Navigating through Economic Complexity: Phase Diagrams & Parameter Sloppiness

TL;DR

This paper emphasizes the importance of phase diagrams in Agent Based Models (ABMs) for understanding emergent phenomena, stability, and robustness, highlighting the role of high-dimensional analysis and parameter sensitivity.

Contribution

It introduces the use of phase diagrams and parameter sloppiness analysis to better understand and interpret ABMs in economics and finance.

Findings

Phase diagrams reveal critical stability boundaries and discontinuities.

Robustness of ABMs can be tested through sensitivity to heuristic rule changes.

Identifying stiff and sloppy directions aids high-dimensional model analysis.

Abstract

We argue that establishing the phase diagram of Agent Based Models (ABM) is a crucial first step, together with a qualitative understanding of how collective phenomena come about, before any calibration or more quantitative predictions are attempted. Computer-aided *gedanken* experiments are by themselves of genuine value: if we are not able to make sense of emergent phenomena in a world in which we set all the rules, how can we expect to be successful in the real world? ABMs indeed often reveal the existence of Black Swans/Dark Corners i.e. discontinuity lines beyond which runaway instabilities appear, whereas most classical economic/finance models are blind to such scenarii. Testing for the overall robustness of the phase diagram against changes in heuristic rules is a way to ascertain the plausibility of such scenarii. Furthermore, exploring the phase diagrams of ABM in high dimensions should benefit enormously from the identification of ``stiff'' and ``sloppy'' directions in parameter space.