Digifesto

Well over a year ago I wrote about building something new.

I’m pleased to say that now there is a 0.1.0 release of scikit-agent available.

Like one does, I find that as I work on it, what was a simple idea expands, and I now understand that the project is indeed quite technically ambitious. This first release is just a tiny part of what the library should be. Nevertheless, the announcement has had a positive reception among friends and even LinkedIn of all fora. It is an encouraging start.

It is very satisfying for me to build this tool, as it feels like the culmination of a lot of prior work. I was reminded this morning that as early as 2013 I was working on strategic graphical models — I hadn’t yet heard of Koller and Milch’s MAIDs or ‘structural causal games’ — out of a concern for “computational asymmetry” — market failures due to an imbalance in available compute infrastructure. I think I may finally be building the research tooling needed to do replicable work on that problem. This is just in time for a global arms race in data center build-out.

The same tooling is, I believe, the right path forward for establishing privacy and loyalty in Agentic AI, and in establishing safety in multi-agent AI systems.

I have one fear, which is that this is a hammer, and everything looks like a nail to me. However, I like to think that I have encountered a lot of strange nails and didn’t have the right tool for them, and that this is what’s informing the hammer design.

I will admit that AI code generation and agents are helping me a lot with the design and implementation of the software. This was a source of discomfort early on. My conclusion is that the key to using these tools is responsible workflow.

I’m very grateful to Alan Lujan for his contributions to the library. scikit-agent is quite literally a fork of Econ-ARK’s HARK library, which is where Alan and I started working together. There are a lot of techniques and use cases for scikit-agent that are based on heterogeneous agent macroeconomic modeling, which is a very technically deep field with its own depths of specialization. Alan and the computational economists are able to see a lot of things that I, with my rather interdisciplinary and scattered training, have as blind spots. I’m always learning in my interactions with him.

I’m likewise very grateful to the now rather dormant Causal Incentives Working Group, which between 2019 and 2025 did a lot of foundational work on causal game theory, which really put it back on the map of AI research after a long hiatus 2005-2015. I only wish I had been able to connect with them earlier and be involved with some of their great work. Now it’s a pleasure to build on it. I expect scikit-agent to eventually include a lot of examples and implementations of their contributions. I have already put a couple of their models into the gallery pages, and there’s much more where that came from.