Complex Systems and models of the International Juridical Order

• Grant number: 24/18736-8
• Support Opportunities: Regular Research Grants
• Start date: April 01, 2025
• End date: March 31, 2028
• Field of knowledge: Interdisciplinary Subjects
• Principal Investigator: Nestor Felipe Caticha Alfonso
• Grantee: Nestor Felipe Caticha Alfonso
• Host Institution: Instituto de Física (IF). Universidade de São Paulo (USP). São Paulo , SP, Brazil
• City of the host institution: São Paulo
• Associated researchers: Ana Paula Tavares Magalhães Tacconi ; Fabiano Lemes Ribeiro ; Juan Pablo Neirotti ; Luis Gabriel Moreno Ocampo ; Marcelo Finger ; Paulo Alberto Nussenzveig ; Pedro Bohomoletz de Abreu Dallari ; Renato Vicente
• Associated scholarship(s): 25/08024-3 - Complex Systems and models of the International Juridical Order, BP.TT; 25/05013-0 - Statistical Mechanics of Societies of Neural Networks and the International Juridical Order, BP.DR; 25/05173-8 - Statistical Mechanics of Societies of Neural Networks, BP.IC

Abstract

The aim in this project is to construct a theoretical framework and a practical tool that might help decisions makersinform their actions and assessment of their risks, in the general area of International Juridical Order. We are interested in using methods from Theoretical Physics and Computer Science to study the world of international relations, which is made up of different agents with interests that can be aligned or conflicting, resulting in highly complex problems. The project has these parts:(1) We analyze problems of current and historical interest, within the area of International Juridical Order, determine the relevant actors and what would be the immediate consequence of their actions.(2)Using techniques from the Statistical Mechanics of disordered systems and computational simulation, construct and study an interacting system, where units are modeled by adaptive neural networks. The aim is to discover the order parameters and the phase diagram, which serves as a map, characterizing the different collective states of the system. (3) Build a set of databases of relevant texts, each representing an agent to train a set of LargeLanguage Models (LLM) agents. (4) Use the system of agents to construct estimates of the order parameters to findtrajectories on the map provided by the phase diagram.The team includes experts in International Law, International Relations, Neural Networksand Statistical Mechanics of Complex Systems. (AU)