Learning What Makes a Society Tick

We present a machine learning methodology (models, al- gorithms, and experimental data) to discovering the agent dynamics that drive the evolution of the social groups in a community. We use a parameterized probabilistic agent- based model integrating with micro-laws to present the agent dynamics. The micro-laws with different parame- ters present different actors´ behaviors. Our approach is to identify the appropriate parameters in the model including discrete parameters together with continues parameters. To solve this mixed optimization problem, we develop heuris- tic expectation-maximization style algorithms for determin- ing the appropriate micro-laws of a community based on either the observed social group evolution, or observed set of communications between actors without considering the semantics. Also, in order to avoid the resulting combina- torial explosion, we appropriately approximate and opti- mize the objective within a coordinate-wise gradient ascent (search) setting for continuous (discrete) variables. Finally, we present the learning performance from extensive experi- ments.