Ahuraic Framework Reproducible Case Studies: Two Network-Based Illustrations
Year: 2026
Type: article
Abstract: This release (v2.1.2) provides the data references, code, documentation, and supporting materials associated with the reproducibility package for the ICSAC manuscript "The Ahuraic Framework as a Proposed Tripartite Architecture for Organization, Change, and Stability" (Mahdi Jalali & Sediqeh Jalali), covering Informational Structural Cost (CIS), D-CIS, and the Capacity–Attractor Principle (CAP). Package contents: Python 3.13.5 scripts (NetworkX 3.6.1, NumPy 2.3.5, pandas 2.2.3, scikit-learn 1.8.0) and associated configuration files, parameter tables, and derived outputs for two network illustrations: (i) Zachary's karate club, computing a candidate CIS expression along a Girvan–Newman edge-deletion trajectory; and (ii) Bitcoin OTC, computing partition-level descriptors R_W and R_C. CAP and the full D-CIS balance decomposition are discussed in the manuscript but are not operationalized in this package. Version 2.1.2 primarily updates package metadata, citation information, and documentation. The computational materials are carried forward from version 2.1.1; this release does not represent a new end-to-end computational rerun of all analyses from raw inputs. Reproducibility limitation: The raw Bitcoin OTC input dataset is not included in this package. The documentation specifies the data source and expected SHA-256 checksum. Reproducing the relevant analysis from the raw input therefore requires obtaining the dataset separately and verifying its checksum. The package documents the computational procedures and reported results. The inclusion of scripts, checksums, and expected outputs should not be interpreted as independent validation of the theoretical framework or as evidence that every analysis has been independently reproduced from raw data in this release. This package is intended to support inspection, reuse, and reproducibility assessment of the associated research materials.
Authors: jalali, mahdi, jalali, sediqeh, jalali, pooya
Institution: Payame Noor University
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204662981