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

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