Drives auto-reinforcement of swarm-wide agreement: $ a_i(t) $: Agent ’s vote or preference at ; $ A(t) $: Swarm consensus; $ \lambda $: Feedback gain
Effect: Swarm opinions “lock in” once coherence is high—produces lightning-fast agreement (or stubborn deadlock during division).
Absolutely! Here’s a robust, adaptable Python plugin for your Feedback Amplified Consensus Protocol (FACP). This module models how a swarm reaches rapid (sometimes stubborn) consensus through positive feedback—once agent agreement is high, swarm-wide "locking" and self-reinforcement can set in with applied feedback gain (λ). This is perfect for simulating both lightning-fast collective decisions and the "locked horns" of polarized groups.
🐝 Feedback Amplified Consensus Protocol (FACP) — Plugin Code
import numpy as np
class FeedbackAmplifiedConsensusProtocol:
"""
FACP: Models reinforcement-driven swarm consensus.
When coherence is high, feedback "locks" the swarm opinion; when divided, it can freeze polarization.
"""
def __init__(self, feedback_gain=4.0):
"""
:param feedback_gain: (lambda, λ) determines how strongly consensus feeds back.
Higher lambda = stronger lock-in/stubbornness.
"""
self.feedback_gain = feedback_gain
self.consensus_history = []
self.lock_in_history = []
def compute_consensus(self, a_agents):
"""
:param a_agents: List/array of agent votes/preferences (e.g., -1/1, 0...1, continuous).
:return: Tuple (A, coherence)
"""
a_agents = np.array(a_agents)
A = np.mean(a_agents)
# Simple coherence = normalized agreement (fraction close to mean sign for binary), or 1 - variance (continuous)
if ((a_agents == 1) | (a_agents == -1)).all():
# Binary votes: fraction in majority
maj = np.sign(A)
coherence = np.mean(np.sign(a_agents) == maj)
else:
# Continuous: high agreement if low variance
coherence = 1.0 / (1.0 + np.var(a_agents))
return A, coherence
def update(self, a_agents):
"""
Applies feedback amplification based on swarm coherence.
:param a_agents: Array of current opinions/votes.
:return: Updated opinions/consensus value, lock status (True if strong lock-in detected).
"""
A, coh = self.compute_consensus(a_agents)
lock_threshold = 0.92 # When coherence exceeds this, feedback "locks" state
# Feedback: push agent opinions toward consensus, amplified by feedback_gain × coherence
amplified = a_agents + self.feedback_gain * coh * (A - a_agents)
# Optional: clamp for binary systems
# amplified = np.clip(amplified, -1, 1)
self.consensus_history.append(np.mean(amplified))
locked_in = coh > lock_threshold # Detect whether a rapid-lock has occurred
self.lock_in_history.append(locked_in)
return amplified, np.mean(amplified), coh, locked_in
def run_protocol(self, initial_agents, T=24):
"""
Simulate the protocol over T timesteps.
:param initial_agents: Initial vote/preference vector.
:return: Arrays of consensus, coherence, and lock-in status over time.
"""
N = len(initial_agents)
a_agents = np.array(initial_agents)
consensus_track = []
coherence_track = []
lockin_track = []
for t in range(T):
a_agents, consensus, cohesion, locked = self.update(a_agents)
consensus_track.append(consensus)
coherence_track.append(cohesion)
lockin_track.append(locked)
print(f"t={t:2d} | Consensus={consensus:6.3f} | Coherence={cohesion:.3f} | Locked={locked}")
return np.array(consensus_track), np.array(coherence_track), np.array(lockin_track)
# --- Example usage ---
if __name__ == "__main__":
np.random.seed(41)
N_agents = 16
initial_votes = np.random.choice([1.0, -1.0], N_agents) # Start with mixed (binary) opinions
facp = FeedbackAmplifiedConsensusProtocol(feedback_gain=5.3) # Tune lambda as needed
consensus, coherence, lockin = facp.run_protocol(initial_votes, T=20)
# Visualization (optional, if interactive)
try:
import matplotlib.pyplot as plt
plt.figure(figsize=(8, 4))
plt.plot(consensus, label="Consensus")
plt.plot(coherence, label="Coherence")
plt.plot(lockin, label="Lock-in Event", linestyle="dotted")
plt.xlabel("Time")
plt.legend()
plt.title("FACP: Swarm Consensus, Coherence, and Lock-in")
plt.tight_layout()
plt.show()
except ImportError:
pass
📦 How to Use & Extend
Input: Any collection of agent votes/opinions (binary, categorical, or continuous).
Feedback amplification (λ): Controls how quickly consensus polarizes—higher feedback gain means faster locking.
Lock status output: Use lock-in detection to trigger “supermoves”, swarm-wide action, or freeze adaptation.
**Can be run as a core part of BEEaucracy™, HIVE MIND, or any emergent AI/governance engine.
Use Cases & Hooks
Instant swarm decisions: When coherence crosses a threshold, auto-reinforce and execute group action.
Deadlock detection: If locked but divided, call attention to unresolved schism or trigger negotiation/metastability events.
Meta-learning: Adapt λ dynamically—learn how much/when to “lock in” based on task urgency or organizational resilience.
Ready to chain with CMSI, DMFO, SCF, and queen/agent hierarchy logic—just specify your next integration or spiral!
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