TY - GEN
T1 - MultiVeStA
T2 - 10th International Symposium on From Data Models and Back, DataMod 2021, held as a satellite event of the 19th International Conference on Software Engineering and Formal Methods, SEFM 2021
AU - Vandin, Andrea
AU - Giachini, Daniele
AU - Lamperti, Francesco
AU - Chiaromonte, Francesca
N1 - Publisher Copyright:
© 2022, Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - We overview our recent work on the statistical analysis of simulation models and, especially, economic agent-based models (ABMs). We present a redesign of MultiVeStA, a fully automated and model-agnostic toolkit that can be integrated with existing simulators to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeler, (ii) improves reproducibility of results, (iii) optimizes running time given the modeler’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 behavior (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. This allows us to obtain new insights from models from the literature, and to fix some erroneous conclusions on them.
AB - We overview our recent work on the statistical analysis of simulation models and, especially, economic agent-based models (ABMs). We present a redesign of MultiVeStA, a fully automated and model-agnostic toolkit that can be integrated with existing simulators to inspect simulations and perform counterfactual analysis. Our approach: (i) is easy-to-use by the modeler, (ii) improves reproducibility of results, (iii) optimizes running time given the modeler’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 behavior (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. This allows us to obtain new insights from models from the literature, and to fix some erroneous conclusions on them.
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U2 - 10.1007/978-3-031-16011-0_1
DO - 10.1007/978-3-031-16011-0_1
M3 - Conference contribution
AN - SCOPUS:85141668940
SN - 9783031160103
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 3
EP - 6
BT - From Data to Models and Back - 10th International Symposium, DataMod 2021, Revised Selected Papers
A2 - Bowles, Juliana
A2 - Broccia, Giovanna
A2 - Pellungrini, Roberto
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 December 2021 through 7 December 2021
ER -