science Documentation

Scientific & Philosophical Concepts

Reference documentation for the ODE-based biological and cognitive layers of the simulation. This engine attempts to model the emergence of self-consciousness through the tension between biological imperative and social recognition.

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1. Metabolism & Homeostasis

layer_id: bio_01

The metabolic layer is governed by a system of ordinary differential equations (ODEs) simulating energy consumption relative to movement and basal existence. In the Hegelian sense, this represents the "Life" stage where the organism distinguishes itself from the environment through consumption.

Governing Equation function

dE/dt = -B - α|v| + βI(t)

Where E is energy, B is basal rate, v is velocity, and I is intake.

Parameter Constraints

Param	Range	Unit
basal_rate	0.01 - 0.05	J/s
move_cost	0.1 - 0.5	J/m
death_thresh	0.0	J

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2. CTRNN Architecture

layer_id: neur_02

Continuous Time Recurrent Neural Networks (CTRNNs) serve as the brain stem. Unlike discrete networks, CTRNNs model the temporal dynamics of neurons, allowing for smooth motor control and short-term memory without explicit recurrence loops in code.

Neuron State Equation

τ_i * dy_i/dt = -y_i + Σ w_ji * σ(y_j + θ_j) + I_i

τ Time constant (decay rate)
w Synaptic weight from j to i
σ Sigmoid activation function

Topology: Fully Connected

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3. Predictive Coding Networks

layer_id: cog_03

The agent minimizes free energy by predicting sensory inputs. Prediction errors are propagated up the hierarchy to update internal models (beliefs). High prediction error drives the organism to act to change the world to fit its model (Active Inference).

Input Layer

Raw sensory data (Distance sensors, Energy level)

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Hidden Layer

Latent variables representing "Self" and "Other"

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Error Units

Computes: Observation - Prediction

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Hegelian Connection

This layer corresponds to the "Concept" stage. The organism is no longer just reacting; it is conceptualizing the external world. The error signal is the "negation" of the subject's internal certainty by objective reality.

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4. Mutual Recognition

layer_id: soc_04

The highest layer of the simulation. Agents must not only survive but achieve a stable state of mutual acknowledgment. This is modeled as a game-theoretic payoff matrix where cooperation yields higher long-term energy stability than pure predation.

State	Interaction	Energy Outcome (∆E)	Recognition Score
Indifference	Agents ignore each other	-0.01 (Basal)	0.0
Conflict	Agent A attacks Agent B	+10.0 / -50.0	-0.5
Recognition	Mutual signaling	+2.0 (Shared)	+1.0

"Self-consciousness exists in and for itself when, and by the fact that, it so exists for another; that is, it exists only in being acknowledged." - G.W.F. Hegel