Information Exchange Dynamics (Brainstorming)
In this blog post I am inventorying the important things that impact information exchange which I can later reassemble and synthesize. Much of this information and thinking is inspired by work of Sean McGrath.
As Steve Jobs pointed out, there needs to be a proper blending of technology and liberal arts. This applies to hardware, operating systems, and software applications.
There are two types of communication: direct and mediated. Direct communication is one person having a conversation with another person; if there is a question about information context, those involved in the communication can directly discuss and resolve any context issues. Mediated communication is indirect and resolving context issues can be more challenging because parties involved in the communication cannot really have a discussion about the immediate context.
Vagueness is the Enemy
In an email Sean McGrath said, “Vagueness is the enemy of automation.” I would extend that statement to include ambiguity (perhaps a type of vagueness) and quality issues (inaccuracy). For effective communication to occur, the threat of vagueness, ambiguity, and inaccuracy must be eliminated.
Vagueness is endemic in financial reporting. Some vagueness might be intentional, other vagueness is unconscious and unintentional. What is desirable and what is not desirable should be clear. Eliminating all vagueness and ambiguity may or may not be possible.
Role of Corpus (Financial reporting scheme)
A financial reporting scheme is a legal corpus or body of knowledge that must be managed. That legal corpus of knowledge eliminates the “wild behavior” of accountants creating financial reports and recording business events as accounting transactions. That legal corpus allows for precision, comparability, and repeatability:
- Description/specification of the terms, associations, structures, assertions, restrictions, constraints in the corpus of knowledge by a standards setter, regulator, or someone else creating a reporting scheme; the corpus could be human readable, machine readable, or preferably both human and machine readable
- Create/construction of a report that is consistent with the rules of that corpus of knowledge and rules using traditional or more modern approaches such as model-based report creation/construction using an expert system leveraging the machine readable description/specification
- Verify/verification that the report created/constructed has been done so consistent with what is permitted by the description/specification using that machine readable corpus of knowledge and rules
- Extract/analyze information from report using software and the description/specification provided by the machine readable corpus or knowledge and rules
The extent to which this is possible provides information about the adequateness or inadequateness of the description/specification.
Sensemaking
Knowledge is a form of familiarity with information from some specific area or corpus. Knowledge is often understood to be awareness of facts, having learned skills, or having gained experience using the things and the state of affairs (situations) within some area of knowledge. An area of knowledge (corpus) is a highly organized socially constructed aggregation of shared knowledge for a distinct subject matter. An area of knowledge has a specialized insider vocabulary, underlying assumptions (axioms, theorems, constraints), and persistent open questions that have not necessarily been resolved (i.e. flexibility is necessary). You can think about an area of knowledge as being characterized in a spectrum with two extremes:
- Kind area of knowledge: clear rules, lots of patterns, lots of rules, repetitive patterns, and unchanging tasks.
- Wicked area of knowledge: obscure data, few or no rules, constant change, and abstract ideas.
Sensemaking is the process of determining the deeper meaning or significance or essence of the collective experience for those within an area of knowledge or corpus. System stakeholders need to be in agreement as to an undisputed core knowledge of a system. The Cynefin Framework provides a tool for understanding and categorizing knowledge and rules within a corpus. Per the Cynefin Framework, knowledge can be categorized as being:
- Best practice (obvious)
- Good practice (only obvious if you have the right skills and experience)
- Emergent practice (tend to have to have more skills and experience, then can use principles to group alternatives)
- Novel practice (tends to be unique, but describable)
Knowledge of facts is distinct from opinion or guesswork by virtue of justification or proof. Knowledge is objective. Opinions and guesswork are subjective. In our case we are talking about certain specific knowledge, the facts that make up that knowledge, being able to create a proof to show the knowledge graph system is complete, consistent, and precise; and all of this logic being put into a form readable by a machine and reach a conclusion as to whether the information in the knowledge graph is functioning properly. Effectively, a machine can read that knowledge and mimic understanding of that knowledge represented in a knowledge graph and the information available to both a human reader and a machine reader would be the same and therefore the human and machine should reach the same conclusion.
Non-complex systems are computable. Intelligence should be clearly defined as not to cause unnecessary confusion. Understandability and how to achieve it should also be clear in one’s mind.
Modern Digital Governance
Per. Law and algorithms in the public domain, there is a spectrum of approaches to upgrading from traditional corpus documentation and management, each alternative with a basket of pros and cons. See. The section Changed Legislative Model?
The article, What lawyers need to learn from accountants, helps one understand how to use modern approaches to managing a corpus of knowledge and rules like a financial reporting scheme. Things like eliminating undesired vagueness, formal processes for documentation of changes, leveraging blockchain based immutable digital distributed ledgers to make authority and authenticity crystal clear, etc.
Things like workflow automation are a result of or consequence of the capability to remove inaccuracies from input information. As is said, “Garbage in, garbage out,” If you can’t trust the input or the process; then how can you trust the output?
Clarity, Transparency, Avoiding Obscure Rules
Accounting is about accountability. Making any part of a system a “black box” serves no one but those trying to undermine or game the system.
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