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README.md 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy há 3 anos atrás
README.pdf 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy há 3 anos atrás
RPID.py e44d9f0d67 [research/lotterysim] reward pid há 3 anos atrás
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auto_crawler_takahashi.py f87435f9ff [research/lotterysim] miscel changes há 3 anos atrás
avg_instance.py b6727d8c12 [research/lotterysim] staked_tokens/accuracy curve há 3 anos atrás
constants.py e44d9f0d67 [research/lotterysim] reward pid há 3 anos atrás
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pallas_unittests.csv 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy há 3 anos atrás
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plot_sim_vs_darkfi_distribution.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy há 3 anos atrás
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sigmas_test_samples.py 0b07b3dee6 [research/lotterysim] scaling to 1k,10k,100k,1m nodes with same accuracy há 3 anos atrás
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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.