The Unlucky Investor – Chapter 4: Methodology and Settings (Biomagnification Contamination Model)
不運な投資家 – 第4章: 方法論と設定（生物濃縮汚染モデル） (AI 翻訳)
Gandolfi, Philipp
🤖 gxceed AI 要約
日本語
この章では、上流汚染が投資家のポートフォリオに影響を与える生物濃縮汚染モデルの方法論を詳細に説明する。モンテカルロシミュレーションと金融フードウェブネットワークを用いた再現性の高いモデリング手法を提供する。
English
This chapter details the methodology of the Biomagnification Contamination Model, which analyzes how upstream pollution contaminates investment portfolios using Monte Carlo simulations and financial food-web networks, with a strong emphasis on reproducibility.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のGX文脈と直接の関連は薄いが、サプライチェーンを通じた汚染の伝播モデルは、日本企業のScope3算定やデューデリジェンスに応用可能性がある。
In the global GX context
This work introduces a novel methodology for modeling pollution propagation in financial supply chains, which could inform due diligence and risk assessment under emerging disclosure frameworks such as ISSB and CSRD.
👥 読者別の含意
🔬研究者:This paper provides a fully reproducible methodology for modeling supply chain contamination, useful for researchers in sustainable finance and environmental accounting.
📄 Abstract（原文）
his deposit contains the complete reproducible materials for Chapter 4: Methodology and Settings of the thesis: “The Unlucky Investor: How Even “Clean-Shot” Investments Are Contaminated by Upstream Pollution – A Natural-Science Approach to Financial Decision-Making in Opaque Markets” by Philipp Gandolfi (May 2026). Contents Full polished text of Chapter 4 (sections 4.1–4.7) Mixed-methods research design and operationalisation of the Biomagnification Contamination Model Detailed hardware, software, and containerised environment specifications (Docker 27.1 + Singularity 3.11) Data sources, preprocessing pipelines, and financial food-web network construction Monte-Carlo simulation design (10,000 runs per scenario), parameter calibration, random-seed strategy, and full reproducibility protocols These materials form the methodological foundation for the empirical analysis in Chapters 5 and 6. All Monte-Carlo simulations, sensitivity analyses, and figures presented in the thesis can be exactly reproduced using the accompanying codebase. Links GitHub repository: https://github.com/philippgandolfi/unlucky-investor-thesis Zenodo DOI for this record: (assigned automatically upon publication) Reproducibility declaration: All empirical results, figures, and tables in this thesis that depend on Chapter 4 can be regenerated exactly from the publicly released codebase, containerised environment, provided data, and fixed random seeds.
🔗 Provenance — このレコードを発見したソース
- Zenodo https://zenodo.org/records/20100539first seen 2026-05-14 21:29:56 · last seen 2026-05-14 21:39:02
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