There will be a workshop on the categorical semantics of entropy at the CUNY Grad Center in Manhattan on Friday May 13th, organized by John Terilla. I was kindly invited to give an online tutorial beforehand on May 11, which I will give remotely to save carbon. Tai-Danae Bradley will also be giving a tutorial that day in person:

• Tutorial: Categorical Semantics of Entropy, Wednesday 11 May 2022, 13:00–16:30 Eastern Time, Room 5209 at the CUNY Graduate Center and via Zoom. Organized by John Terilla. To attend, register here.

12:00-1:00 Eastern Daylight Time — Lunch in Room 5209.

1:00-2:30 — Shannon entropy from category theory, John Baez, University of California Riverside; Centre for Quantum Technologies (Singapore); Topos Institute.

Shannon entropy is a powerful concept. But what properties single out Shannon entropy as special? Instead of focusing on the entropy of a probability measure on a finite set, it can help to focus on the “information loss”, or change in entropy, associated with a measure-preserving function. Shannon entropy then gives the only concept of information loss that is functorial, convex-linear and continuous. This is joint work with Tom Leinster and Tobias Fritz.

2:30-3:00 — Coffee break.

3:00-4:30 — Operads and entropy, Tai-Danae Bradley, The Master’s University; Sandbox AQ.

This talk will open with a basic introduction to operads and their representations, with the main example being the operad of probabilities. I’ll then give a light sketch of how this framework leads to a small, but interesting, connection between information theory, abstract algebra, and topology, namely a correspondence between Shannon entropy and derivations of the operad of probabilities.

• Symposium on Categorical Semantics of Entropy, Friday 13 May 2022, 9:30-3:15 Eastern Daylight Time, Room 5209 at the CUNY Graduate Center and via Zoom. Organized by John Terilla. To attend, register here.

9:30-10:00 Eastern Daylight Time — Coffee and pastries in Room 5209.

10:00-10:45 — Operadic composition of thermodynamic systems, Owen Lynch, Utrecht University.

The maximum entropy principle is a fascinating and productive lens with which to view both thermodynamics and statistical mechanics. In this talk, we present a categorification of the maximum entropy principle, using convex spaces and operads. Along the way, we will discuss a variety of examples of the maximum entropy principle and show how each application can be captured using our framework. This approach shines a new light on old constructions. For instance, we will show how we can derive the canonical ensemble by attaching a probabilistic system to a heat bath. Finally, our approach to this categorification has applications beyond the maximum entropy principle, and we will give an hint of how to adapt this categorification to the formalization of the composition of other systems.

11:00-11:45 — Polynomial functors and Shannon entropy, David Spivak, MIT and the Topos Institute.

The category Poly of polynomial functors in one variable is extremely rich, brimming with categorical gadgets (e.g. eight monoidal products, two closures, limits, colimits, etc.) and applications including dynamical systems, databases, open games, and cellular automata. In this talk I’ll show that objects in Poly can be understood as empirical distributions. In part using the standard derivative of polynomials, we obtain a functor to Set × Set

^{op}which encodes an invariant of a distribution as a pair of sets. This invariant is well-behaved in the sense that it is a distributive monoidal functor: it acts on both distributions and maps between them, and it preserves both the sum and the tensor product of distributions. The Shannon entropy of the original distribution is then calculated directly from the invariant, i.e. only in terms of the cardinalities of these two sets. Given the many applications of polynomial functors and of Shannon entropy, having this link between them has potential to create useful synergies, e.g. to notions of entropic causality or entropic learning in dynamical systems.

12:00-1:30 — Lunch in Room 5209

1:30-2:15 — Higher entropy, Tom Mainiero, Rutgers New High Energy Theory Center.

Is the frowzy state of your desk no longer as thrilling as it once was? Are numerical measures of information no longer able to satisfy your needs? There is a cure! In this talk we’ll learn about: the secret topological lives of multipartite measures and quantum states; how a homological probe of this geometry reveals correlated random variables; the sly decategorified involvement of Shannon, Tsallis, Réyni, and von Neumann in this larger geometric conspiracy; and the story of how Gelfand, Neumark, and Segal’s construction of von Neumann algebra representations can help us uncover this informatic ruse. So come to this talk, spice up your entropic life, and bring new meaning to your relationship with disarray.

2:30-3:15 — On characterizing classical and quantum entropy, Arthur Parzygnat, Institut des Hautes Études Scientifiques.

In 2011, Baez, Fritz, and Leinster proved that the Shannon entropy can be characterized as a functor by a few simple postulates. In 2014, Baez and Fritz extended this theorem to provide a Bayesian characterization of the classical relative entropy, also known as the Kullback–Leibler divergence. In 2017, Gagné and Panangaden extended the latter result to include standard Borel spaces. In 2020, I generalized the first result on Shannon entropy so that it includes the von Neumann (quantum) entropy. In 2021, I provided partial results indicating that the Umegaki relative entropy may also have a Bayesian characterization. My results in the quantum setting are special applications of the recent theory of quantum Bayesian inference, which is a non-commutative extension of classical Bayesian statistics based on category theory. In this talk, I will give an overview of these developments and their possible applications in quantum information theory.

Wine and cheese reception to follow, Room 5209.

Shannon Entropy and Prime Numbers. http://www.math-math.com/2020/10/entropy-of-primes.html