This is an idea proposed in 2022 as a Cambrige Computer Science Part III or MPhil project, and has been completed by Sharan Agrawal. It was supervised by Anil Madhavapeddy and Srinivasan Keshav as part of my Remote Sensing of Nature project.
As the world faces twinned crises of climate change and biodiversity loss, the need for integrated policy approaches addressing both is paramount. To help address this, this project investigates a new agent-based model dubbed the VDSK-B. Using Dasgupta's review of the economics of biodiversity, it builds on the Dystopian Schumpeter meets Keynes (DSK) climate economics model to link together the climate, economy and biosphere. This is the first ABM proposed that integrates all 3 key elements.
The project also investigates how to scale such ABMs to be applicable for global policy design and scale to planetary-sized models. A new ABM framework called SalVO expresses agent updates as recursive applications of pure agent functions. This formalism differs from existing computational ABM models but is shown to be expressive enough to emulate a Turing complete language. SalVO is built on a JAX backend and designed to be scalable, vectorized, and optimizable. Employing hardware acceleration, tests showed it was more performant and more able to scale on a single machine than any existing ABM framework, such as FLAME (GPU).
The dissertation is available as UCAM-CL-TR-985 from the Cambridge Computer Lab technical reports series. The project was awarded the "2023 best M.Phil Project" prize from the Cambridge Computer Science department.
Sharan Agrawal also presented this work at PROPL 2024:
Pedro Sousa did a follow up project on Reverse emulating agent-based models for policy simulation in 2023.