Joel Dyer
![Personal photo - Joel Dyer](/files/14629//profile2.jpeg)
Joel Dyer
Room
214,
Wolfson Building,
Parks Road, Oxford OX1 3QD
United Kingdom
Interests
- Agent-based modelling
- Bayesian inference
- Monte Carlo
- Simulation-based planning and optimisation
- Machine learning
Biography
Joel is a senior postdoctoral researcher at the Department of Computer Science and a Senior Research Fellow at the Oxford Institute for New Economic Thinking, where he develops methodology and tooling for agent-based simulation models. He recently completed a DPhil in computational statistics and machine learning at the University of Oxford’s Mathematical Institute and Institute for New Economic Thinking under the supervision of Prof. J. Doyne Farmer, where his research focus was on likelihood-free parameter inference methods for simulation models in the social sciences. Previously, Joel has worked as a Research Scientist at Improbable, and has held visiting research positions at The Alan Turing Institute and University of Bristol through The Alan Turing Institute’s Enrichment Scheme.
Selected Publications
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Population synthesis as scenario generation for simulation−based planning under uncertainty
Joel Dyer‚ Arnau Quera−Bofarull‚ Nicholas Bishop‚ J. Doyne Farmer‚ Anisoara Calinescu and Michael Wooldridge
In 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024). 2024.
Details about Population synthesis as scenario generation for simulation−based planning under uncertainty | BibTeX data for Population synthesis as scenario generation for simulation−based planning under uncertainty | Link to Population synthesis as scenario generation for simulation−based planning under uncertainty
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Black−box Bayesian inference for agent−based models
Joel Dyer‚ Patrick Cannon‚ J. Doyne Farmer and Sebastian M. Schmon
In Journal of Economic Dynamics and Control. Vol. 161. 2024.
Details about Black−box Bayesian inference for agent−based models | BibTeX data for Black−box Bayesian inference for agent−based models | DOI (https://doi.org/10.1016/j.jedc.2024.104827) | Link to Black−box Bayesian inference for agent−based models
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Gradient−assisted calibration for financial agent−based models
Joel Dyer‚ Arnau Quera−Bofarull‚ Ayush Chopra‚ J. Doyne Farmer‚ Anisoara Calinescu and Michael Wooldridge
In Proceedings of the Fourth ACM International Conference on AI in Finance (ICAIF '23). Pages 288–296. 2023.
Details about Gradient−assisted calibration for financial agent−based models | BibTeX data for Gradient−assisted calibration for financial agent−based models | DOI (https://doi.org/10.1145/3604237.3626857) | Link to Gradient−assisted calibration for financial agent−based models