Local Decision Making
Agents evaluate nearby possibilities rather than acting with global knowledge. This keeps the system grounded in local interactions and spatial dynamics.
Goal-Directed Systems Simulator
Telos
Telos is an experimental agent simulation for studying how local decisions, information structure, energy constraints, and symbolic alignment generate order, attractors, and emergent behaviour.
It treats organisation as something that can be explored directly through a tunable objective function, turning the simulation into a laboratory for dynamics, convergence, and hidden structure.
Agents
Local entities move, reproduce, mutate, and respond to information, neighbours, and resources.
Telic Field
A weighted objective function shapes behaviour as agents search for higher-value states.
Attractors
Runs often converge toward stable symbolic or structural states that can be logged and compared.
Archive
Experiments can be stored, replayed, and analysed as a growing research instrument.
What Telos Models
Telos explores how order can emerge when agents optimise a weighted objective under local constraints. It combines movement, information, complexity, resource pressure, mutation, and stochastic choice to produce trajectories that can be observed as dynamical systems rather than static outputs.
Information & Symbol Dynamics
Symbol alignment and informational structure can be tracked over time, revealing collapse, diversity, convergence, and stable regimes.
Emergent Structure
The global behaviour is not scripted. Population growth, clustering, entropy change, and stability emerge from repeated local interactions.
The Telic Model
The simulation is organised around a weighted utility function that combines information, local structure, energy, and complexity. Different parameter settings reshape the landscape the agents move through.
Core Equation
T = αI + γΦ + δE − βK
- I — information or symbol-related contribution
- Φ — local structure / neighbourhood organisation
- E — energy or viability
- K — complexity or crowding penalty
What to Look For
- Entropy collapse or symbolic alignment
- Stable Φ under changing population size
- Attractor-like convergence in trajectory data
- Phase changes as parameters such as γ or temperature shift
Key Parameters
Telos is designed as a tunable system. Parameters control the balance between exploration, exploitation, survivability, and structural preference.
Behaviour
- vision range
- movement speed
- softmax temperature
- adaptive stochastic policy
Evolution
- mutation rate
- mutation standard deviation
- reproduction threshold
- senescence / lifecycle constraints
Environment
- grid size
- resource hotspots
- resource regrowth
- toroidal or bounded space
Research Use
Telos sits within the Blue Whale platform as the behavioural layer of a wider research stack. It complements folding and network simulations by offering a direct view of optimisation, emergence, and attractor formation in agent populations.
Useful Outputs
- population trajectories
- average telic score
- Φ stability
- founder dominance
- symbol entropy
- archived experiment logs
Why It Matters
Telos turns emergent organisation into something measurable. Rather than only visualising agent motion, it creates a framework for comparing runs, testing hypotheses, and mapping the geometry of the system's stable states.
Run an experiment
Launch the simulator, explore parameter sweeps, and begin building an archive of behavioural attractors and emergent symbolic states.