ertosns 3ee043cdbc [research/lotterysim] fix a bug with APR period during headstart phase 3 years ago
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.ipynb_checkpoints 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 years ago
blockchain_scripts bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 years ago
core 3ee043cdbc [research/lotterysim] fix a bug with APR period during headstart phase 3 years ago
img b3a868999d [research/lotterysim] plot from ./log initial stake, apr for each node in plot_darkies.py 3 years ago
pid 26809109ce script/research/lotterysim/pid/pid.md: further fixed total tokens calculation 3 years ago
reports fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
search_space dee4a65777 [research/lotterysim] merge controllers 3 years ago
.gitignore 6318c2bd76 script/research/lotterysim: .gitignore added 3 years ago
README.md 03a2aac436 lotterysim: create config.py. 3 years ago
__init__.py bd78bdaa45 [research/lotterysim] reorg lotterysim, acc_staked_ratio plot added 3 years ago
acc_vs_staked_ratio.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
acc_vs_staked_ratio_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
acc_vs_staked_ratio_pi_headstart.py d7f477e4f9 [research/lotterysim] headstart, remove airdrop 3 years ago
config.py 03a2aac436 lotterysim: create config.py. 3 years ago
discrete_instance.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
discrete_instance_pi_headstart.py 3ee043cdbc [research/lotterysim] fix a bug with APR period during headstart phase 3 years ago
draw.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 years ago
example.csv ede647e1d5 lotterysim: add example.csv 3 years ago
metrics.py ba80aa439c lotterysim: add inflation rate analytics 3 years ago
playground.ipynb 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 years ago
plot_darkies.py 6e7f7367dd [research/lotterysim] premint coexist zero-coin; although headstart unactivated, set to zero 3 years ago
pool_advantage.py 2e2aff3919 [reesarch/lotterysim] pool zero advantage demonestration 3 years ago
primary_discrete_auto_crawler.py f0a4095833 [research/lotterysim] simulate timelocked airdrop, enhance log 3 years ago
primary_discrete_auto_crawler_pi.py 576940e3e5 [research/lotterysim] plot apr as well as initial stake from log, before/after end of airdrop, fix apy index 3 years ago
secondary_discrete_auto_crawler.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
secondary_discrete_auto_crawler_pi.py fc012bb2c4 [research/lotterysim] update cascade report with pi vs pid, and pi scripts 3 years ago
secondary_takahashi_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 3 years ago
takahashi_instance.py 2f497332f9 [research/lotterysim] replace apy with apr 3 years ago
vesting.py ba80aa439c lotterysim: add inflation rate analytics 3 years ago

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