Agents.jl and the next chapter in agent based modelling
2021-07-28, 13:00–13:30 (UTC), Blue

Complex dynamical systems are comprised of many interacting sub-systems that couple together through multiple, varying (and many times non-linear) processes: creating emergent system properties as a consequence. Agents.jl provides a framework to work with such dynamics, through a bottom-up approach known as Agent Based Modelling. This talk provides an overview of the package, and discusses how the greater Julia ecosystem may provide the next paradigm shift in this well established research area.


Agent based modelling (ABM) is a simulation method in which autonomous agents react to their environment, given a predefined set of rules. It is a bottom-up approach for modelling and simulating complex systems, such as behavior, decision making, crowd dynamics and other socio-economic problems; as well as, but not limited to, complex natural sciences such as chemical reactions or biological processes.
Since ABMs are not described by simple and concise mathematical equations, code that generates them is typically complicated, large, and slow. In addition, since many of these problems are very domain specific, a lot of ABMs are hand written from scratch.
Agents.jl provides a solution to this complication. Acknowledging that ABM frameworks have existed for decades, we show that Agents.jl is not only the most performant, but also the least complicated software (in terms of lines of code written to implement well-known ABM test cases), providing the same (and sometimes more) features as competitors.
This enables rapid prototyping of your domain specific ABM, with tried and tested (but generic) tooling.
The talk will provide an introduction to many of these helpful features, as well as showcase how well it integrates with the entire Julia ecosystem. Interactive applications with Makie.jl, differential equations from DifferentialEquations.jl, parameter optimization from BlackboxOptim.jl, and more.
To conclude, we'll outline some of the big next-steps on the roadmap that other ABM frameworks will struggle to match in the absence of the Julia ecosystem.

Head of Scientific Computing at Cervest, working on Climate Intelligence solutions which quantify climate risk on a per-asset level, globally.

PhD in Physics from RMIT University in Melbourne, Australia; focusing on defects in the Josephson junctions of superconducting phase-qubits. Followed up by a postdoctoral position at Chalmers University in Gothenburg, Sweden investigating high energy laser-plasma interactions and fusion energy. More recently a researcher at the Stockholm Resilience Centre, with research areas concerning Planetary Boundaries, global climate-economy models, social ecological systems and other Earth system sciences.