Agent-based model introspection using differentiable probabilistic surrogates

Agent-based modelling (ABMing) is a natural and powerful modelling paradigm for complex systems. However, the complexity of these simulators complicates the task of analysing and understanding these models, their full variety of behaviours, and the sensitivities of these behaviours to changes in the parameters. This problem has motivated previous work on approaches to efficiently exploring the parameter space – and therefore the behaviours and parameter sensitivities of the model – based on Fisher information matrix-like objects and through appeal to the notion of “sloppiness”. These works have not however provided a full probabilistic treatment of ABMs, which are often stochastic. In this paper, we propose a framework for constructing Fisher information matrices for ABMs using differentiable, probabilistic surrogate models. We demonstrate a simple implementation of this framework that enables us to consistently identify stiff and sloppy directions in parameter space for a macroeconomic ABM of economic growth.