  Innovation diffusion has been studied extensively in a variety of
disciplines, including sociology, economics, marketing, ecology, and computer
science. Traditional literature on innovation diffusion has been dominated by
models of aggregate behavior and trends. However, the agent-based modeling
(ABM) paradigm is gaining popularity as it captures agent heterogeneity and
enables fine-grained modeling of interactions mediated by social and geographic
networks. While most ABM work on innovation diffusion is theoretical,
empirically grounded models are increasingly important, particularly in guiding
policy decisions. We present a critical review of empirically grounded
agent-based models of innovation diffusion, developing a categorization of this
research based on types of agent models as well as applications. By connecting
the modeling methodologies in the fields of information and innovation
diffusion, we suggest that the maximum likelihood estimation framework widely
used in the former is a promising paradigm for calibration of agent-based
models for innovation diffusion. Although many advances have been made to
standardize ABM methodology, we identify four major issues in model calibration
and validation, and suggest potential solutions.
