Automated and distributed statistical analysis of economic agent-based models - Andrea Vandin; Daniele Giachini; Francesco Lamperti; Francesca Chiaromonte (Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), 2020)
We propose a novel approach to the statistical analysis of simulation models and, especially, agent-based models (ABMs). Our main goal is to provide a fully automated and model-independent tool-kit to inspect simulations and perform counter-factual analysis. Our approach: (i) is easy-to-use by the modeller, (ii) improves reproducibility of results, (iii) optimizes running time given the modeller's machine, (iv) automatically chooses the number of required simulations and simulation steps to reach user-specified statistical confidence, and (v) automatically performs a variety of statistical tests. In particular, our framework is designed to distinguish the transient dynamics of the model from its steady state behaviour (if any), estimate properties of the model in both ''phases'', and provide indications on the ergodic (or non-ergodic) nature of the simulated processes - which, in turns allows one to gauge the reliability of a steady state analysis. Estimates are equipped with statistical guarantees, allowing for robust comparisons across computational experiments. To demonstrate the effectiveness of our approach, we apply it to two models from the literature: a large scale macro-financial ABM and a small scale prediction market model. Compared to prior analyses of these models, we obtain new insights and we are able to identify and fix some erroneous conclusions.
Keywords: C15; C18; C63; D53; E30; 330; ABM; Automated and Distributed Simulation-based Analysis; Statistical Model Checking; Steady-state and Transient analysis; Warmup estimation; T-test and power; Prediction markets; Macro ABM