 # A quick script for estimating how unpredictable the sequencer is with various SSLE setups

This assumes a setup as follows:

• There is a buffer of size `BUFFER_SIZE`
• Every slot, `TAKE_SIZE` proposers are taken from the front of the buffer, and `TAKE_SIZE` new proposers are added.
• Every slot, `SHUFFLE_SIZE` randomly selected indices in the buffer are reshuffled

The script models the buffer as probability distributions, and computes basic properties of these distributions. For simplicity it focuses on the average probability distribution of the first proposer in each slot, so a probability of 0.038 means that you can identity a validator that has a 3.8% chance of being the first proposer.

One result:

``````5000 rounds completed
Tested shuffling 512 proposers in each slot and taking 65, with a buffer size of 2048
Average probability of most likely proposer: 0.038 (1 in 26.327)
Average entropy: 4.344 nats (equiv to uniform distribution of 77.046 proposers)
``````

The script:

``````import random, math

BUFFER_SIZE = 2048
SHUFFLE_SIZE = 512
TAKE_SIZE = 65
ROUNDS = 5000

# Object representing probability distributions
class ProbabilityDistribution():
def __init__(self, probs):
assert 0.9999999 < sum(probs.values()) < 1.0000001
self.probs = {k:v for k,v in probs.items()}

@classmethod
def average(cls, dists):
out = {}
L = len(dists)
for p in dists:
for k, v in p.probs.items():
out[k] = out.get(k, 0) + v/L
return cls(out)

def __str__(self):
return str({k: int(v*10000)/10000 for k,v in self.probs.items()})

# Randomly select K of N indices
def select_indices(buffer_size, selections):
# If K > N/2 more efficient to select the complement
if selections > buffer_size // 2:
inverse_selections = select_indices(buffer_size, buffer_size - selections)
return set(i for i in range(buffer_size) if i not in inverse_selections)
o = set()
while len(o) < selections:
return o

def simulate_proposer_selection():
# Initialize the buffer with known proposers
proposer_buffer = [ProbabilityDistribution({i:1}) for i in range(BUFFER_SIZE)]
# These variables help us later compute the average max and entropy
max_accumulator = 0
entropy_accumulator = 0
# For each round.....
for r in range(ROUNDS):
# Pick indices to shuffle
shuffle_indices = sorted(select_indices(BUFFER_SIZE, SHUFFLE_SIZE))
# Average over all possible shuffles
avg = ProbabilityDistribution.average([proposer_buffer[index] for index in shuffle_indices])
for index in shuffle_indices:
proposer_buffer[index] = avg
# Probability of most likely proposer
max_prob = max(list(proposer_buffer.probs.values()))
# Entropy of the probability distribution
entropy = sum([-x * math.log(x) for x in proposer_buffer.probs.values()])
max_accumulator += max_prob
entropy_accumulator += entropy
# Remove the desired number of proposers and replace them with new ones
for _ in range(TAKE_SIZE):
proposer_buffer.pop(0)
proposer_buffer.append(ProbabilityDistribution({BUFFER_SIZE+r:1}))
if r % 100 == 99:
print("{} rounds completed".format(r+1))
# Print parameters and results
print("Tested shuffling {} proposers in each slot and taking {}, with a buffer size of {}".format(SHUFFLE_SIZE, TAKE_SIZE, BUFFER_SIZE))
avg_max = max_accumulator / ROUNDS
print("Average probability of most likely proposer: {:.3f} (1 in {:.3f})".format(avg_max, 1/avg_max))
avg_entropy = entropy_accumulator / ROUNDS
print("Average entropy: {:.3f} nats (equiv to uniform distribution of {:.3f} proposers)".format(avg_entropy, math.exp(avg_entropy)))

if __name__ == '__main__':
simulate_proposer_selection()
``````