Neurologistics: Replication Package for an Exploratory Computational Experiment on Anticipatory Ethical Knowledge in AI-Enabled Supply Chains

Some internal file names and variables retain the legacy label “learning”; throughout the manuscript and interpretation documents, these refer only to programmed computational state updating, not human, organisational, or behavioural learning. All outputs are synthetically generated from explicitly defined model assumptions and parameters. The package contains no empirical human, organisational, neurological, employee, community, emissions, or operational field data.

Behaviourally informed variables are abstract computational proxies and should not be interpreted as direct measurements or reproductions of neural processes. The results describe model behaviour and structural stability within the examined design space. They do not establish external validity, causal effects, behavioural realism, predictive accuracy, or organisational effectiveness.

Empirical validation would require behavioural data, stakeholder assessment, and organisational field studies. The deposited materials include: - Python source code and configuration files; - automated reproducibility and verification tests; - data and parameter dictionaries; - assumptions and empirical-calibration warnings; - full 100-seed publication outputs; - generated CSV tables and figures; - configuration and completion reports; - a structural-stability report; - a SHA-256 file manifest.

Recommended software environment: Python 3.10 or later.