency are described here, and also @Mol_Eliza is working on defining what the best validator set is
Is this even remotely similar to how you see the approach to solving this problem, or have I described the wrong thing altogether?
Anyway, I will be very interested in observing the process of creating such a mechanism and happy to help in any way if possible