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RE: A brief note on community building and the role of government interference.

in #steemit8 years ago (edited)

If stakeholders want to protect their stake, and they think the payouts are too large, one way to fix this might be to get the witnesses directly involved using parameters. Witnesses could set the size of the reward pool. Then we can get some price discovery going.

If the stakeholders know that the reward pool is half the size today compared to yesterday, wouldn't they be half as likely to flag "overpaid" posts? Granted, any given author would also know this, and maybe he'll put off posting his big article until the reward pool goes back up. That's the whole point of price discovery: "Should I allocate this resource here, now?"

Last night on SteemSpeak, @noisy told us about an idea he had: one day a week, the max payout is set to something like $10. I thought it was a pretty neat idea. It would be interesting to see how posting habits changed if something like that was implemented. Would people just post twice as much because they have a good chance of getting $10 (each post), since all of the people who routinely get on trending would avoid that day?

I like @noisy's idea, but I don't think it should be once a week. I think it should be another witness parameter. If all of the witnesses decide on an exact day and an exact max reward, only then will it happen. Then there'll be a 1 week cool-down. Then they can try it again.

I wonder what bots would do in that situation? It adds a bit of uncertainty that would be difficult for bots to predict until a few of these "max-payout" days have gone by.

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Fascinating ideas in this post. I love what @noisy proposed. If bots don't know the potential outcome then bots become useless. Seems this particular solution solves the trigger happy flagging and pushes bot to the brink of extinction.

I think the bots will know better than most human users.

It's true that bots would know with certainty that the witnesses have set these parameters, they just won't be able to predict the behaviour of the authors and stakeholders under those parameters.