skoupidi 8c33d59f40 chore: updated all repo references to codeberg		vor 2 Jahren
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.ipynb_checkpoints	242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging	vor 3 Jahren
blockchain_scripts	bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added	vor 3 Jahren
core	6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr	vor 3 Jahren
img	35405831e3 [research/lotterysim] simulate transaction fee, and tipless mechanism, with controlled pid	vor 3 Jahren
pid	a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning	vor 3 Jahren
reports	8c33d59f40 chore: updated all repo references to codeberg	vor 2 Jahren
search_space	dee4a65777 [research/lotterysim] merge controllers	vor 3 Jahren
.gitignore	6318c2bd76 script/research/lotterysim: .gitignore added	vor 3 Jahren
README.md	8c33d59f40 chore: updated all repo references to codeberg	vor 2 Jahren
__init__.py	bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added	vor 3 Jahren
acc_vs_staked_ratio.py	fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts	vor 3 Jahren
acc_vs_staked_ratio_pi.py	fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts	vor 3 Jahren
acc_vs_staked_ratio_pi_headstart.py	d7f477e4f9 [research/lotterysim] headstart, remove airdrop	vor 3 Jahren
basefee_discrete_autocrawler_pi.py	a7d2776ac4 [research/lotterysim] add base fee controller crawler for tuning	vor 3 Jahren
config.py	03a2aac436 lotterysim: create config.py.	vor 3 Jahren
discrete_instance.py	6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr	vor 3 Jahren
discrete_instance_pi_headstart.py	635c326c17 [research/lotterysim] fix drop in accuracy after 0mint distribution ends; starting with 0 premint at genesis/pre-genesis	vor 3 Jahren
draw.py	242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging	vor 3 Jahren
example.csv	ede647e1d5 lotterysim: add example.csv	vor 3 Jahren
metrics.py	3aaf7e466c [research/lotterysim] handle 0 premint divin by zero case]	vor 3 Jahren
playground.ipynb	242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging	vor 3 Jahren
plot_darkies.py	6e6bf0ba33 [research/lotterysim] fix update vesting, and negative apr	vor 3 Jahren
pool_advantage.py	2e2aff3919 [reesarch/lotterysim] pool zero advantage demonestration	vor 3 Jahren
primary_discrete_auto_crawler.py	f0a4095833 [research/lotterysim] simulate timelocked airdrop, enhance log	vor 3 Jahren
primary_discrete_auto_crawler_pi.py	2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr	vor 3 Jahren
secondary_discrete_auto_crawler.py	2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr	vor 3 Jahren
secondary_discrete_auto_crawler_pi.py	2a0a9d84b8 [research/lotterysim] update crawlers, re-tune controllers for better acc, and stable apr	vor 3 Jahren
secondary_takahashi_auto_crawler.py	2f497332f9 [research/lotterysim] replace apy with apr	vor 3 Jahren
takahashi_instance.py	2f497332f9 [research/lotterysim] replace apy with apr	vor 3 Jahren
vesting.py	d60d72d088 [research/lotterysim] update vesting darkie id	vor 3 Jahren

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.

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

comparing range of target values between

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

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

README.md