|Subject:||Re: [Swarm-modeling] Automatic Differentiation of Programs with Discrete Randomness|
|Date:||Mon, 24 Oct 2022 16:40:50 +0200|
|User-agent:||Mozilla/5.0 (Macintosh; Intel Mac OS X 10.15; rv:102.0) Gecko/20100101 Thunderbird/102.4.0|
Paul, Glen et al.,
I recently taught a course on Agent-Based and Hybrid Modeling in Ecology – all the notes and links to references are posted at
so this contains my perspective on the current state of ABMs at least in application to ecology.
Louis J. Gross (he, him, his)
Chancellor’s Professor Emeritus and Emeritus Distinguished Professor
of Ecology and Evolutionary Biology and Mathematics
Director Emeritus, National Institute for Mathematical and Biological
Director Emeritus The Institute for Environmental Modeling
University of Tennessee - Knoxville
President, 2006-2007, 2021-2022, UTK Faculty Senate
Past-President, 2003-2005, Society for Mathematical Biology
Neat! More importantly, what else going on with Glen?
I left KU, working in business. But I still get calls from time to time asking for agent based models.
I wondered what is the state of the art?
Paul E. Johnson
Senior Data Scientist, H&R Block Corporate Headquarters
Professor Emeritus, University of Kansas
On October 20, 2022 1:11:50 PM CDT, glen e ropella <firstname.lastname@example.org> wrote:
Automatic differentiation (AD), a technique for constructing new programs which compute the derivative of an original program, has become ubiquitous throughout scientific computing and deep learning due to the improved performance afforded by gradient-based optimization. However, AD systems have been restricted to the subset of programs that have a continuous dependence on parameters. Programs that have discrete stochastic behaviors governed by distribution parameters, such as flipping a coin with probability of being heads, pose a challenge to these systems because the connection between the result (heads vs tails) and the parameters (p) is fundamentally discrete. In this paper we develop a new reparameterization-based methodology that allows for generating programs whose expectation is the derivative of the expectation of the original program. We showcase how this method gives an unbiased and low-variance estimator which is as automated as traditional AD mechanisms. We demonstrate unbiased forward-mode AD of discrete-time Markov chains, agent-based models such as Conway's Game of Life, and unbiased reverse-mode AD of a particle filter. Our code is available at this https URL.
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