# Universal Decision Models

arXiv: https://arxiv.org/abs/2110.15431 · HTML: https://mlsanity.com/paper/2110.15431/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 57 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 2 of 2)

- [Markov Properties for Graphical Models with Cycles and Latent Variables](https://mlsanity.com/paper/1710.08775/) (2017)
- [Causal Inference in Network Economics](https://mlsanity.com/paper/2109.11344/) (2021)

## Built on by (most cited first, 7 of 7)

- [Unifying Causal Inference and Reinforcement Learning using Higher-Order Category Theory](https://mlsanity.com/paper/2209.06262/) (2022)
- [Working Paper: Towards a Category-theoretic Comparative Framework for Artificial General Intelligence](https://mlsanity.com/paper/2603.28906/) (2026)
- [On The Universality of Diagrams for Causal Inference and The Causal Reproducing Property](https://mlsanity.com/paper/2207.02917/) (2022)
- [Universal Reinforcement Learning in Coalgebras: Asynchronous Stochastic Computation via Conduction](https://mlsanity.com/paper/2508.15128/) (2025)
- [Universal Decision Learners](https://mlsanity.com/paper/2605.30694/) (2026)
- [A Layered Architecture for Universal Causality](https://mlsanity.com/paper/2212.08981/) (2022)
- [Consciousness as a Functor](https://mlsanity.com/paper/2508.17561/) (2025)

## Abstract (arXiv)

Humans are universal decision makers: we reason causally to understand the world; we act competitively to gain advantage in commerce, games, and war; and we are able to learn to make better decisions through trial and error. In this paper, we propose Universal Decision Model (UDM), a mathematical formalism based on category theory. Decision objects in a UDM correspond to instances of decision tasks, ranging from causal models and dynamical systems such as Markov decision processes and predictive state representations, to network multiplayer games and Witsenhausen's intrinsic models, which generalizes all these previous formalisms. A UDM is a category of objects, which include decision objects, observation objects, and solution objects. Bisimulation morphisms map between decision objects that capture structure-preserving abstractions. We formulate universal properties of UDMs, including information integration, decision solvability, and hierarchical abstraction. We describe universal functorial representations of UDMs, and propose an algorithm for computing the minimal object in a UDM using algebraic topology. We sketch out an application of UDMs to causal inference in network economics, using a complex multiplayer producer-consumer two-sided marketplace.