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Tutorial on Differentiable Probabilistic Programming for Agent-Based Models [ICAIF 2024]

Welcome to the tutorial on differentiable agent-based models!

Description

This tutorial will introduce a new paradigm for agent-based models (ABMs) that leverages automatic differentiation (AD) to efficiently compute simulator gradients. In particular, this tutorial will provide:

  1. An overview of vanilla AD and AD methods to differentiate through discrete stochastic programs.
  2. A walkthrough on how to build a differentiable economic ABM.
  3. State-of-the-art methods for calibrating differentiable ABMs.

Target audience

The tutorial is aimed at members of the ICAIF community who are interested in the deployment, calibration and analysis of large-scale agent-based models. No previous experience with automatic differentiation will be required to understand the tutorial material.

Outline and Schedule

Date: Friday 15th of November 2024

Time Session Speaker
08:00 - 08:40 Automatic Differentiation for Agent-Based Models Nicholas Bishop
08:40 - 08:50 Break
08:50 - 09:45 Gradient-assisted calibration techniques for ABMs Joel Dyer

Materials

We have created a set of Jupyter notebooks which provide a practical walkthrough of the tutorial material. You can access the Google colab and nbviewer versions of each notebook using the links below:

  1. Automatic Differentiation: [Colab] [nbviewer]
  2. Differentiating Randomness: [Colab][nbviewer]
  3. Differentiable ABMs: [Colab][nbviewer]
  4. Variational Inference: [Colab][nbviewer]

Presentation slides, which supplement the notebooks above, are available [here]({{ site.baseurl }}/icaif_slides.pdf).

Presenters

Joel Dyer is a senior postdoctoral researcher at the University of Oxford’s Department of Computer Science and a Senior Research Fellow at the Oxford Institute for New Economic Thinking.

Nick Bishop is a postdoctoral researcher at the University of Oxford, working within the Department of Computer Science on problems at the intersection of machine learning and agent-based modelling.

Ani Calinescu is an Associate Professor at the University of Oxford's Department of Computer Science, a Senior Research Fellow at the Oxford Institute for New Economic Thinking, and a Co-Investigator on a UKRI-funded project on Robust Large-Scale Agent-Based Modelling, and a Co-PI or Co-Investigator on several JPMC Faculty Research Awards.

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