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blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 лет назад
core ba80aa439c lotterysim: add inflation rate analytics 3 лет назад
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log 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 лет назад
pid 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 лет назад
reports fe1c82bec0 [research/lotterysim/reports] update cascade report with simulation params, change title 3 лет назад
search_space dee4a65777 [research/lotterysim] merge controllers 3 лет назад
README.md 03a2aac436 lotterysim: create config.py. 3 лет назад
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 лет назад
acc_vs_staked_ratio.py 6916f7c4c1 [research/lotterysim/reports] finalize report 3 лет назад
config.py 03a2aac436 lotterysim: create config.py. 3 лет назад
discrete_instance.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 лет назад
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 лет назад
example.csv ede647e1d5 lotterysim: add example.csv 3 лет назад
metrics.py ba80aa439c lotterysim: add inflation rate analytics 3 лет назад
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 лет назад
primary_discrete_auto_crawler.py 6916f7c4c1 [research/lotterysim/reports] finalize report 3 лет назад
secondary_discrete_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 лет назад
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 лет назад
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr 3 лет назад
vesting.py ba80aa439c lotterysim: add inflation rate analytics 3 лет назад

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