A framework for the calibration of social simulation models

TL;DR

This paper introduces a simulation-based framework for calibrating agent-based social models with distributional outputs, using indirect inference and applied to online community norm emergence.

Contribution

It presents a novel calibration method for agent-based models with complex distributional outputs, demonstrated on real-world Wikipedia data.

Findings

Effective calibration of social simulation models achieved

Model fit and diagnostics discussed in detail

Framework applicable to large-scale socio-technical systems

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.