Empirical Validation of Agent-Based Models: Alternatives and Prospects

By Paul Windrum, Giorgio Fagiolo, Alessio Moneta. Journal of Artificial Societies and Social Simulation, 2007

602 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/419717e4a57b1d6f5430b222dc45456df08c3b6d

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Citations in the corpus

Models in evolutionary economics and environmental policy: Towards an evolutionary environmental economics (2009, background): “An important weakness lies in the lack of empirical testing of existing evolutionary models, due to the fact that evolutionary models generally have (too) many parameters [115].”
More is different ... and complex! the case for agent-based macroeconomics (2019, background): “Finally, some works estimate ABMs via indirect inference (see e.g. Alfarano et al., 2005; Winker et al., 2007; Grazzini et al., 2013; Grazzini and Richiardi, 2015) or Bayesian methods (Grazzini et al., 2017).”
Faraway, So Close: Coupled Climate and Economic Dynamics in an Agent-based Integrated Assessment Model (2018, unknown): “31In particular, the model has been calibrated through an indirect calibration exercise (Windrum et al., 2007).”
Agent-Based Modeling in Public Health: Current Applications and Future Directions (2018, background): “Extensive reviews of ABM validation have been offered in other disciplines, including ecology and economics (36, 40, 64, 105).”
Spatial agent-based models for socio-ecological systems: Challenges and prospects (2013, background): “Substantial attention has also been given to systemizing approaches to empirically characterize and parameterize agents' behavior in ABMs for SES (Janssen and Ostrom, 2006; Robinson et al., 2007; Windrum et al., 2007; Valbuena et al., 2008; Smajgl et al., 2011).”
Agent-based simulation of innovation diffusion: a review (2011, background): “A recurring issue (cf., e.g., Garcia et al. 2007; Midgley et al. 2007; Windrum et al. 2007; Ormerod and Rosewell 2009), which we continue to emphasize here, is the dif-ﬁculty of validating ABMs in general, and agent-based diffusion models in particular.”
The Complexities of Agent-Based Modeling Output Analysis (2015, background): “discuss each of these, their advantages, and drawbacks, in the context of ABMs. 3.9 In one-parameter-at-a-time (OAT), each input parameter in turn is examined over a set of values (defined either ex ante to the SA or dynamically during SA) and in isolation by holding the other parameters at a consta”