Digital Financial Reporting

The Data Coalition calls it "smart regulation". Others refer to it as "algorithmic regulation". Still others conjure up the same meaning using terms such as "robo cop".  I will use the term computational regulation which is consistent with computational law, computational audit, and computational economics.

By "computational" I mean able to be processed by a computer, a symbolic system.

So what exactly is computational regulation?  Well; rather than defining statutory and regulatory terms and rules only in documents that only humans can read and where intended meaning can be (and often is) ambiguous; terms and rules are also defined using machine-readable, machine-understandable, machine-interpretable standard formal models.

Because those standard formal models can be processed using computer software, the statutory and regulatory rules can be significantly less ambiguous so the intent of those rules is clear to those that must comply with those rules.  Because of this increased clarity compliance with rules an be tested which reduces risk of those being regulated.

And so how can these symbolic systems, this logic, be processed?  There are many different approaches (Here are details that explain exactly how).

The human imagination is incredibly powerful.  Albert Einstein pointed out, "Imagination is more important than knowledge."  Transformational moments in human history have come about when someone or some group of people were able to imagine a world the rest of us could not yet see.

XBRL International was formed in 1999 when a group of people imagined possibilities such as computational regulation.  Twenty years later far less imagination is necessary.  All the pieces are almost in place!

This is an interesting way to organize a taxonomy.

Shawn Riley posted excellent information that describes the four levels of interoperability. Here are those four levels: 

• Foundational (Level 1): Establishes the inter-connectivity requirements needed for one system or application to securely communicate data to and receive data from another.  
• Structural (Level 2): Defines the format, syntax and organization of data exchange including at the data field level for interpretation.
• Semantic (Level 3): Provides for common underlying models and codification of the data including the use of data elements with standardized definitions from publicly available value sets and coding vocabularies, providing shared understanding and meaning to the user.
• Organizational (Level 4): Includes governance, policy, social, legal and organizational considerations to facilitate the secure, seamless and timely communication and use of data both within and between organizations, entities and individuals. These components enable shared consent, trust and integrated end-user processes and workflows.

Here are more details related to interoperability (i.e. effective information exchanges).

The above interoperability model is consistent with an interoperability model used in health care.

So in prior posts I mentioned computational law and computational audit.

In this post I want to provide an example of computational economics which is another example of a symbolic system.  Here is a definition of computational economics.

I read a book over the weekend, The Deficit Myth, which explains Modern Monetary Theory (MMT).  MMT like other economic theories have models.  Here is one model from MMT: (from here)

(T-G) + (S-I) + (M-X) = 0

I took that MMT model and some other things from the book and represented in in XBRL.  Here is my first draft: 

• Human Readable
• Machine Readable XBRL instance
• Machine Readable XBRL taxonomy
• Machine Readable XBRL Formulas (Rules) (human readable validation result)
• Download files

Again, what I have right now is just a draft.  I want to do this for, say, multiple different governments like the US, UK, Japan.

What if these models and data were provided in machine readable form? What if the information format was standardized rather than being provided in Excel or CSV?  What if the rules where not embedded in Excel, but rather publicly available and usable across models?

I am going to build out my MMT model and put in real data in order to check out MMT to see if it makes sense.  So, stay tuned.

Stanford University has a unique undergraduate or graduate major offering called the Symbolic Systems Program.

So, what is a symbolic system? Per the associate director of the program when interviewed by The Stanford Daily:

“[The major is] a combination of studying the human mind … and the intelligence of machines and of the design interaction that happens between them, [as well as] how those things can inform each other,” said symbolic systems associate director Todd Davies ’84 M.S. ’85 Ph.D. ’95 in an interview with The Daily.  

A symbolic system is essentially a system built with symbols such as natural language, programming languages, mathematics, or formal logic. An interesting thing is that symbolic systems are understandable by both humans and by computers.

You can get a more detailed understanding of symbolic systems from the Stanford Bulletin which describes the course.  Cognitive science is somewhat similar to symbolic systems. Computational linguistics is also somewhat similar.

Why is this important?

In his book Saving Capitalism, Robert Reich describes (page 204-206) three categories that all modern work/jobs fit into: 

• Routine production services which entails repetitive tasks,
• In-person services where you physically have to be there because human touch was essential to the tasks, 
• Symbolic-analytic services which include problem solving, problem identification, and strategic thinking that go into the manipulation of symbols (data, words, oral and visual representations). 

In describing the third category, symbolic-analytic services, Mr. Reich elaborates:

“In essence this work is to rearrange abstract symbols using a variety of analytic and creative tools - mathematical algorithms, legal arguments, financial gimmicks, scientific principles, powerful words and phrases, visual patterns, psychological insights, and other techniques for solving conceptual puzzles. Such manipulations improve efficiency-accomplishing tasks more accurately and quickly-or they better entertain, amuse, inform, or fascinate the human mind.”

Think Computational Law and Computational Audit.  Many tasks in accounting, reporting, auditing, and analysis are related to symbolic-analytic services and rearranging abstract symbols. As I pointed out a while back, the "Learn to code" is a hysteria and is misguided.

The Essence of Accounting has a lot of information that will help you get your head around the accounting symbolic system.  The Logical Theory Describing Financial Report will help you learn about reports.  Understanding Digital helps tie accounting, reporting, auditing, and analysis together.

More advanced information is provided by Processing Logical Systems. One more thing worth checking out if you are interested in all this is Introduction to the Fact Ledger.

Or, if you want the "full meal deal" and want to work through all my best information methodically, deliberately, and rigorously; please see: Mastering XBRL-based Digital Financial Reporting.