The attribution of internationally wrongful acts to state in the law of state responsibility
2019
0 views
0 downloads
Advisor: Prof. Dr. Ayşe Nur Tütüncü
Abstract (EN)
Our study is about attribution of internationally wrongful act to state. Rules relating to attribution of acts contrary to international law will become even more important in the 21st century as the international community is increasingly engaged with non state actors like terrorists, private military companies and cyber hackers. The basic question of our study is determined as follows: Under what circumstances will the acts of the private persons be attributable to the State? What are the rules of international law regarding to this question. In the first chapter, The existing rules about attribution of internationally wrongful act to stateis examined. In the second part, attribution rules about private persons is analysed. In the third part, how the current rules are applying military groups, private military companies, terrorist activities and cyber attack activities is studied. Keywords: State Responsibility in International Law, Internationally Wrongful Act of State, Effective Control, Overall Control, Terrorist Activities, Private Military Companies, Cyber Attacks
Author
Dr. Ufuk Dal
How to Cite
Ufuk Dal (Doctorate thesis). The attribution of internationally wrongful acts to state in the law of state responsibility, 2019, İstanbul University.
Keywords
License
Tüm Hakları Saklıdır
This work is shared under the specified license terms.
More theses from İstanbul University
- In the covid 19 pandemic of female employees at a university hospital attitudes and affecting factors in nutrition of 9 months-6 years old children(2022)
- The perception of the right-wing movements in Turkey as to the 27 May Coup: 1960-1980(2020)
- Economic and social life in the Ottoman Empire according to the 1890 year's news of La Turquie Newspaper(2022)
- Land regime in the Umayyads period(2022)
- Merkel hücreli karsinomda tanısal ve prognostik belirteçler(2022)
- Use of machine learning methods in classification of respiratory system diseases(2021)