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

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