ertosns f20c88b2f1 [research/lotterysim] emulate slashing as random process with certain low probability 3 年 前
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.ipynb_checkpoints 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 年 前
blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 年 前
core f20c88b2f1 [research/lotterysim] emulate slashing as random process with certain low probability 3 年 前
img b3a868999d [research/lotterysim] plot from ./log initial stake, apr for each node in plot_darkies.py 3 年 前
pid 26809109ce script/research/lotterysim/pid/pid.md: further fixed total tokens calculation 3 年 前
reports fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
search_space dee4a65777 [research/lotterysim] merge controllers 3 年 前
.gitignore 6318c2bd76 script/research/lotterysim: .gitignore added 3 年 前
README.md 03a2aac436 lotterysim: create config.py. 3 年 前
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 年 前
acc_vs_staked_ratio.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
acc_vs_staked_ratio_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
acc_vs_staked_ratio_pi_headstart.py d7f477e4f9 [research/lotterysim] headstart, remove airdrop 3 年 前
config.py 03a2aac436 lotterysim: create config.py. 3 年 前
discrete_instance.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
discrete_instance_pi_headstart.py f20c88b2f1 [research/lotterysim] emulate slashing as random process with certain low probability 3 年 前
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 年 前
example.csv ede647e1d5 lotterysim: add example.csv 3 年 前
metrics.py ba80aa439c lotterysim: add inflation rate analytics 3 年 前
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 年 前
plot_darkies.py e9e2de886d [research/lotterysim] update Sigma (total stake) with primary reward with headstart 3 年 前
pool_advantage.py 2e2aff3919 [reesarch/lotterysim] pool zero advantage demonestration 3 年 前
primary_discrete_auto_crawler.py 5f44e59bb1 [research/lotterysim] bug fixed with cascade control, round primary feedback precision to .2f 3 年 前
primary_discrete_auto_crawler_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
secondary_discrete_auto_crawler.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
secondary_discrete_auto_crawler_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 年 前
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 年 前
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr 3 年 前
vesting.py ba80aa439c lotterysim: add inflation rate analytics 3 年 前

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