In the ensuing decades, mathematicians began working with this new thing, the category, and this new idea, equivalence. In so doing they created a revolutionary new approach to mathematics, category theory, that many see as supplanting set theory. Imagine if writers had spent 150 years representing the world only through basic description: This is a red ball. That is a 60-foot tree. This is a dog. Then one day someone discovers metaphor. Suddenly, our ability to find new ways to represent the world explodes, as does our knowledge of writing as a discipline.

"If mathematics is the science of analogy, the study of patterns, then category theory is the study of patterns of mathematical thought."

The intermediary category contains three modes: Dorian, Locrian, and Locrian Ω2

[Zettel feedback] Functor (Yeah, just that)

Here we have extended this model to a slightly different category, a category where morphisms are represented by embellished functions, and their composition does more than just pass the output of one function to the input of another. We have one more degree of freedom to play with: the composition itself. It turns out that this is exactly the degree of freedom which makes it possible to give simple denotational semantics to programs that in imperative languages are traditionally implemented using side effects.

For our limited purposes, a Kleisli category has, as objects, the types of the underlying programming language. Morphisms from type A to type B are functions that go from A to a type derived from B using the particular embellishment. Each Kleisli category defines its own way of composing such morphisms, as well as the identity morphisms with respect to that composition.

There is just one little nit for mathematicians to pick: morphisms don’t have to form a set. In the world of categories there are things larger than sets. A category in which morphisms between any two objects form a set is called locally small.

It’s worth emphasizing that Haskell lets you express equality of functions, as in: mappend = (++) Conceptually, this is different than expressing the equality of values produced by functions, as in: mappend s1 s2 = (++) s1 s2 The former translates into equality of morphisms in the category Hask (or Set, if we ignore bottoms, which is the name for never-ending calculations). Such equations are not only more succinct, but can often be generalized to other categories. The latter is called extensional equality, and states the fact that for any two input strings, the outputs of mappend and (++) are the same. Since the values of arguments are sometimes called points (as in: the value of f at point x), this is called point-wise equality. Function equality without specifying the arguments is described as point-free. (Incidentally, point-free equations often involve composition of functions, which is symbolized by a point, so this might be a little confusing to the beginner.)

Is composition associative? Check!

Let’s characterize these ordered sets as categories. A preorder is a category where there is at most one morphism going from any object a to any object b. Another name for such a category is “thin.” A preorder is a thin category.

A set of morphisms from object a to object b in a category C is called a hom-set and is written as C(a, b) (or, sometimes, HomC(a, b)). So every hom-set in a preorder is either empty or a singleton. That includes the hom-set C(a, a), the set of morphisms from a to a, which must be a singleton, containing only the identity, in any preorder. You may, however, have cycles in a preorder. Cycles are forbidden in a partial order.

Chris, Chris... concepts and propositions are not nebulous dictionary definitions unless you're joining the Frankfurt School :) :).Click here to get some clarity about these basic terms as applied to learning: https://cmap.ihmc.us/docs/concept.phpAbout systems and emergence, I prefer Mario Bunge's book: https://www.amazon.com/Emergence-Convergence-Qualitative-Knowledge-Philosophy/dp/1442628219Irony? No way. You are always bringing new information about the historical roots of Zettelkasten. Keep doing that, please! Thanks!

Hypergraph Categories

for settling in a finite number of steps, whether a relevant object hasproperty P.Relatedly, the answer to a question Q is effectively decidable ifand only if there is an algorithm which gives the answer, again by adeterministic computation, in a finite number of steps.

okay so remind you what is a sheath so a sheep is something that allows me to 00:05:37 translate between physical sources or physical realms of data and physical regions so these are various 00:05:49 open sets or translation between them by taking a look at restrictions overlaps 00:06:02 and then inferring

i think so like in social terms the conservatives would say well i like that it benefits from the wisdom of math already invented you're not 00:36:39 throwing anything away you're not you're not throwing it all away and starting over you're taking what we already have and you're you're using it that's great and a libertarian might say i really like that you're free to create as you see fit you can make anything you 00:36:52 want and you're working within this background framework that's minimally invasive it doesn't make a lot of rules for you but it is highly functional i like that it kind of keeps everyone in line while 00:37:03 like satisfying some formal contracts or something while still being uh i'm still free to create and a progressive might say i like about category that theory that everyone can contribute to 00:37:15 making their own world making it more rich adding new ideas uh making it more meaningful understanding connections between things a modern viewpoint would say i like that 00:37:26 it's completely rigorous that it's been used in proving well-known conjectures that people thought were important to prove but also that it's interesting it's useful in science and technology and a postmodern person might say i like 00:37:40 that um that no perspective is right that that there's just all sorts of different categories but that navigating between these perspectives lets you look at problems from all sides or a hippie might say i like that it's 00:37:53 all about relationship and connection or irrelevant i don't know what that means maybe a practical person might say that i like that it's that we can actually use it to organize and learn from big data in 00:38:06 today's world or to manage complexity of software projects that are that are very large and changing all the time i like that you can think about ai and other complex systems with this stuff i think it's relevant and 00:38:19 practical for right now so that's that's my uh tutorial or that's the the part i'm going to record and now i'm going to open it up for questions

The CommunitySensor community network ontology can be positioned somewhere in the middle of this spectrum: community network representatives are totally free to come up with their own terms for element and connection types in their own ontologies. However, these terms are organized in a deep structure with community-specific element and connection types being classified by higher-order element and connection type (sub)categories described in the CommunitySensor community network conceptual model.

Figure 1. The relationship between models, documents, source code, and documentation.

In fact categories can themselves be viewed as type theories of a certain kind

Though rarer in computer science, one can use category theory directly, which defines a monad as a functor with two additional natural transformations. So to begin, a structure requires a higher-order function (or "functional") named map to qualify as a functor:

It's hard to say why people think so because you certainly don't need to know category theory for using them, just like you don't need it for, say, using functions.

In some sense, by studying one model deeply enough, we can study them all.

In category theory

categorical formalism should provide a much needed high level language for theory of computation, flexible enough to allow abstracting away the low level implementation details when they are irrelevant, or taking them into account when they are genuinely needed. A salient feature of the approach through monoidal categories is the formal graphical language of string diagrams, which supports visual reasoning about programs and computations. In the present paper, we provide a coalgebraic characterization of monoidal computer. It turns out that the availability of interpreters and specializers, that make a monoidal category into a monoidal computer, is equivalent with the existence of a *universal state space*, that carries a weakly final state machine for any pair of input and output types. Being able to program state machines in monoidal computers allows us to represent Turing machines, to capture their execution, count their steps, as well as, e.g., the memory cells that they use. The coalgebraic view of monoidal computer thus provides a convenient diagrammatic language for studying computability and complexity.

But there is an alternative. It’s called denotational semantics and it’s based on math. In denotational semantics every programing construct is given its mathematical interpretation. With that, if you want to prove a property of a program, you just prove a mathematical theorem

hierarchy of questions: "What about the relationships between the relationships between the relationships between the...?" This leads to infinity categories. [And a possible brain freeze.] For more, see here.)  As pie-in-the-sky as this may sound, these ideas---categories, functors, and natural transformations---lead to a treasure trove of theory that shows up almost everywhere.

“pulls it back”

Exercise1.75.Doesbù3chave a right adjointR:N!N? If not, why? If so, does itsright adjoint have a right adjoint?

Remark1.73.IfPandQare total orders andf:P!Qand1:Q!Pare drawn witharrows bending as in Exercise 1.72, we believe thatfis left adjoint to1iff the arrows donot cross. But we have not proved this, mainly because it is difficult to state precisely,and the total order case is not particularly general

The preservation of meets and joins, and hence whether a monotone map sustainsgenerative effects, is tightly related to the concept of a Galois connection, or moregenerally an adjunction.

Galois connections between posets were first considered by Évariste Galois—whodidn’t call them by that name—in the context of a connection he found between “fieldextensions” and “automorphism groups”. We will not discuss this further,

In his work on generative effects, Adam restricts his attention to maps that preservemeets, even while they do not preserve joins. The preservation of meets implies that themapbehaves well when restricting to a subsystem, even if it can throw up surpriseswhen joining systems

n [Ada17], Adam thinks of monotone maps as observations. A monotone map:P!Qis a phenomenon ofPas observed byQ. He defines generative effects of such a mapto be its failure to preserve joins (or more generally, for categories, its failure topreserve colimits)

Example1.61.Consider the two-element setPfp;q;rgwith the discrete ordering.The setAfp;qgdoes not have a join inPbecause ifxwas a join, we would needpxandqx, and there is no such elementx.Example1.62.In any posetP, we havep_pp^pp.Example1.63.In a power set, the meet of a collection of subsets is their intersection,while the join is their union. This justifies the terminology.Example1.64.In a total order, the meet of a set is its infimum, while the join of a set isits supremum.Exercise1.65.Recall the division ordering onNfrom Example 1.29: we say thatnmifndivides perfectly intom. What is the meet of two numbers in this poset? Whatabout the join?

These notions will have correlates in category theory, called limits and colimits,which we will discuss in the Chapter 3. For now, we want to make the definition ofgreatest lower bounds and least upper bounds, called meets and joins, precise.

Ifxyandyx, we writexyand sayxandyareequivalent. We call a set with a preorder aposet.

Example1.49.Recall from Example 1.36 that given a setXwe defineEXto be theset of partitions onX, and that a partition may be defined using a surjective functions:XPfor some setP.Any surjective functionf:X!Yinduces a monotone mapf:EY! EX, going“backwards”. It is defined by sending a partitions:YPto the compositef:s:XP

Example1.42 (Opposite poset).Given a posetπP;∫, we may define the opposite posetπP;op∫to have the same set of elements, but withpopqif and only ifqp.

Example1.40 (Product poset).Given posetsπP;∫andπQ;∫, we may define a posetstructure on the product setPQby settingπp;q∫  πp0;q0∫if and only ifpp0andqq0. We call this theproduct poset. This is a basic example of a more generalconstruction known as the product of categories

Contrary to the definition we’ve chosen, the term poset frequently is used to meanpartiallyordered set, rather than preordered set. In category theory terminology, therequirement thatxyimpliesxyis known asskeletality. We thus call partiallyordered setsskeletal posets

Lemma 2.5.1.14

Since ducks can both swim and fly, each duck is found twice inC, once labeled as aflyer and once labeled as a swimmer. The typesAandBare kept disjoint inC, whichjustifies the name “disjoint union.”

facts, which are simply “path equivalences” in an olog. It isthe notion of path equivalences that make category theory so powerful.Apathin an olog is a head-to-tail sequence of arrows

Consider the aspectpan objectqhas››››—pa weightq. At some point in history, thiswould have been considered a valid function. Now we know that the same objectwould have a different weight on the moon than it has on earth. Thus as world-views change, we often need to add more information to our olog. Even the validityofpan object on earthqhas››››—pa weightqis questionable. However to build a modelwe need to choose a level of granularity and try to stay within it, or the whole modelevaporates into the nothingness of truth!

An aspect of a thingxis a way of viewing it, a particular way in whichxcan be regardedor measured. For example, a woman can be regarded as a person; hence “being a person”is an aspect of a woman. A molecule has a molecular mass (say in daltons), so “havinga molecular mass” is an aspect of a molecule. In other words, byaspectwe simply meana function. The domainAof the functionf:A—Bis the thing we are measuring, andthe codomain is the set of possible “answers” or results of the measurement.

We represent eachtype as a box containing asingular indefinite noun phrase.

Data gathering is ubiquitous in science. Giant databases are currently being minedfor unknown patterns, but in fact there are many (many) known patterns that simplyhave not been catalogued. Consider the well-known case of medical records. A patient’smedical history is often known by various individual doctor-offices but quite inadequatelyshared between them. Sharing medical records often means faxing a hand-written noteor a filled-in house-created form between offices.

As mentioned above category theory has branched out into certain areas of scienceas well. Baez and Dolan have shown its value in making sense of quantum physics, itis well established in computer science, and it has found proponents in several otherfields as well. But to my mind, we are the very beginning of its venture into scientificmethodology. Category theory was invented as a bridge and it will continue to serve inthat role.

All this time, however, category theory was consistently seen by much of the mathe-matical community as ridiculously abstract. But in the 21st century it has finally cometo find healthy respect within the larger community of pure mathematics. It is the lan-guage of choice for graduate-level algebra and topology courses, and in my opinion willcontinue to establish itself as the basic framework in which mathematics is done

In 1980 Joachim Lambek showed that the types and programs used in computerscience form a specific kind of category. This provided a new semantics for talking aboutprograms, allowing people to investigate how programs combine and compose to createother programs, without caring about the specifics of implementation. Eugenio Moggibrought the category theoretic notion of monads into computer science to encapsulateideas that up to that point were considered outside the realm of such theory.

Bill Lawvere saw category theory as a new foundation for all mathematical thought.Mathematicians had been searching for foundations in the 19th century and were reason-ably satisfied with set theory asthe foundation. But Lawvere showed that the categoryof sets is simply a category with certain nice properties, not necessarily the center ofthe mathematical universe. He explained how whole algebraic theories can be viewedas examples of a single system. He and others went on to show that higher order logicwas beautifully captured in the setting of category theory (more specifically toposes).It is here also that Grothendieck and his school worked out major results in algebraicgeometry.

The paradigm shift brought on by Einstein’s theory of relativity brought on the real-ization that there is no single perspective from which to view the world. There is nobackground framework that we need to find; there are infinitely many different frame-works and perspectives, and the real power lies in being able to translate between them.It is in this historical context that category theory got its start.

These theorems have not made theirway out into the world of science, but they are directly applicable there. Hierarchies arepartial orders, symmetries are group elements, data models are categories, agent actionsare monoid actions, local-to-global principles are sheaves, self-similarity is modeled byoperads, context can be modeled by monads.

No one would dispute that vector spaces are ubiquitous.But so are hierarchies, symmetries, actions of agents on objects, data models, globalbehavior emerging as the aggregate of local behavior, self-similarity, and the effect ofmethodological context.

I will use a mathematical tool calledologs, or ontology logs, to givesome structure to the kinds of ideas that are often communicated in pictures like theone on the cover. Each olog inherently offers a framework in which to record data aboutthe subject. More precisely it encompasses adatabase schema, which means a system ofinterconnected tables that are initially empty but into which data can be entered.

Agreementis the good stuff in science; it’s the high fives.But it is easy to think we’re in agreement, when really we’re not. Modeling ourthoughts on heuristics and pictures may be convenient for quick travel down the road,but we’re liable to miss our turnoff at the first mile. The danger is in mistaking ourconvenient conceptualizations for what’s actually there. It is imperative that we havethe ability at any time to ground out in reality.