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Composable Uncertainty in Symmetric Monoidal Categories for Design Problems

Marius Furter, Yujun Huang, Gioele Zardini

TL;DR

This work employs the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal $\mathcal{V}$-category with parametric maps in a Markov category induced by a symmetric monoidal monad, allowing (co)monoidal and compact closed structures to be transferred.

Abstract

Applied category theory often studies symmetric monoidal categories (SMCs) whose morphisms represent open systems. These structures naturally accommodate complex wiring patterns, leveraging (co)monoidal structures for splitting and merging wires, or compact closed structures for feedback. A key example is the compact closed SMC of design problems (DP), which enables a compositional approach to co-design in engineering. However, in practice, the systems of interest may not be fully known. Recently, Markov categories have emerged as a powerful framework for modeling uncertain processes. In this work, we demonstrate how to integrate this perspective into the study of open systems while preserving consistency with the underlying SMC structure. To this end, we employ the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal V-category C with parametric maps A to C(X,Y) in a Markov category induced by a symmetric monoidal monad. This results in a symmetric monoidal 2-category N*C with the same objects as C and reparametrization 2-cells. By choosing different monads, we capture various types of uncertainty. The category underlying C embeds into N*C via a strict symmetric monoidal functor, allowing (co)monoidal and compact closed structures to be transferred. Applied to DP, this construction leads to categories of practical relevance, such as parametrized design problems for optimization, and parametrized distributions of design problems for decision theory and Bayesian learning.

Composable Uncertainty in Symmetric Monoidal Categories for Design Problems

TL;DR

This work employs the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal -category with parametric maps in a Markov category induced by a symmetric monoidal monad, allowing (co)monoidal and compact closed structures to be transferred.

Abstract

Applied category theory often studies symmetric monoidal categories (SMCs) whose morphisms represent open systems. These structures naturally accommodate complex wiring patterns, leveraging (co)monoidal structures for splitting and merging wires, or compact closed structures for feedback. A key example is the compact closed SMC of design problems (DP), which enables a compositional approach to co-design in engineering. However, in practice, the systems of interest may not be fully known. Recently, Markov categories have emerged as a powerful framework for modeling uncertain processes. In this work, we demonstrate how to integrate this perspective into the study of open systems while preserving consistency with the underlying SMC structure. To this end, we employ the change-of-base construction for enriched categories, replacing the morphisms of a symmetric monoidal V-category C with parametric maps A to C(X,Y) in a Markov category induced by a symmetric monoidal monad. This results in a symmetric monoidal 2-category N*C with the same objects as C and reparametrization 2-cells. By choosing different monads, we capture various types of uncertainty. The category underlying C embeds into N*C via a strict symmetric monoidal functor, allowing (co)monoidal and compact closed structures to be transferred. Applied to DP, this construction leads to categories of practical relevance, such as parametrized design problems for optimization, and parametrized distributions of design problems for decision theory and Bayesian learning.
Paper Structure (13 sections, 9 theorems, 19 equations, 2 figures)

This paper contains 13 sections, 9 theorems, 19 equations, 2 figures.

Table of Contents

  1. Introduction
  2. Acknowledgments.
  3. Background
  4. Enriched categories
  5. Change of base
  6. Uncertainty with external parametrization
  7. Uncertainty monads
  8. Partial slice functor
  9. Main construction
  10. Application to co-design
  11. Representing parameterized and uncertain design problems
  12. Making decisions
  13. Learning design problems

Key Result

Proposition 2.18

Any monoidal functor $N \colon \mathcal{V} \to \mathcal{W}$ induces a 2-functor $N_* \colon {\mathbb{V}\mathsf{cat}} \to {\mathbb{W}\mathsf{cat}}$, given by change-of-base along $N$. Explicitly, $N_*$ is defined as follows:

Figures (2)

  • Figure 1: Design problem for an electric vehicle composed of chassis and battery components.
  • Figure 2: Parameterized version of the electric vehicle design problem. The rounded rectangles represent 1-cells $A \to \mathsf{M} F \mathsf{DP}(\mathcal{P}, \mathcal{Q})$, while the rectangles represent reparameterization 2-cells given by arrows in $\mathsf{Kl}_\mathsf{M}$.

Theorems & Definitions (56)

  • Example 2.1: Design problems
  • Definition 2.2: Monoidal functor
  • Definition 2.3: $\mathcal{V}$-category
  • Definition 2.4: $\mathcal{V}$-functor
  • Definition 2.5: $\mathcal{V}$-natural transformation
  • Example 2.6
  • Example 2.7
  • Example 2.8
  • Example 2.9
  • Definition 2.10: Category underlying a $\mathcal{V}$-category
  • ...and 46 more