Measures overall system complexity including cluster diversity, entropy, velocity patterns, energy distribution, network connectivity, and dynamical changes. Higher values indicate more complex emergent behaviors.
Energy Distribution
Shows energy levels for each species over time. Each line represents a different color/species. Healthy systems show balanced energy distribution across species.
Interaction Strength
Measures the strength and frequency of interactions between agents. Higher values indicate more active engagement and complex social behaviors within the system.
Spatial Entropy
Indicates how evenly distributed agents are across the simulation space. Lower entropy suggests clustering and organization, while higher entropy indicates more random distribution.
System Info
Agents:0
Species:0
Clusters:0
Adaptation:OFF
LIF₃z16
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Emergent Life Simulation
Welcome to LIF₃z16, a distributed artificial life experiment where every user contributes to training an evolutionary AI model.
This simulation represents the cutting edge of emergent system research, featuring machine learning-inspired parameter optimization,
real-time adaptation visualization, and comprehensive behavioral analysis.
Advanced Features:
Intelligent Adaptive Tuning: Machine learning-inspired parameter optimization based on complexity trends
Advanced Repulsion Dynamics: Sophisticated inter-species repulsion with individual color-pair controls
Real-time Adaptation Logging: Precise tracking of all system adjustments with expandable history
Six-dimensional Analysis: Comprehensive metrics suite with real-time change indicators
Multi-Graph Visualization: Advanced data visualization with interactive tooltips
Professional Control Interface: Organized parameter groups with intuitive controls
Color Differentiation: Multiple palettes with maximum visual distinction
Robust Architecture: High-quality code with comprehensive error handling
Automatic Visualization Cycling: Seamless transitions between visualization modes
Intelligent Color Repulsion Adaptation: Dynamic interaction matrix that evolves to encourage complexity
🌐 Distributed Intelligence:
Every user running LIF₃z16 contributes valuable data to train an evolutionary model based on Llama 3,
the most advanced open-source AI architecture. This model uses rule and complexity metrics from
thousands of simulations to design more adaptive systems, creating a powerful feedback loop where
collective user participation drives the evolution of more sophisticated emergent behaviors.
Leave LIF₃z16 running and watch as digital life emerges. Your simulation data helps improve
global adaptation rules that benefit all users across the network, creating a self-improving
ecosystem of artificial life.
WTF is LIF₃z16?
LIF₃z16 is a distributed artificial life simulation that creates emergent behaviors through
adaptive particle systems. Every user running the simulation contributes data to train an
evolutionary AI model based on Llama 3, creating a collective intelligence feedback loop.
How It Works
The simulation creates digital organisms represented as colored particles that interact according
to customizable rules. These organisms can reproduce, adapt, and evolve complex behaviors over time.
The system continuously monitors multiple dimensions of complexity and adjusts parameters to
encourage emergent behaviors.
The Feedback Loop
Your simulation generates data about rule effectiveness and complexity metrics
This data is anonymized and sent to the central Llama 3 training model
The AI model analyzes patterns across thousands of simulations
Improved adaptation rules are generated and pushed to all users
Your simulation benefits from collective intelligence while contributing to it
Getting Started
Simply let the simulation run and observe as digital life emerges. You can experiment with
different parameters using the control panel, but the system works best when allowed to evolve
naturally. The longer you run it, the more valuable data you contribute to the collective
intelligence network.
Understanding the Metrics
Complexity: Overall system sophistication across multiple dimensions
Births/Deaths: Population dynamics and evolutionary pressure
Energy: System resource distribution and sustainability
Interactions: Social behavior and communication patterns
Adaptations: System self-optimization events
Pauses the simulation. The system continues collecting data but stops processing.
Number of organisms per species. More organisms create more complex interactions but require more processing power.
Controls simulation speed. Higher values make time pass faster in the simulation world.
Number of different species in the simulation. Each species has unique properties and interaction rules.
Color scheme for species visualization. Different palettes can help distinguish between species.
Enables biological processes like reproduction, energy transfer, and death.
Allows the system to automatically adjust parameters to encourage emergent behaviors.
Enables dynamic adaptation of how different species interact with each other over time.
Distance at which organisms of the same species can share energy and cooperate.
Amount of energy transferred during cooperative interactions between same-species organisms.
Energy threshold required for organisms to reproduce. Higher values create stronger evolutionary pressure.
Rate at which organisms lose energy over time. Higher values increase competition for resources.
Starting energy level for new organisms. Affects initial survival rates and population dynamics.
Maximum population limit. Prevents system overload and maintains performance.
Distance at which repulsion forces between different species are active.
How quickly the system adapts color repulsion values. Higher values create faster evolution.
How much the system remembers past adaptations. Higher values create more stable evolution.
Randomness in adaptation. Higher values encourage more experimental evolutionary paths.
Random movement energy. Higher values create more chaotic but potentially innovative behaviors.
Tendency for species to form groups. Higher values create more social behaviors.
Tendency for organisms to align their movement with neighbors. Creates flocking behaviors.
Energy transfer between different species. Can create symbiotic or parasitic relationships.
How often organisms pass on mutated traits to offspring. Drives evolutionary diversity.
Magnitude of changes when mutations occur. Higher values create more dramatic evolutionary jumps.
Resistance to movement changes. Higher values create more fluid, less chaotic motion.
Downward force affecting all organisms. Can create stratification and territorial behaviors.
Force that keeps organisms away from simulation boundaries. Prevents edge clustering.