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Poll
Question: How should Eligius reward miners?
Capped Pay Per Share with Backpay - 15 (12.5%)
Capped Pay Per Share with Equalized Backpay - 17 (14.2%)
Equalized Shared Maximum Pay Per Share - 18 (15%)
Proportional / Pay Per Share (this is NOT straight Prop or PPS) - 12 (10%)
Pay Per Last N Shares; N=Diff*2 (86% CDF) - 12 (10%)
Pay Per Last N Shares; N=Diff*8 (99.9% CDF) - 13 (10.8%)
Pay Per Last Limit-N Shares; N=Diff*2 (86% CDF) - 16 (13.3%)
Pay Per Last Limit-N Shares; N=Diff*8 (99.9% CDF) - 17 (14.2%)
Total Voters: 47

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Author Topic: Eligius: Reward method POLL: 2011 August (TAKE 2)  (Read 1684 times)
Luke-Jr
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August 03, 2011, 08:54:24 PM
 #1

BitcoinTalk forum stupidly disabled the ability to change votes when I added the new option for PPLLNS on the other poll, so we'll have to start over Sad

Please:
  • vote your preference(s) for Eligius's next reward method (you can choose more than one!)
  • only vote if you at least intend to use Eligius after the change (assume your vote is chosen)
  • only vote if you understand the different reward methods, and shortcomings of each method
  • assume PPLNS and PPLLNS apply to both weighed and unweighed shares
There is presently no planned date or ETA for this change: it may happen next week, next month, or never.

Summaries:
See also: Real-world example graphs for most payout methods

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iopq
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August 04, 2011, 02:24:37 AM
 #2

how long does it take you to go through difficulty * 8 shares? that might definitely influence my vote

Luke-Jr
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August 04, 2011, 02:27:44 AM
 #3

how long does it take you to go through difficulty * 8 shares? that might definitely influence my vote
On average, 8 blocks...

Sukrim
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August 04, 2011, 07:46:11 AM
 #4

8 blocks on Eligius - not on the network, just to add.

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GriphZero
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August 04, 2011, 04:17:04 PM
 #5

In the example charts, what is the reason the total payout for Capped PPS with Backpay is so much higher than Pay Per Last N Shares?
Sukrim
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August 04, 2011, 10:13:42 PM
 #6

The data seems to show a bad luck streak. During good luck times (see the beginning for example), "short memory" methods should pay out more than pure PPS even.

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Luke-Jr
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August 04, 2011, 10:44:44 PM
 #7

The data seems to show a bad luck streak. During good luck times (see the beginning for example), "short memory" methods should pay out more than pure PPS even.
Most of the methods pay out PPS when the pool is lucky, so I had to drop blocks from the simulation to get what I considered a useful comparison.

GriphZero
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August 04, 2011, 11:31:33 PM
 #8

True, PPLNS is ahead some of the time durring the first few days.  But even before the bad luck streak, when everything falls below the PPS line, CPPSB is way ahead.

Also, intrestingly, CPPSB somehow earns a lot more than all the other *PPS methods.  They don't seem to be able to stay on the PPS line like CPPSB does.
1bitc0inplz
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August 04, 2011, 11:36:16 PM
 #9

One thing to keep in mind, is that the graph shows exceptionally bad luck over a very short term. To get a true picture of how a payout method effects a user (and a pool) one must consider all possible outcomes.

When we were developing ESMPPS we created a simulator to plot the various possible outcomes of a payout system. It doesn't have a lot of these methods, because some we were not considering. But, it does currently simulate ESMPPS, PPPS, SMPPS and Prop (for a baseline). And, since it's open source, I highly encourage everyone to play with it and (perhaps) implement some of the above methods that are missing.

The URL is: https://github.com/lowentropy/pool_sim

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Sukrim
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August 05, 2011, 01:56:38 AM
 #10

True, PPLNS is ahead some of the time durring the first few days.  But even before the bad luck streak, when everything falls below the PPS line, CPPSB is way ahead.

It would stay way below (at pure PPS value) during good luck times though, like all other *PPS methods. To me it seems as if it has less variance than other methods from that data.

I would really like to see another graph with equally "lucky" data or at least have this one marked with "unlucky simulation"...

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