This research introduces the Hierarchical Emergence Framework (HEF) to explain universal convergence across complex systems in machine learning, biology, and physics. HEF models emergence as a phase transition, leading to a unique minimum-cost mechanism and proving convergence towards a fixed-point.
BEHAVE is a novel hybrid AI framework designed for real-time modeling of collective human dynamics. It treats groups of interacting humans as complex dynamical systems, addressing limitations of existing AI that fail to capture emergent collective behaviors like escalation or breakdown.
This paper introduces an intuitive and tractable "Active Data" approach, treating data as atomic, interactive objects, to reason over large and complex datasets. It posits that this bottom-up method enhances designs facing computational and conceptual complexity, exemplified by an implementation in air traffic flow management.
The article challenges the assumption that correct individual system parts guarantee correct system behavior, especially in software design, testing, and operation. It highlights that a system can be considered "healthy" based on valid responses and satisfied constraints, yet still produce unintended outcomes.
GraphStream is a tool designed to bridge the gap between complex systems and dynamic graphs, providing functionalities for analyzing and visualizing these data structures.