Basic Category Theory for Computer Scientists

Category theory has been tightly connected to abstract mathematics since the first paper on cohomology by Eilenberg and Mac Lane [EML45] which establishes its basic notions. This appendix is a reminder of a few elementary definitions and results in this branch of mathematics. We refer to [ML71, AL91] for adequate introductions and wider perspectives. In mathematical practice, category theory is helpful in formalizing a problem, as it is a good habit to ask in which category we are working in, if a certain transformation is a functor, if a given subcategory is reflective, etc. Using category theoretical terminology, one can often express a result in a more modular and abstract way. A list of ‘prescriptions’ for the use of category theory in computer science can be found in [Gog91]. Categorical logic is a branch of category theory that arises from the observation due to Lawvere that logical connectives can be suitably expressed by means of universal properties. In this way one represents the models of, say, intuitionistic propositional logic, as categories with certain closure properties where sentences are interpreted as objects and proofs as morphisms (cf. section 4.3). The tools developed in categorical logic begin to play a central role in the study of programming languages. A link between these two apparently distant topics is suggested by: The role of (typed) λ-calculi in the work of Landin, McCarthy, Strachey, and Scott on the foundations of programming languages. […]

Category theory was invented in the 1940s to unify and synthesize different areas in mathematics, and it has proven remarkably successful in enabling powerful communication between disparate fields and subfields within mathematics. This book shows that category theory can be useful outside of mathematics as a rigorous, flexible, and coherent modeling language throughout the sciences. Information is inherently dynamic; the same ideas can be organized and reorganized in countless ways, and the ability to translate between such organizational structures is becoming increasingly important in the sciences. Category theory offers a unifying framework for information modeling that can facilitate the translation of knowledge between disciplines. Written in an engaging and straightforward style, and assuming little background in mathematics, the book is rigorous but accessible to non-mathematicians. Using databases as an entry to category theory, it begins with sets and functions, then introduces the reader to notions that are fundamental in mathematics: monoids, groups, orders, and graphs -- categories in disguise. After explaining the big three concepts of category theory -- categories, functors, and natural transformations -- the book covers other topics, including limits, colimits, functor categories, sheaves, monads, and operads. The book explains category theory by examples and exercises rather than focusing on theorems and proofs. It includes more than 300 exercises, with selected solutions. Category Theory for the Sciences is intended to create a bridge between the vast array of mathematical concepts used by mathematicians and the models and frameworks of such scientific disciplines as computation, neuroscience, and physics.

The correspondence between partial metrics and semivaluations

Mathematician and popular science author Eugenia Cheng is on a mission to show you that mathematics can be flexible, creative, and visual. This joyful journey through the world of abstract mathematics into category theory will demystify mathematical thought processes and help you develop your own thinking, with no formal mathematical background needed. The book brings abstract mathematical ideas down to earth using examples of social justice, current events, and everyday life – from privilege to COVID-19 to driving routes. The journey begins with the ideas and workings of abstract mathematics, after which you will gently climb toward more technical material, learning everything needed to understand category theory, and then key concepts in category theory like natural transformations, duality, and even a glimpse of ongoing research in higher-dimensional category theory. For fans of How to Bake Pi, this will help you dig deeper into mathematical concepts and build your mathematical background.

Using basic category theory, this Element describes all the central concepts and proves the main theorems of theoretical computer science. Category theory, which works with functions, processes, and structures, is uniquely qualified to present the fundamental results of theoretical computer science. In this Element, readers will meet some of the deepest ideas and theorems of modern computers and mathematics, such as Turing machines, unsolvable problems, the P=NP question, Kurt Gödel's incompleteness theorem, intractable problems, cryptographic protocols, Alan Turing's Halting problem, and much more. The concepts come alive with many examples and exercises.

Consciousness is a central issue in neuroscience, however, we still lack a formal framework that can address the nature of the relationship between consciousness and its physical substrates. In this review, we provide a novel mathematical framework of category theory (CT), in which we can define and study the sameness between different domains of phenomena such as consciousness and its neural substrates. CT was designed and developed to deal with the relationships between various domains of phenomena. We introduce three concepts of CT which include (i) category; (ii) inclusion functor and expansion functor; and, most importantly, (iii) natural transformation between the functors. Each of these mathematical concepts is related to specific features in the neural correlates of consciousness (NCC). In this novel framework, we will examine two of the major theories of consciousness, integrated information theory (IIT) of consciousness and temporospatial theory of consciousness (TTC). We conclude that CT, especially the application of the notion of natural transformation, highlights that we need to go beyond NCC and unravels questions that need to be addressed by any future neuroscientific theory of consciousness.

Preface: Volume 81

This paper provides an analysis of the meaning–reference problem, as traditionally advanced in the philosophy of language, for imperative programming languages. Addressing the problem is relevant for the philosophy of computer science in the context of the debate of whether simulative programs can be considered representational theories of their simulated systems. This paper challenges the thesis that, since programming languages have semantics, they also have reference, presumably to the external world. First, denotational, operational, game theoretic, and axiomatic semantics are epistemologically analysed to provide a theory of meaning for high-level language programs. It is argued that programming language semantics can be seen, to some extent, under the light of Fregean descriptivism, thereby rejecting the view that it is reference that determines meaning. Secondly, descriptivist, causal, character, and intentionalist theories of reference are examined to argue that meaning is not sufficient for programming languages to determine reference, thereby rejecting the view that meaning determines reference. It is argued that it is the intentions of agents that allow programming language semantics to identify referents. The paper concludes that if the semantics of programming languages may determine their reference, the latter is not to the external world but to the internal operations of the implementation executing them. This does not prevent simulative programs from representing empirical systems, but they do so not on the basis of programming language semantics.

Partial equivalence relations (and categories of these) are a standard tool in semantics of type theories and programming languages, since they often provide a cartesian closed category with extended definability. Using the theory of exact categories, we give a category-theoretic explanation of why the construction of a category of partial equivalence relations often produces a cartesian closed category. We show how several familiar examples of categories of partial equivalence relations fit into the general framework.

In this paper, we present a software module developed for the course Semantics of Programming Languages. This software will be part of the intended comprehensive software package to simplify and make the teaching of formal principles in theoretical computer science more attractive. During the pandemic, the need to support illustrative and illustrative online teaching increased. Our software is designed to illustrate and visualize the translation of a higher-level language into code for the Abstract Machine, the definition of which is based on the structural operational semantics of programs.

The concept of affordance, proposed by James J. Gibson as an opportunity for action offered by the environment to the organism, has been adopted in various fields, including psychology, neuroscience, and robotics. However, different interpretations exist as to whether it is a feature of a relation between the environment and the organism and therefore cannot exist independently of the organism, or a “resource" that exists in the environment independent of the organism's presence and is waiting to be used, or both, or neither. In this paper, we defend the position that affordances are both relational and resources using a category-theoretic approach. This idea is formalized by the concept of “natural transformations" in category theory, which are structure-preserving transformations between “functors" – mathematical expressions representing “seeing from a particular perspective." We propose that formalizing the realism of affordance in terms of natural transformations offers a more rigorous and lucid understanding of this concept. Furthermore, our formalization enables us to relate the reality of affordances to a broader context, especially the shift in the meaning of “reality" in modern physics. Our category-theoretic approach offers a potential solution to the problems and limitations associated with existing set theory-based frameworks for affordances, paving the way for a future theory that better accounts for the open-ended interplay between organisms and their environments.

In this paper, we formalize precisely the sense in which the application of a cellular automaton to partial configurations is a natural extension of its local transition function through the categorical notion of Kan extension. In fact, the two possible ways to do such an extension and the ingredients involved in their definition are related through Kan extensions in many ways. These relations provide additional links between computer science and category theory, and also give a new point of view on the famous Curtis–Hedlund theorem of cellular automata from the extended topological point of view provided by category theory. These links also allow to relatively easily generalize concepts pioneered by cellular automata to arbitrary kinds of possibly evolving spaces. No prior knowledge of category theory is assumed for the most part.

Mathematical Structures in Computer Science bridges the gap between theoretical computer science and software design. By publishing original perspectives from all areas of computing, the journal stresses applications from logic, algebra, geometry, category theory and other areas of logic and mathematics. Through issues such as this special issue, the journal also plans to play an occasional, but important role in the fields of intelligent computation and automation.

Preface: Volume 52, Issue 2

∗-Autonomous categories of bimodules