skoupidi 8c33d59f40 chore: updated all repo references to codeberg 2 년 전
..
.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 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr 3 년 전
img 35405831e3 [research/lotterysim] simulate transaction fee, and tipless mechanism, with controlled pid 3 년 전
pid a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning 3 년 전
reports 8c33d59f40 chore: updated all repo references to codeberg 2 년 전
search_space dee4a65777 [research/lotterysim] merge controllers 3 년 전
.gitignore 6318c2bd76 script/research/lotterysim: .gitignore added 3 년 전
README.md 8c33d59f40 chore: updated all repo references to codeberg 2 년 전
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 년 전
acc_vs_staked_ratio.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 년 전
acc_vs_staked_ratio_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 년 전
acc_vs_staked_ratio_pi_headstart.py d7f477e4f9 [research/lotterysim] headstart, remove airdrop 3 년 전
basefee_discrete_autocrawler_pi.py a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning 3 년 전
config.py 03a2aac436 lotterysim: create config.py. 3 년 전
discrete_instance.py 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr 3 년 전
discrete_instance_pi_headstart.py 635c326c17 [research/lotterysim] fix drop in accuracy after 0mint distribution ends; starting with 0 premint at genesis/pre-genesis 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 3aaf7e466c [research/lotterysim] handle 0 premint divin by zero case] 3 년 전
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 년 전
plot_darkies.py 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr 3 년 전
pool_advantage.py 2e2aff3919 [reesarch/lotterysim] pool zero advantage demonestration 3 년 전
primary_discrete_auto_crawler.py f0a4095833 [research/lotterysim] simulate timelocked airdrop, enhance log 3 년 전
primary_discrete_auto_crawler_pi.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr 3 년 전
secondary_discrete_auto_crawler.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr 3 년 전
secondary_discrete_auto_crawler_pi.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable 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 d60d72d088 [research/lotterysim] update vesting darkie id 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