보상 풀이 적은 것인지...

in kr •  4 years ago 

현재 블러트 파워가 약 8100여개이고,
포스트에 보팅 해보니
풀 보팅의 가치가 약 0.02 BLURT입니다.
현재 BLURT의 시세가 40원 정도인데,
0.02 BLURT이면 많이 낮은 게 아닌가 보여집니다.
아직 개발과 정비가 진행 중이니
좀 더 지켜봐야죠.

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  ·  4 years ago  ·   (edited)

You are correct. There is an ongoing issue with rewards. Let me tell you about it. Steem has several models for handing out rewards.

The most recent version of Steem (the one we built on) uses what's called the convergent linear reward curve.

Unfortunately, the convergent linear reward curve assumes history, and had no history at launch.

In hard fork one, an adjustment was made to the rewards curve. I think to a value, recent_claims. Which would kind of give it a history, but the adjustment was not large enough.

You may remember that we were supposed to launch two chains on July 4th 2020, blurt and zapata.

As it were, we launched learn got it rolling, and not too long after realized that we had shipped with some rather significant bugs. One of those bugs was the reward pool bug and the other one was the resource credit bug. Both of them were related to the fact that Blurt had no history, it started from zero and so those two systems malfunctioned.

For resource credits, we are studying the issue. My personal point of view is that transaction fees are actually better. I think that transaction fees are better because there is no such thing as a free transaction. It simply does not exist.

There is a cost for storing things on the chain, and in our rushed patch solution, we actually allowed the people who bear that cost, the witnesses to set the fee level. So I am pretty comfortable with the solution for fees / resource credits.

However, for rewards it is still not fully solved. I think you may recall that now, rewards are a bit bigger than they were before. However, they are not as big as they should be.

There are two ways that we can address this, I think. I could be wrong.

  1. move to linear rewards
  2. readjust recent_claims
  ·  4 years ago  ·  

선형보상으로의 이동을 적극 지지합니다.

  ·  4 years ago  ·  

I think no. 1 is better. We may need longer time to apply convergent linear reward curve.

Thank you very much for your description.