Scalable Models for Patterns of Life

Abstract

Patterns of life (POL) are emergent properties of com- plex social systems such as neighborhoods or even cities. Accurately modeling POL is not only an academic pursuit; complex training and analysis efforts rely upon these models. Computational POL models will benefit academic researchers by giving them new tools to generate and validate theories about complex sociocultural patterns. POL presents an interesting computational modeling challenge for research in many AI fields because of its complex, multi-level interaction. POL modeling differs from standard agent-based modeling in that not only individual behaviors, but also their collective emergent patterns, are targets of study. At the same time, the behavior of any single individual is potentially important.