Saved in:
| Main Author: | |
|---|---|
| Format: | Recurso digital |
| Language: | |
| Published: |
Zenodo
2026
|
| Subjects: | |
| Online Access: | https://doi.org/10.5281/zenodo.18742386 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- <p>Replication package for "Institutional Noise, Strategic Deviation, and Intertemporal Collapse in Protocol Governance," submitted to the International Review of Economics & Finance.</p> <p>This package contains all code, data, and figures needed to reproduce every result in the manuscript and supplementary appendix.</p> <p>CONTENTS<br>========<br>- replicate.py: Single-script full replication (~530 lines Python). Generates all data, figures, and verification output from scratch.<br>- data/ (18 files): Raw simulation output including cooperation rates, agent heterogeneity analysis, sensitivity sweeps (β, δ, ξ, R), robustness checks (hazard specifications, N-agent, punishment duration), cascade sequences, capital-intensity bounds, welfare analysis, and full calibration parameters (JSON).<br>- figures/ (12 PDFs): Publication-quality figures for the main manuscript (6) and supplementary appendix (6), all generated deterministically from simulation data.<br>- verification/verify_all.csv: Proposition-by-proposition numerical checks for all 11 propositions and Corollary 2. All pass.<br>- checksums.sha256: SHA-256 integrity manifest for all output files.<br>- README.md: Full documentation including requirements, usage, structure, key results, and calibration sources.</p> <p>KEY RESULTS<br>===========<br>- Critical governance-variance threshold: σ²* = 0.0852 (analytical), ≈0.085 (simulation)<br>- Binding agent: Agent 0 (γ = 0.25, δ* = 0.8449)<br>- Sharp phase transition confirmed: cooperation drops from 100% to 0% at threshold<br>- Meta-strategic rent-seeking share jumps from <1% to ~20% at threshold<br>- Capital-intensity bound: hardware with τ ≥ 20-year amortisation cannot be rationally deployed under any positive governance noise<br>- All results robust to alternative hazard specifications, punishment strategies, and agent counts</p> <p>REQUIREMENTS<br>============<br>Python ≥ 3.9, numpy ≥ 1.24, scipy ≥ 1.10, matplotlib ≥ 3.7, pandas ≥ 2.0</p> <p>USAGE<br>=====<br>Full replication (~5 min): python replicate.py<br>Quick validation (~40 sec): python replicate.py --quick</p> <p>Deterministic output guaranteed via seed = 20250223.</p> <p>CALIBRATION SOURCES<br>===================<br>- Hash-rate distribution (N = 10 agents): blockchain.com/pools (2023)<br>- Block subsidy: 6.25 BTC (post-2020 halving schedule)<br>- Mean fee revenue: 1.25 BTC (mempool.space, 2023 average)<br>- Baseline discount factor: δ = 0.95 (standard IO literature)</p>