Modeling System Dynamics

Getting artificial intelligence to work effectively involves understanding how computers work (engineering) and understanding how an area of knowledge works (sensemaking). It also involves communicating detailed information using an approach that is approachable to non-technical business professionals.  In my view, that approach is logic. Business professionals have an innate understanding of logic.  Logic can be used to create a logical conceptualization that can be described using a logical theory which can be enforced using a logical schema to keep semantic hygiene of a global standard knowledge assembly of the logical system (systems theory) where it needs to be (sigma level 6). Complexity must be embraced.  There are no short cuts.  Full stop.

An area of knowledge is a highly organized socially constructed aggregation of shared knowledge for a distinct subject matter.  An area of knowledge has a specialized insider vocabulary or “jargon”, underlying assumptions (axioms, theorems, constraints, restrictions, assertions), and persistent open questions that have not necessarily been resolved (i.e. flexibility is necessary, system changes can occur).


Accounting is an area of knowledge.  You can explain aspects of the accounting area of knowledge, such as the nature of a financial report, using a logical theory which explains a logical model.  A logical theory can be tested and proven by providing a proof. When all the details are worked out, you have a best practice based proven method.

Knowledge can be represented in human-readable form, in machine-readable form, or in a machine-readable form that can be effectively converted into human-readable form. Representing the core knowledge of an area of knowledge in machine readable form takes skilled and experienced members of that area of knowledge. That core knowledge can then serve as training data that helps machine based processes to create even more useful knowledge. Maximizing the perceiving, learning, abstracting, and reasoning takes a lot of hard work.

You can think about an area of knowledge as being characterized in a spectrum with two extremes: (Introduction to Sensemaking; The Cynefin Framework; Complexity)

·         Kind area of knowledge: clear information, clear rules, lots of patterns, lots of rules, repetitive patterns, and typically unchanging tasks. (good practice, best practice)

·         Wicked area of knowledge: obscure data, few or no rules, constantly changing tasks, and abstract ideas. (emergent practice, novel practice)

An area of knowledge can have aspects of both extremes, but tends to lean toward one side of the spectrum or the other.

Financial reporting is a rich collection of knowledge developed over hundreds of years. Financial accounting and reporting tend to lean more toward the “kind” end in many ways, particularly the quantitative aspects of accounting and reporting.  The qualitative aspects may more in the wicked side of the spectrum.

There is “pressure” that pushes an area of knowledge in both directions.  Some system stakeholders tend to like clarity which allows the system stakeholders to effectively achieve what the system is trying to achieve. “Gaming the system” is minimized by the stakeholders of the system to optimize the functioning of the system, satisfying the purpose of the system. 

Others focus on “gaming the system” or “spinning” things to take advantage of “cracks” or “flaws” in the system. 

For example, things like picking alternatives that provide for the most favorable tax position, favorable impact on the perception of a company in the stock market. 

This “gaming the system” or “spin” is similar to arbitrage; taking advantage of the “slack” or “tolerance” or “cracks” in the system to satisfy one participant’s specific self-interests.

This system of financial accounting and reporting is not natural, it is a man-made logical system.  Because the system is made by man, it is not perfect.  How you perceive the system can be impacted by the lens used to view the logical system. 

Think of the logical system as if it were a game.  Every game has rules.  When you play the game, you can have the perspective of whether you are following the rules (right/wrong) or whether you are achieving the goal of the system (win/lose).  The self interest of the system stakeholder can influence one’s perspective.

Keeping the dynamics which impact the logical system as clear as possible helps one understand the moving pieces of the logical system.  One needs to be able to differentiate unintended ambiguity that exists within such a logical system and the intensions of the stakeholders of the system.  Making these moving puzzle pieces clear helps one understand the system better.

A system has describable patterns of behavior.  A system can be describe using principles and/or a system can be described by rules.  The principles and rules should not contradict one another.

Confusing the system dynamics makes it much more difficult to model the dynamics of the system.  Sensemaking is the process of determining the deeper meaning or significance or essence of the collective experience for those within an area of knowledge.  Sensemaking is the process of understanding system dynamics so that the system dynamics can be modeled per the objectives of the stakeholders of the system.  Sensemaking allows you to untangle a system.

A logical theory enables a community of stakeholders trying to achieve a specific goal or objective or a range of goals/objectives with some logical system to agree on important logical statements used for capturing meaning or representing a shared understanding of and collection of knowledge in some area of knowledge.

XBRL-based financial reporting per the Seattle Method is an example of what is possible.  OMG’s Standard Business Report Model (SBRM) could take this to an entirely new level. The Financial Data Transparency Act of 2022 is a massive opportunity in this space. More generally, XBRL + SBRM = an Improved, enhanced BI. Think semantic spreadsheet, a more modern spreadsheet tool.

Artificial intelligence (AI) PLUS human intelligence (HI) equals transformation. XBRL and SBRM will lead to The Great Transmutation of financial accounting, reporting, auditing, and analysis. Just like the transformation from drafting to CAD/CAM and BIM; financial reporting will be transformed.

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