Thanks @mdo for writing up the true contribution details and @richai for the big picture blog post

I have a question from before, but might have been missed, or I didn’t understand the answer, so I try again.

You write the goal of true contribution is

“to estimate how much a user’s signal improves or detracts from the returns of Numerai’s portfolio”

and true contribution is

“gradient of optimized portfolio returns with respect to the NMR staked”

and

“if a data scientist staked slightly more on their model (thereby increasing their weight in the Stake-Weighted Meta Model), what would the change be to post-optimization portfolio returns?”

So is the gradient the appropriate metric for true contribution goal?

For instance, imagine the user with the best predictions and the perfect stake. Sure, other users predictions can be used to improve, but this particular user shouldn’t increase or decrease their stake because it is already just right.

Do they get a TC of zero? Then as a result, would they be incentivised not to stake on this model?

If so the interests might be misaligned. HF wants `TC=0`

(ie optimal stake on a model) but staker wants to maximise `(TC*stake)`

and so wants `TC>>0`

Or is the users own stake always zeroed out when calculating the TC?

**TLDR**: where am i going wrong if I conclude that TC encourages increased staking on models that will help HF, but discourages continued staking on models already contributing closer to optimal ?