Composable Uncertainty in Symmetric Monoidal Categories for Design Problems

TL;DR

This paper introduces a framework integrating uncertainty into symmetric monoidal categories for design problems, enabling compositional modeling of uncertain open systems with applications in optimization and Bayesian learning.

Contribution

It develops a method to incorporate Markov category-based uncertainty into symmetric monoidal categories, preserving their structure for complex system modeling.

Findings

Enables modeling of uncertain open systems within SMCs.

Allows transfer of (co)monoidal structures to categories with uncertainty.

Facilitates applications in optimization, decision theory, and Bayesian learning.

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.