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

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