Institute for Categorical Cybernetics
Governance and control for the age of AI
Our mission is to develop theory and software for governing systems that learn and make decisions, for the benefit of their users and of humanity.
Latest posts
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Foundations of Bidirectional Programming I: Well-Typed Substructural Languages
This is the first post in a new series documenting my work developing a bidirectional programming language, in which all programs are interpreted as optics. This is something I've been thinking about for a long time, and eventually I became convinced that there were enough subtle issues that I should take things extremely slowly and actually learn some programming language theory. As a result, this post will not be about categorical cybernetics at all, but is a foundation to a huge tower of categorical cybernetics machinery that I will build later. -
Beliefs, Belief Propagation and Belief Clusters
In which we try to capture all the ways how beliefs can shape social and economic interaction. -
Compositionality and the Mass Customization of Economic Models
Are economic models useful for making decisions? One might expect that there is clear answer to this simple question. But in fact opinions on the usefulness or non-usefulness of models as well as what exactly makes models useful vary widely. In this post, I want to explore the question of usefulness. Even more so, I want to explore how the usefulness ties into the modelling process. The reason for doing so is simple: Part of our efforts at CyberCat is to build software tools to improve and accelerate the modelling process. -
The Yoga of Contexts I
Suppose we have some category, whose morphisms are some kind of processes or systems that we care about. We would like to be able to talk about contexts (or environments) in which these processes or systems can be located. -
Reinforcement Learning through the Lens of Categorical Cybernetics
This is an overview of the 'RL lens', a construction that we recently introduced to understand some reinforcement learning algorithms like Q-learning -
The Blurry Boundary between Economics and Operations Research
In which we bring back together the estranged fraternal disciplines of economics and operations research and map out how we can combine them to design cybernetic economies. -
Exploring best response dynamics
I explore the effect of players following their best response dynamics in large random normal form games. -
The Build Your Own Open Games Engine Bootcamp — Part I: Lenses
The first installment of a multi-part series demistifying the underlying mechanics of the open games engine in a simple manner. -
Building a Neural Network from First Principles using Free Categories and Para(Optic)
In this post we will look at how dependent types can allow us to effortlessly implement the category theory of machine learning directly, opening up a path to new generalisations. -
Enriched Closed Lenses
I'm going to record something that I think is known to everyone doing research on categorical cybernetics, but I don't think has been written down somewhere: an even more general version of mixed optics that replaces the backwards actegory with an enrichment. With it, I'll make sense of a curious definition appearing in The Compiler Forest. -
Modular Error Reporting with Dependent Lenses
Dependent lenses are useful for general-purpose programming, but in which way exactly? This post demonstrates the use of dependent lenses as input/output-conversion processes, using parsing and error location reporting as a driving example. -
Value Chain Integrity
In which we discuss how knowledge travels thru the economy, and how, when and where it forms clusters. -
Colimits of Selection Functions
In Towards Foundations of Categorical Cybernetics we built a category whose objects are selection functions and whose morphisms are lenses. It was a key step in how we justified open games in that paper: they're just parametrised lenses weighted by selection functions. In this post I'll show that by adding dependent types and stirring, we can get a nicer category that does the same job but has all colimits, and comes extremely close to having all limits. Fair warning: this post assumes quite a bit of category-theoretic background. -
On Organization
In which we describe organization and organizations as tectonic plates shaped by clashing beliefs. -
Learning with Invariant Preferences
A system whose architecture has invariant preferences will act in a way to bring about or avoid certain states of the world, no matter what it learns. A lot of people have already put a lot of thought into the issue of good and bad world-states, including very gnarly issues of how to agree on what they should be - what I'm proposing is a technological missing link, how to bridge from that level of abstraction to low-level neural network architectures.