Python prototype simulating multi-agent consensus enforcement using SE44 drift checks in real-time symbolic mesh scenarios
import random
import time
from statistics import mean, stdev
# SE44 validation thresholds
C_MIN = 0.985
S_MAX = 0.01
RMS_DRIFT_MAX = 0.001
# Simulate an agent’s symbolic emission
def generate_emission(agent_id, prev_omega):
state = random.uniform(0.4, 0.6)
bias = random.uniform(0.1, 0.3)
alpha = random.uniform(1.1, 1.3)
omega = (state + bias) * alpha
# Simulate entropy, coherence, and drift
entropy = round(random.gauss(0.005, 0.002), 5)
coherence = round(random.gauss(0.995, 0.003), 5)
rms_drift = abs(omega - prev_omega) if prev_omega else 0
return {
"agent": agent_id,
"omega": omega,
"coherence": coherence,
"entropy": entropy,
"rms_drift": rms_drift
}
# SE44 Gate
def validate_emission(e):
return (
e["coherence"] >= C_MIN and
e["entropy"] <= S_MAX and
e["rms_drift"] <= RMS_DRIFT_MAX
)
# Simulate consensus among agents
def consensus_phase(agents, prev_omega):
emissions = [generate_emission(agent, prev_omega) for agent in agents]
valid = [e for e in emissions if validate_emission(e)]
if len(valid) >= len(agents) // 2 + 1:
consensus_omega = mean([e["omega"] for e in valid])
print(f"\n✅ Consensus reached. Valid emissions: {len(valid)}/{len(agents)}")
print(f"Ω_consensus ≈ {round(consensus_omega, 5)}\n")
return consensus_omega
else:
print(f"\n❌ Consensus failed. Only {len(valid)}/{len(agents)} passed SE44.\n")
return prev_omega
# Main simulation loop
def run_mesh_simulation(agents, ticks=10):
omega = None
for t in range(ticks):
print(f"🕒 Tick {t+1}")
omega = consensus_phase(agents, omega)
time.sleep(1)
# Initialize
agents = [f"Agent_{i}" for i in range(1, 10)]
run_mesh_simulation(agents)
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