Thanks @mic for the great question. This is a concern I had as well, but it turns out to be far more of a theoretical than an empirical or practical concern. A nice way to assess this is to evaluate the distribution of gradients for each user across the 100 rounds of dropout. Because in each round of dropout ~50% of staked users have their stakes zeroed out, for each user there are ~50 gradients taken with their stake set to 0 and ~50 taken at their full stake. If we compare these two distributions of gradients using a t-test and find their difference to be statistically insignificant then the effect of stake on the TC estimate isn’t much of a concern. I did this analysis on with the largest staker, user stocks_ai_g, and found that indeed it was the case that the difference between the two distributions was statistically insignificant. It looks like there could be a significant difference with extremely large stakes, i.e. 5%+ of the total staked, but no one is even close to that so it really doesn’t matter. Furthermore, the optimal distribution of stakes is a moving target as the market evolves, i.e. what is optimal one week may not be next week, which makes it even less of a concern. And to encourage originality, it has to work such that increased stakes on similar signals yield less and less payout, otherwise it would have the same problems as CORR. But it is something we’ll keep an eye on, just in case!