Heterogeneity in Agent-Based Models

Agent-based models are flexible tools that allow modellers to capture heterogeneity in agent attributes, characteristics, and behaviours. In this paper, heterogeneity is defined as agent granularity, referring to the level of detail used to describe agent attributes, behaviours, interaction processes, and decision-making rules. However, the increased complexity associated with greater levels of heterogeneity, and hence more parameters, can make the already challenging process of model calibration even more difficult. While modellers recognise the importance of calibration, the issue of uniquely determining model input based on a given output, known as parameter identification, is often overlooked. A central point of this study is that identifiability crucially depends on the outcomes or summary statistics chosen for calibration: even a well-specified model may become empirically uninformative if the selected statistics are not sufficiently sensitive to parameter variation. This paper argues that one significant impact of increasing heterogeneity in an agent-based model is the parameter identification problem, where the effects of model inputs cannot be uniquely distinguished in model outputs. To address this issue, the paper presents a comparative study of homogeneous and heterogeneous scenarios in agent-based models. Using a simple contagion case study model and approximate Bayesian computation for calibration, the study demonstrates that introducing heterogeneity reduces the accuracy of parameter calibration compared to the homogeneous case. This decline in accuracy is attributed to the difficulty in isolating the effects of the additional parameters introduced by heterogeneity. Rather than proposing computational fixes, the paper situates these findings within the broader methodological debate between KISS (“Keep It Simple, Stupid†) and KIDS (“Keep It Descriptive, Stupid†) strategies, highlighting how the trade-off between descriptive realism and tractability directly shapes the reliability of inference from ABMs.