Gradient-Assisted Calibration for Financial Agent-Based Models

Author:

Dyer Joel1ORCID,Quera-Bofarull Arnau1ORCID,Chopra Ayush2ORCID,Farmer J. Doyne1ORCID,Calinescu Anisoara1ORCID,Wooldridge Michael1ORCID

Affiliation:

1. University of Oxford, United Kingdom

2. MIT, USA

Funder

European Union Horizon2020

UKRI

Publisher

ACM

Reference41 articles.

1. Philipp Andelfinger . 2021. Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization. arXiv:2103.12476 [cs, eess] (March 2021 ). arxiv:2103.12476 [cs, eess] Philipp Andelfinger. 2021. Differentiable Agent-Based Simulation for Gradient-Guided Simulation-Based Optimization. arXiv:2103.12476 [cs, eess] (March 2021). arxiv:2103.12476 [cs, eess]

2. Automatic differentiation of programs with discrete randomness;Arya Gaurav;Advances in Neural Information Processing Systems,2022

3. Efficient Calibration of Multi-Agent Simulation Models from Output Series with Bayesian Optimization

4. Automatic differentiation in machine learning: a survey;Baydin Atilim Gunes;Journal of Marchine Learning Research,2018

5. Yoshua Bengio , Nicholas Léonard , and Aaron Courville . 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 ( 2013 ). Yoshua Bengio, Nicholas Léonard, and Aaron Courville. 2013. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432 (2013).

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