ertosns 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr %!s(int64=3) %!d(string=hai) anos
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.ipynb_checkpoints 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging %!s(int64=3) %!d(string=hai) anos
blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added %!s(int64=3) %!d(string=hai) anos
core 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr %!s(int64=3) %!d(string=hai) anos
img 35405831e3 [research/lotterysim] simulate transaction fee, and tipless mechanism, with controlled pid %!s(int64=3) %!d(string=hai) anos
pid a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning %!s(int64=3) %!d(string=hai) anos
reports fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts %!s(int64=3) %!d(string=hai) anos
search_space dee4a65777 [research/lotterysim] merge controllers %!s(int64=3) %!d(string=hai) anos
.gitignore 6318c2bd76 script/research/lotterysim: .gitignore added %!s(int64=3) %!d(string=hai) anos
README.md 03a2aac436 lotterysim: create config.py. %!s(int64=3) %!d(string=hai) anos
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added %!s(int64=3) %!d(string=hai) anos
acc_vs_staked_ratio.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts %!s(int64=3) %!d(string=hai) anos
acc_vs_staked_ratio_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts %!s(int64=3) %!d(string=hai) anos
acc_vs_staked_ratio_pi_headstart.py d7f477e4f9 [research/lotterysim] headstart, remove airdrop %!s(int64=3) %!d(string=hai) anos
basefee_discrete_autocrawler_pi.py a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning %!s(int64=3) %!d(string=hai) anos
config.py 03a2aac436 lotterysim: create config.py. %!s(int64=3) %!d(string=hai) anos
discrete_instance.py 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr %!s(int64=3) %!d(string=hai) anos
discrete_instance_pi_headstart.py 635c326c17 [research/lotterysim] fix drop in accuracy after 0mint distribution ends; starting with 0 premint at genesis/pre-genesis %!s(int64=3) %!d(string=hai) anos
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging %!s(int64=3) %!d(string=hai) anos
example.csv ede647e1d5 lotterysim: add example.csv %!s(int64=3) %!d(string=hai) anos
metrics.py 3aaf7e466c [research/lotterysim] handle 0 premint divin by zero case] %!s(int64=3) %!d(string=hai) anos
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging %!s(int64=3) %!d(string=hai) anos
plot_darkies.py 6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr %!s(int64=3) %!d(string=hai) anos
pool_advantage.py 2e2aff3919 [reesarch/lotterysim] pool zero advantage demonestration %!s(int64=3) %!d(string=hai) anos
primary_discrete_auto_crawler.py f0a4095833 [research/lotterysim] simulate timelocked airdrop, enhance log %!s(int64=3) %!d(string=hai) anos
primary_discrete_auto_crawler_pi.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr %!s(int64=3) %!d(string=hai) anos
secondary_discrete_auto_crawler.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr %!s(int64=3) %!d(string=hai) anos
secondary_discrete_auto_crawler_pi.py 2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr %!s(int64=3) %!d(string=hai) anos
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr %!s(int64=3) %!d(string=hai) anos
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr %!s(int64=3) %!d(string=hai) anos
vesting.py d60d72d088 [research/lotterysim] update vesting darkie id %!s(int64=3) %!d(string=hai) anos

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