# Dynamic Operads, Dynamic Categories: From Deep Learning to Prediction Markets

arXiv: https://arxiv.org/abs/2205.03906 · HTML: https://mlsanity.com/paper/2205.03906/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 4 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 1 of 1)

- [Learners' Languages](https://mlsanity.com/paper/2103.01189/) (2021)

## Built on by (most cited first, 2 of 2)

- [Knowledge Engineering for Wind Energy](https://mlsanity.com/paper/2310.00804/) (2023)
- [Higher Gauge Flow Models](https://mlsanity.com/paper/2507.16334/) (2025)

## Abstract (arXiv)

Natural organized systems adapt to internal and external pressures and this happens at all levels of the abstraction hierarchy. Wanting to think clearly about this idea motivates our paper, and so the idea is elaborated extensively in the introduction, which should be broadly accessible to a philosophically-interested audience. In the remaining sections, we turn to more compressed category theory. We define the monoidal double category Org of dynamic organizations, we provide definitions of Org-enriched, or dynamic, categorical structures -- e.g. dynamic categories, operads, and monoidal categories -- and we show how they instantiate the motivating philosophical ideas. We give two examples of dynamic categorical structures: prediction markets as a dynamic operad and deep learning as a dynamic monoidal category.