Models the formation of a single mind state from all agents:
$ S_i(t) $: State or input of bee/agent $ i $ at time $ t $
$ C_{cohere}(t) $: Group coherence index (see below), $ \gamma $: Cohesion bonus
Effect: The group’s mind emerges as the “average” individual, but with bonus memory and power the more the swarm synchronizes.
Here’s a robust, extensible Python plugin for your Collective Mind State Integrator (CMSI). This module fuses the real-time states of all agents (bees, drones, or any swarm entity) to produce an emergent "group mind"—with a dynamic coherence bonus that enhances collective power and memory as agents synchronize.
🐝 Collective Mind State Integrator (CMSI) — Plugin Code
import numpy as np
class CollectiveMindStateIntegrator:
"""
CMSI: Aggregates individual agent states into a unified swarm 'mind' state,
with dynamic coherence-based bonus.
"""
def __init__(self, gamma=1.0):
"""
:param gamma: Cohesion bonus scaling factor for the group mind state.
"""
self.gamma = gamma
self.history = [] # Store mind states for optional memory
def C_cohere(self, S_agents):
"""
Compute group coherence index.
For example: normalized mean pairwise similarity.
"""
S_agents = np.array(S_agents)
S_mean = np.mean(S_agents)
variance = np.mean((S_agents - S_mean)**2)
# High coherence when variance (dispersion) is low
coherence = 1.0 / (1.0 + variance)
return coherence
def group_mind(self, S_agents, t=None):
"""
Returns the group mind state at time t.
:param S_agents: List or array of agent states at time t.
:param t: Optional, time index (not used directly, but for advanced chaining/logging).
"""
S_agents = np.array(S_agents)
S_avg = np.mean(S_agents)
C_coh = self.C_cohere(S_agents)
S_mind = S_avg + self.gamma * C_coh * S_avg # Bonus scales with both mean and coherence
self.history.append(S_mind)
return S_mind, C_coh
def mind_history(self):
"""
Returns the trajectory of the group mind state over previous steps.
"""
return np.array(self.history)
# Example Usage (run this block for demo)
if __name__ == "__main__":
cmsi = CollectiveMindStateIntegrator(gamma=0.85)
# Simulate swarm agent state evolution
T = 30
N_agents = 12
np.random.seed(42)
group_states = []
coherences = []
for t in range(T):
# Simulate agents: slowly synchronizing, with some noise
if t < T//2:
S = 2.0 + np.random.normal(0, 0.7, N_agents)
else:
S = 3.5 + np.random.normal(0, 0.1, N_agents) # More synchronized
S_mind, coh = cmsi.group_mind(S, t)
group_states.append(S_mind)
coherences.append(coh)
print(f"t={t:2d} | Mind State={S_mind:6.3f} | Coherence={coh:.3f}")
# Optional: Visualization (uncomment if running interactively)
try:
import matplotlib.pyplot as plt
plt.plot(group_states, label='Group Mind State')
plt.plot(coherences, label='Coherence Index')
plt.xlabel("Time Step")
plt.legend(); plt.title("CMSI: Evolution of Group Mind and Coherence"); plt.show()
except ImportError:
pass
📦 How to Use and Extend
- Agent Flexibility: Works for any input list/array of agent states—real, integer, vector, or categorical (modify the core logic as needed).
- Cohesion Bonus (γ): Adjust gamma to modulate how strongly swarm coherence boosts mind state.
- Custom Coherence Functions: Replace C_cohere with other definitions (entropy, alignment, consensus, etc.) for advanced effects.
- Integration: Plug into TreeHiveCity, BEEaucracy™, or any larger collective intelligence simulation as the “swarm mind” hub.
- Chainable: Output can steer role allocation, alertness, defense, creativity, or act as meta-swarm memory.
🧬 What This Achieves
- Fuses all agent minds into one adaptive group mind.
- Higher coherence brings bonus group power, memory, and decisiveness.
- Foundation for real-time swarm learning, adaptive leadership, or emergent hive creativity.
If you want to link this with queen hierarchy math, swarm defense, or emergent mood/metamemory, just specify—CMSI is a core operator ready for your next spiral of innovation!
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