A framework for the calibration of social simulation models

Abstract

Simulation with agent-based models is increasingly used in the study of complex socio-technical systems and in social simulation in general. This paradigm offers a number of attractive features, namely the possibility of modeling emergent phenomena within large populations. As a consequence, often the quantity in need of calibration may be a distribution over the population whose relation with the parameters of the model is analytically intractable. Nevertheless, we can simulate. In this paper we present a simulation-based framework for the calibration of agent-based models with distributional output based on indirect inference. We illustrate our method step by step on a model of norm emergence in an online community of peer production, using data from three large Wikipedia communities. Model fit and diagnostics are discussed.

31 pages, 7 figures, accepted on Adv. Compl. Sys

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Cited by in corpus (4)

• Opinion dynamics: models, extensions and external effects
• Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers
• Indirect inference through prediction
• A Note on Simulation-Based Inference by Matching Random Features