2020/31 | LEM Working Paper Series | ||||||||||||||||
Automated and Distributed Statistical Analysis of Economic Agent-Based Models |
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Andrea Vandin, Daniele Giachini, Francesco Lamperti and Francesca Chiaromonte |
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Keywords | |||||||||||||||||
ABM; Statistical Model Checking; Ergodicity analysis; Steady state analysis; Transient analysis;
Warmup estimation; T-test and power; Prediction markets; Macro ABM.
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JEL Classifications | |||||||||||||||||
C15, C18, C63, D53, E30
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Abstract | |||||||||||||||||
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.
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