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Empirically grounded agent-based models of innovation diffusion: a critical review

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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.

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Notes

  1. The concepts of calibration and validation are explained in Sect. 5.1 below.

  2. For simplicity, we omit “20” and use the last two digits to denote a year. For example, “07(2)” stands for 2 publications in year 2007.

  3. The short for the Unified Modeling Language, developed by the Object Management Group: http://www.omg.org.

  4. A standard to describe agent-based models originally proposed by Grimm et al. (2006) for ecological modeling.

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Acknowledgements

This work was partially supported by the U.S. Department of Energy (DOE) office of Energy Efficiency and Renewable Energy, under the Solar Energy Evolution and Diffusion Studies (SEEDS) program, the National Science Foundation (IIS-1526860), and the Office of Naval Research (N00014-15-1-2621).

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    Haifeng Zhang & Yevgeniy Vorobeychik

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Zhang, H., Vorobeychik, Y. Empirically grounded agent-based models of innovation diffusion: a critical review. Artif Intell Rev 52, 707–741 (2019). https://doi.org/10.1007/s10462-017-9577-z

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