This is in response to community feedback that our existing reputation
calculation is too likely to disqualify storage nodes unfairly with
extreme swings up and down.
For details and analysis, please see the data_loss_vs_dq_chance_sim.py
tool, the "tuning reputation further.ipynb" Jupyter notebook in the
storj/datascience repository, and the discussion at
https://forum.storj.io/t/tuning-audit-scoring/14084
In brief: changing the lambda and initial-alpha parameters in this way
causes the swings in reputation to be smaller and less likely to put a
node past the disqualification threshold unfairly.
Note: this change will cause a one-time reset of all (non-disqualified)
node reputations, because the new initial alpha value of 1000 is
dramatically different, and the disqualification threshold is going to
be much higher.
Change-Id: Id6dc4ba8fde1be3db4255b72282207bab5491ca3
0dcc0a9ee0