Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/243489 
Erscheinungsjahr: 
2020
Schriftenreihe/Nr.: 
LEM Working Paper Series No. 2020/31
Verlag: 
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM), Pisa
Zusammenfassung: 
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.
Schlagwörter: 
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
JEL: 
C15
C18
C63
D53
E30
Dokumentart: 
Working Paper

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