Empirically Grounded Agent-Based Models of Innovation Diffusion: A Critical Review

TL;DR

This paper critically reviews empirically grounded agent-based models of innovation diffusion, highlighting their importance for policy and proposing calibration and validation solutions.

Contribution

It categorizes existing models, connects methodologies across fields, and suggests maximum likelihood estimation as a promising calibration framework.

Findings

Empirically grounded ABMs are increasingly used for policy guidance.

Calibration and validation of ABMs face four major challenges.

Maximum likelihood estimation is promising for model calibration.

Abstract

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.