Binary Anchor Model (BAM) V2.0

This repository contains the official Python simulation code for the paper:
"The Binary Anchor: Cognition, Symbolic Loops, and Systems Failure" by Norimitsu Sawada.

• Zenodo Preprint (V2.0.0): DOI: 10.5281/zenodo.21759689
• Concept DOI (All Versions): DOI: 10.5281/zenodo.21566423
• License: [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/

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Overview

The Binary Anchor Model (BAM) describes decision-making as a two-layer homeostatic structure:

• System A (Biological Anchor): Rooted in survival, interoception, and the amygdala.
• System B (Social Anchor): Rooted in language, institutions, and the PFC.

Version 2.0 formalizes the Master Equation $A_t = \text{WTA}(V_t) \in {0, 1}$ as the apex organizing principle, systematizes all equations within a Level 0–3 modular argument-space structure, and introduces the probabilistic Soft-WTA extension.

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Key Formalizations

• Master Equation & Soft-WTA: $A_t \sim \text{Bernoulli}(\sigma(V_t - T_E))$
• Hesitation Energy ($H_t$) & Accumulated Load ($I_t$): Dual-trigger mechanisms for computational breakdown (Burst).
• Neurobiological Grounding of $\eta_{A0}$: Amygdala sensitivity and plasticity ($\eta_A(t) = \eta_{A0} \cdot (1 - \phi_t)$).
• Quantification of $T_{\text{recovery}}$ & [R-3] $h_t$ Threshold Modification: Recovery paths from Type 1 Burst and past violence reinforcement dynamics.

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Simulation

Run the BAM simulator (requires numpy, matplotlib, pandas, seaborn):

python bam_simulator.py

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Citation

If you use this model, concepts, or simulation code in your research, please cite:

@techreport{sawada2026binary,
  author      = {Sawada, Norimitsu},
  title       = {The Binary Anchor: Cognition, Symbolic Loops, and Systems Failure},
  institution = {Zenodo},
  year        = {2026},
  month       = {aug},
  version     = {2.0.0},
  doi         = {10.5281/zenodo.21759689},
  url         = {https://doi.org/10.5281/zenodo.21759689}
}

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License

CC BY 4.0