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lotterysim
police d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
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.ipynb_checkpoints f87435f9ff [research/lotterysim] miscel changes 3 anni fa
Makefile 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
README.md 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
README.pdf 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
RPID.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
auto_crawler.py 69c35617f7 [research/lotterysim] sigmoid strategy, and miscel changes 3 anni fa
auto_crawler_takahashi.py 69c35617f7 [research/lotterysim] sigmoid strategy, and miscel changes 3 anni fa
avg_instance.py b6727d8c12 [research/lotterysim] staked_tokens/accuracy curve 3 anni fa
constants.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
crawler.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
darkie.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
draw.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
elbow.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
f.hist d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
f_history_processed.png f87435f9ff [research/lotterysim] miscel changes 3 anni fa
gains.txt 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
heuristics.png 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
highest_gain.txt d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
highest_gain_takahashi.txt f87435f9ff [research/lotterysim] miscel changes 3 anni fa
instance.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
lead_history_processed.png f87435f9ff [research/lotterysim] miscel changes 3 anni fa
leads.hist d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
lottery.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
lottery_dist.png 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
main.py f87435f9ff [research/lotterysim] miscel changes 3 anni fa
one_year_reward.py 131bf0c901 [research/lotterysim] apy 3 anni fa
pallas_unittests.csv 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
pid.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
playground.ipynb f87435f9ff [research/lotterysim] miscel changes 3 anni fa
plot_sim_vs_darkfi_distribution.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
reward_auto_crawler.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
sigmas_test_samples.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
stake.png b6727d8c12 [research/lotterysim] staked_tokens/accuracy curve 3 anni fa
stake_reward.py ca0545ec44 [research/lotterysim] staking strategies 3 anni fa
stake_rewards.txt 131bf0c901 [research/lotterysim] apy 3 anni fa
stats 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
strategy.py d0aa3de958 [research/lotterysim] cascade control of block reward value 3 anni fa
takahashi.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
takahashi_gains.txt 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
takahashi_instance.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy 3 anni fa
utils.py 131bf0c901 [research/lotterysim] apy 3 anni fa

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.