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.ipynb_checkpoints 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added il y a 3 ans
core df9dc1f9aa lotterysim: update constants to read example.csv il y a 3 ans
img 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
log 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
pid 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
reports fe1c82bec0 [research/lotterysim/reports] update cascade report with simulation params, change title il y a 3 ans
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README.md 2c282de051 lotterysim: add usage note to README il y a 3 ans
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added il y a 3 ans
acc_vs_staked_ratio.py 6916f7c4c1 [research/lotterysim/reports] finalize report il y a 3 ans
discrete_instance.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
example.csv ede647e1d5 lotterysim: add example.csv il y a 3 ans
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging il y a 3 ans
primary_discrete_auto_crawler.py 6916f7c4c1 [research/lotterysim/reports] finalize report il y a 3 ans
secondary_discrete_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr il y a 3 ans
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr il y a 3 ans
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr il y a 3 ans
vesting.py a26a995eda [research/lotterysim] minor issue in vesting.py il y a 3 ans

README.md


title: darkfi lottery simulation author: ertosns

date: 11/1/2023

simulate darkfi consensus lottery with a discrete controller

discrete pid controller.

control lottery f tunning paramter

$$f[k] = f[k-1] + K_1e[k] + K_2e[k-1] + K_3e[k-2]$$

with $k_1 = k_p + K_i + K_d$, $k_2 = -K_p -2K_d$, $k_3 = K_d$, and e is the error function.

simulation criterion

find $K_p$, $k_i$, $K_d$ for highest accuracy running the simulation on N trials, of random number of nodes, starting with random airdrop (that all sum to total network stake), running for random runing time.

alt text

notice that best parameters are spread out in the search space, picking the highest of which, and running the simulation, running for 600 slots, result in with >36% accuracy

alt text

comparing range of target values between

notice below that both y,T in the pallas field, and simulation have same range.

alt text

conclusion

using discrete controller the lottery accuracy > 33% with randomized number of nodes, and randomized relative stake. can be coupled with khonsu^1 to achieve 100% accuracy and instant finality.

usage

Replace example.csv with local distribution data. Modify the file name in core/constants.py as follows:

VESTING_FILE='example.csv'

Then run the program:

python vesting.py

When prompted, enter the number of slots the simulation should run for. For testing purposes keep the slot time to 1-5k slots.