Knowledge of beneficial owners of companies is key to monitoring and managing wealth inequality in any country. Here we propose a robust and scalable network-based algorithm to reveal hidden ultimate owners in public ownership data. Our approach is based on the idea of Katz centrality in complex networks and circumvents the problem of cyclic ownership used to obscure effective control through closed chains of intermediaries. When applied to a country-scale directed ownership network with 6 million nodes, the algorithm identifies ultimate holders of every organisation in 2021’s Russia. The distribution of asset ownership in the country follows a power law, indicating strong wealth inequality with Gini index of 0.93. 51.7% of net assets of non-financial companies are ultimately held by the state and state-owned enterprises, 25.0% — by individuals (incl. 3.4% held by Forbes–200-listed individuals), and 11.3% are owned by foreign entities (incl. 5.7% in tax havens).

This paper constructs a legal text generation and assembly system in the domain of international investment law. We rely on a corpus of 1600+ bilateral investment treaties split into 22 600 articles to train a character-level recurrent neural network (char-RNN). Prior work [1] has shown that while char-RNNs can produce legally meaningful texts, its output tends to be repetitive. In this contribution, we remedy this shortcoming by proposing a new framework for RNN-based text production. First, we elicit priors at the training stage to give more weight to under-represented treaty practice. Second, we use q-gram distance and GloVe word embeddings [12] as filters imposed on the generated texts to draw them closer to a target document. Third, we develop a validation routine that compares the distribution of pre-defined legal concepts in actual and generated texts. Our results indicate that the RNN produces texts that are not repetitive and convey meaningful legal concepts. We conclude by showcasing a practical application of our framework by predicting provisions of the USA-China bilateral investment treaty currently under negotiation.

Negotiating international investment agreements is costly, complex, and prone to power asymmetries. Would it then not make sense to let computers do part of the work? In this contribution, we train a character-level recurrent neural network (RNN) to write international investment agreements. Benefitting from the formulaic nature of treaty language, the RNN generates texts of lawyer-like quality on the article-level, but fails to compose treaties in a legally sensible manner. By embedding RNNs in a user-controlled pipeline we overcome this problem. First, users can specify the treaty content categories ex ante on which the RNN is trained. Second, the pipeline allows a filtering of output ex post by identifying output that corresponds most closely to a user-selected treaty design benchmark. The result is an improved system that produces meaningful texts with legally sensible composition. We test the pipeline by comparing predicted treaties to actually concluded ones and by verifying that our filter captures latent policy preferences by predicting the outcome of current investment treaty negotiations between China and the United States.

In this paper, we present an on-going research project whose aim is to develop a new database of international investment agreements that complements existing endeavors. In particular, this paper describes our efforts to build a standardized corpus of multi-lingual and multi-format agreement texts in order to enable researchers in the fields of international law and economics systematically investigate investment treaties.

Close to 3000 bilateral investment agreements (BITs) have been concluded by 2015 and virtually every country is a signatory. What makes these treaties special is their enforcement mechanism: private investors can sue states directly before international arbitration potentially winning multi-million dollar awards. Given its size and atomized nature, however, practitioners struggle to effectively navigate the BIT universe. To reduce investment law's complexity, this demo introduces a new interactive web-based tool that relies on state-of-the-art technology to allow users to asses similarities and differences between agreements quickly and intuitively. The tool thereby assists investment law practitioners in structuring negotiations around a common denominator in treaty practice or helps litigators to advance their client's case by distinguishing or analogizing treaties.