About this Research Topic
In recent years, a growing body of research understands the interaction of law and society as a complex adaptive system. There has been growth in social, political and economic complexity which has in turn manifested in legal complexity. In support of this view, in extant academic literature, scholars have leveraged techniques and tools from statistical physics, complexity and computational social science to both characterize and predict the behavior of various legal institutions.
In part to confront the scale and complexity of the law, the commercial sphere has seen significant growth in the advent of Legal Tech and use of applied disciplines such as processing engineering and design.
In sum, how interactions between individuals are shaped by norms, and what are the emerging ("collective") phenomena in the highly interconnected "legal" landscape - interpreted in the broadest sense - constitute the core questions that this Research Topic will address.
This Research Topic aims to collect scientific contributions from scholars working at the interface between law and complexity science. We also welcome contributions from colleagues interested in the scientific challenges posed by the use of technology as a tool to tame the complex web of interactions between individuals and norms in the "legal" domain. From the question of how complex network theory applies to items of legislation to the use of agent-based models for democratic and representative systems, the spectrum of real-life issues around how normed societies work is very broad and of paramount interest. The collection of papers in this Research Topic is expected to become a state-of-the-art reference point for scientists interested in the application of robust modeling and data analysis to all aspects of the legal and political world. It will also offer a specialized and thematic venue for computational social scientists and political scientists with a quantitative background, and is expected to play a pivotal role in fostering cross-fertilization between fields - statistical physics, computational social science, politics and legal studies - that have traditionally followed rather separate trajectories.
We welcome Original Research and Reviews where complexity science and quantitative approaches are employed to advance knowledge on the following (non-exhaustive) list of topics:
- Complexity of legal texts.
- Voting systems, including modeling and robust data analysis.
- Dynamics of democratic systems, including agent-based modeling and collective behavior of normed societies.
- Network-theoretical analysis of contracts, items of legislation, and normative systems.
- Topics at the interface between law and probabilistic reasoning.
- Quantitative analysis of LawTech and FinTech ecosystems, including emergence and development of new technologies (e.g. smart contracts).
- Machine-learning approaches to data mining from legal and political texts.
- Effect of regulations on financial markets (dark trading, liquidity and competition, traders' behavior, ...).
- Impact of digitalization and automation on society (e.g. legal services) as 'complexity-reduction' engines.
- Algorithmic decision-making and human-machine assisted decision making.
- Political controversy and information spreading on social networks.
- Science in the Courtroom: juries, trials, and quantitative aspects of the administration of justice.
Prof. Daniel Katz was affiliated with the startup LexPredict which is now part of Elevate Services. Prof. John Ruhl is affiliated with the startup Skopos Labs. Dr. Pierpaolo Vivo declares no competing interests with regard to the Research Topic subject.
Keywords: legal systems, complexity science, LawTech, statistical physics, legal complexity
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.