ertosns 7b1bb391be [research/lotterysim] edit cascade report 3 년 전
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.ipynb_checkpoints 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 년 전
core 7b1bb391be [research/lotterysim] edit cascade report 3 년 전
img 7b1bb391be [research/lotterysim] edit cascade report 3 년 전
log 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
pid 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
reports 7b1bb391be [research/lotterysim] edit cascade report 3 년 전
search_space dee4a65777 [research/lotterysim] merge controllers 3 년 전
README.md 03a2aac436 lotterysim: create config.py. 3 년 전
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 년 전
acc_vs_staked_ratio.py 7b1bb391be [research/lotterysim] edit cascade report 3 년 전
config.py 03a2aac436 lotterysim: create config.py. 3 년 전
discrete_instance.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
example.csv ede647e1d5 lotterysim: add example.csv 3 년 전
metrics.py ba80aa439c lotterysim: add inflation rate analytics 3 년 전
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
primary_discrete_auto_crawler.py 6916f7c4c1 [research/lotterysim/reports] finalize report 3 년 전
secondary_discrete_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 년 전
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 년 전
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr 3 년 전
vesting.py ba80aa439c lotterysim: add inflation rate analytics 3 년 전

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. Edit config.py as follows:

vesting_file = 'your_local_data.csv'

Edit config.py to define the exchange rate and simulation running time, measured in slots.

Then run the program:

python vesting.py