  This paper studies the feasibility of synthetic data generation for
mission-critical applications. The emphasis is on synthetic data generation for
anomalous detection in complex social networks. In particular, the development
of a heuristic generative model, capable of creating data for anomalous rare
activities in complex social networks is sought. To this end, lessons from
social and political literature are applied to prototype a novel implementation
of the Agent-based Modeling (ABM) framework, based on simple social
interactions between agents, for synthetic data generation in the context of
terrorist profile desegregation. The conclusion offers directions for further
verification, fine-tuning, and proposes future directions of work for the ABM
prototype, as a complex-societal approach to synthetic data generation, by
identifying heuristic hyper-parameter tuning methodologies to further ensure
the generated data distribution is similar to the true distribution of the
original data-sets. While a rigorous mathematical optimization for reducing the
distances in distributions is not offered in this work, we opine that this
prototype of an autonomous-agent generative complex social model is useful for
studying and researching on pattern of life and anomaly detection where there
is strict limitation or lack of sufficient data for using practical machine
learning solutions in mission-critical applications.
