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City Research Online City, University of London Institutional Repository Citation: ElBahrawy, A. Y. (2020). Quantifying and modelling online decentralised systems: a complex systems approach. (Unpublished Doctoral thesis, City, University of London) This is the accepted version of the paper. This version of the publication may differ from the final published version. Permanent repository link: https://openaccess.city.ac.uk/id/eprint/24023/ Link to published version: Copyright: City Research Online aims to make research outputs of City, University of London available to a wider audience. Copyright and Moral Rights remain with the author(s) and/or copyright holders. URLs from City Research Online may be freely distributed and linked to. Reuse: Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. City Research Online: http://openaccess.city.ac.uk/ [email protected] CITY,UNIVERSITY OF LONDON Quantifying and Modelling Online Decentralised Systems: A Complex Systems Approach Author: Abeer ElBahrawy Supervisors: First supervisor: Dr. Andrea Baronchelli Second supervisor: Dr. Mark Broom Examiners: Dr. Lucas Lacasa and Prof. Tobias Preis A doctoral dissertation submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics School of Mathematics, Computer Science and Engineering January 11, 2020 i CITY, UNIVERSITY OF LONDON Abstract Quantifying and Modelling Online Decentralised Systems: A Complex Systems Approach Cryptocurrencies are unique examples of decentralised socioeconomic systems. All the transactions, trading, and development are traceable and publicly available. Bitcoin, the first cryptocurrency, was introduced in 2009 launching a market of more than 2500 cryptocurrencies and has a value of more than 200 billion dollars. In comparison to the rising importance of cryptocurrencies in the financial world, the research on cryptocurrencies is still limited. In this thesis, we analyse three novel datasets namely, cryptocurrencies’ market data, cryptocurrencies’ Wikipedia page views and edits, and illicit transactions on Bitcoin. We study the cryptocurrencies ecosystem, including the market dynamics, the social attention and the transaction network. We find that the ecological neutral model can capture the market dynamics, hinting at the extent to which technological differences between cryptocurrencies are considered in investment decisions. We also investigate the relationship between information production and consumption and cryptocurrency market dynamics. We find that a small community of tightly connected editors is responsible for most of the production of information about cryptocurrencies in Wikipedia. Finally, we assess dark markets’ Bitcoin transactions showing the ability of the markets to adapt to multiple closures, including law enforcement raids. We expect that our contribution will be of interest to researchers working on either cryptocurrencies or complex systems. We anticipate that adopting a complex systems approach, will spark more research that interweaves both the technological and socioeconomic aspect of cryptocurrencies. ii Publications This thesis is based on the following publications: I. Abeer ElBahrawy, Laura Alessandretti, Anne Kandler, Romualdo Pastor- Satorras, and Andrea Baronchelli. Evolutionary dynamics of the cryp- tocurrency market. Royal Society Open Science, 4(11), 2017. II. Abeer ElBahrawy, Laura Alessandretti and Andrea Baronchelli. Wikipedia and Cryptocurrencies: Interplay Between Collective Attention and Mar- ket Performance. Frontiers in Blockchain, 2019, doi: 10.3389/fbloc.2019.00012. III. Abeer ElBahrawy, Laura Alessandretti, Leonid Rusnac, Daniel Gold- smith, Alexander Teytelboym, Andrea Baronchelli. Collective Dynam- ics of Dark Web Marketplaces. arXiv preprint arXiv:1911.09536, 2019. Presented at Harvard Big Data 2019. IV. Sam Miller, Abeer ElBahrawy, Martin Dittus, Joss Wright, and Mark Graham. Predicting drug demand with Wikipedia views: Evidence from darknet markets. Accepted in The World Wide Web Conference. V. Laura Alessandretti, Abeer ElBahrawy, Luca Maria Aiello, and An- drea Baronchelli. Anticipating cryptocurrency prices using machine learning. Complexity, 2018. Other publications: VI. Mike Seiferling, Abeer ElBahrawy, Tales Padilha, and Keith Chan. Cryp- tocurrencies and the future of money, 2019. To appear soon. iii Acknowledgements I have worked on this thesis, surrounded by a vibrant and warm community. I am very thankful for: Andrea Baronchelli, who has guided me through my PhD with care and challenged me to work to my full potential. The staff of the Mathematics Department at City, who have welcomed me in a friendly research environment. Among them, a special thank goes to Anne Kandler, Alessandro De Martino, and Mark Broom. My office mates, who shared with me a windowless room and many hard moments. Thanks to Cecilia de Fazio, Lleonard Rubio, Valdo Tatitscheff, Johann Bauer, Patrick Serwene, Abrar Ali, Adam Varga, Hamish Forbes, Julia Cen and Roberta Amato. A special thanks to Laura Alessandretti, for working closely with me, I will remember you with every graph! My colleagues at Chainalysis who welcomed me in a whole new environment for me. Thanks to Philip Gradwell, Leonid Rusnac, Daniel Goldsmith and Kim Grauer. The Turing Institute for the support and allowing me to meet great colleagues and collaborators, Among them Sam Miller and Martin Dittus. My collaborators Romualdo Pastor-Satorras, Alex Teytelboym, and Luca Maria Aiello for the insightful discussions and contribution. My Master’s thesis supervisor Mohammed El-Beltagy for always inspiring and encouraging me. Tobias Preis and Lucas Lacasa, who have kindly accepted to act as examiners for this thesis and my defence. My flatmates; Siobhan, James, and Li who filled our home with good cooking and Catan. Nazla Eid for the non stop “oumy zakry”. Sahar Khaled for never ending CD repeats. Malte Probst, for the roasting and all the laughters. My family, especially my little nieces Aya, Menna and Kinda for filling my life with joy and silliness. iv Declaration of Authorship I, Abeer ElBahrawy, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged below. I attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party’s copyright or other Intellectual Property Right, or contain any confidential material. I confirm that this thesis has not been previously submitted for the award of a degree by this or any other university. The copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author. Signed: Date: v Contents 1 Introduction1 2 Background7 2.1 Bitcoin, the first cryptocurrency..................8 2.1.1 From the white paper to reality.............. 11 2.1.2 Dark markets and illicit activities............. 14 2.2 Bitcoin is not alone......................... 16 2.3 Cryptocurrency market....................... 19 2.4 Data sources............................. 25 3 Data collection and preparation 27 3.1 Market data............................. 28 3.2 Wikipedia data........................... 31 3.2.1 Cryptocurrencies page................... 31 3.2.2 Drug pages......................... 35 3.3 Dark markets data......................... 36 3.3.1 Clustering and identification techniques......... 38 3.3.2 Our dataset......................... 41 3.3.3 Drug sales on dark markets................ 49 4 Evolutionary dynamics of the cryptocurrency market 53 4.1 Materials and methods....................... 54 4.2 Market description......................... 55 4.3 Decreasing Bitcoin market share................. 56 4.4 Stability of the cryptocurrency market.............. 57 4.5 A simple model for the cryptocurrency ecology......... 60 4.6 After the publication........................ 64 4.7 Conclusion and discussion..................... 67 vi 5 Wikipedia and cryptocurrencies: interplay between collective at- tention and market performance 70 5.1 Materials and methods....................... 71 5.2 Wikipedia pages and market properties............. 73 5.3 Evolution of cryptocurrency pages................ 75 5.4 Role of editors............................ 78 5.5 An investment strategy based on Wikipedia attention..... 83 5.6 Conclusion and discussion..................... 89 6 Coordinating in the dark: the rise and fall of Bitcoin’s market- places 93 6.1 Materials and methods....................... 94 6.2 Markets resilience.......................... 94 6.3 Users migration........................... 95 6.4 Who is migrating?.......................... 97 6.5 Coordination in the dark...................... 99 6.6 Conclusion and discussion..................... 102 7 Predicting dark markets’ drug demand using Wikipedia views 103 7.1 Relevant literature......................... 104 7.2 Materials and methods....................... 105 7.3 Results................................ 106 7.3.1 Pooled model........................ 106 7.3.2 Modelling each drug separately............. 109 7.3.3 Modelling
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