BEHAVE framework models collective human dynamics in real-time

BEHAVE (Behavioral Engine for Human Activity Vector Estimation), proposed by Helene Malyutina, rethinks human behavior modeling by treating interacting groups as complex dynamical systems. Unlike existing AI that analyzes individuals or detects events after they occur, BEHAVE captures emergent collective dynamics—nonlinearity, feedback loops, phase transitions—in real time. It builds a continuous behavioral field over an interaction space derived from observable physical signals. Kinematic micro-signals (position, velocity, body orientation, gestural activity) are structured into a directed interaction graph, then aggregated into a basis of non-redundant behavioral fields. The framework rests on one theorem and two structural propositions characterizing the tension field, field basis, and criticality index, with neural networks implementing perception and forecasting layers.

In a demonstrated use case on a 7-agent negotiation snapshot, BEHAVE accurately modeled the group's state and potential transitions to escalation or breakdown. Its formal mathematical grounding allows recalibration for diverse contexts: crowd safety (predicting stampedes), crisis-team coordination (detecting breakdown signals), education (monitoring classroom engagement), and clinical settings (tracking group therapy dynamics). By forecasting collective behavior from raw sensor data, BEHAVE opens the door to real-time intervention systems that prevent failures before they happen. The paper is available on arXiv (2605.12730).