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pid 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 3 years ago
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search_space dee4a65777 [research/lotterysim] merge controllers 3 years ago
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discrete_instance.py 242060eac2 [research/lotterysim] update vesting, fix vesting apr, enhance logging 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
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primary_discrete_auto_crawler.py 6916f7c4c1 [research/lotterysim/reports] finalize report 3 years ago
secondary_discrete_auto_crawler.py 2f497332f9 [research/lotterysim] replace apy with apr 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