Looking at General Purpose Financial Reports Logically
Financial reports are knowledge graphs which convey the logic of information provided within that financial report. There is no natural way to represent an economic entity the way it “really is” in the real world; there are just certain purposeful selections of specific aspects of an economic entity, call them abstractions or models, that provide a useful enough simplification that satisfies some specific goal we might have. Past approaches to representing this logic included stone tablets, paper, and what amounts to "e-paper" (e.g. PDF, HTML). But now general purpose financial reports can be represented in machine-readable digital format.
A problem arises when a knowledge graph of financial report information is less capable masquerades as being more capable or fully capable knowledge graph of meaning. These less capable knowledge graphs tend to be incomplete representations of reported logic and/or logic represented is inconsistent with the logic of financial reporting and accounting rules. Professional accountants creating such representations, auditing logical representations created by others, or using such logical representations for analysis need to be able to distinguish between these less capable and more capable representations of meaning. The Seattle Method is a tool that I created that helps professional accountants think about XBRL-based digital financial reports. The Standard Business Report Model (SBRM) is a similar sort of tool.
A general-purpose financial report tells a story. That story is supposed to be a "true and fair" representation of information about an economic entity. A financial report is not the actual economic entity, it merely conveys fairly high-fidelity information about an economic entity that is generally of very high-quality and deemed to be good enough per some financial reporting scheme created by standards setters and/or regulators.
Think of a general-purpose financial report and think of the following:
Two economic entities, A and B, each have information about their financial position and financial performance. They must communicate their information to an investor who is making investment decisions which will make use of the combined information so as to draw some conclusions. All three parties (economic entity A, economic entity B, investor) are using a common set of basic logical principles (facts, logical statements, rules, deductive reasoning, inductive reasoning, etc.), common financial reporting standard concepts and relations (i.e. US GAAP, UK GAAP, IFRS, etc.), and a common world view so they should be able to communicate this information fully, so that any inferences which, say, the investor draws from economic entity A's information should also be derivable by economic entity A itself using basic logical principles, common financial reporting standards (terms, relations, rules), and common world view; and vice versa; and similarly for the investor and economic entity B.
That is the nature of a general-purpose financial report, to represent information about an economic entity for a specific purpose. That representation is good enough to be useful. Enabling a machine-readable representation of such general purpose financial report knowledge graphs is, in my view, not only a good idea but a necessary idea if you recognize that we are in the information age.
Knowledge is a form of familiarity with information from some specific area. 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. Professional accountants, auditors, and analysts understand all this for their area of knowledge, their industry specialty, for the financial reporting schemes with which they work.
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 logic of a 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.
Effectively, a machine can read that logic within a knowledge graph and mimic understanding of that knowledge represented in that logical 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. Regardless of the technical syntax used to represent that logical knowledge graph; the logic of the knowledge representation MUST always be the same.
Philosophy is a formal discipline which provides tools and techniques for the systematic study of specific things including knowledge and reasoning.
Logic is a tool provided by the discipline of philosophy. Logic is the study of correct reasoning. Logic uses artificial languages with a precise symbolic representation to investigate reasoning. The tools of logic which provide the foundation for mathematics are leveraged by computers to mimic tools previously available only to humans, opening up the possibility of machines literally mimicking an understanding of knowledge. These tools can perform deductive reasoning, inductive reasoning, and abductive reasoning. (Watch this video to understand the difference.) Tools such as ChatGPT-type knowledge copilots can leverage this rock solid grounding in logic for an area of knowledge and high quality machine readable financial reports. (See my oracle machine prototype here.)
A knowledge graph is a communications tool. What is represented by a knowledge graph is logical statements. Logical statements define terms, define structures, define associations, specify assertions/constraints/restrictions, and articulate fact. All of these are logical statements. Logical statements are put into machine readable form using graph theory and network theory. The result is a graph of knowledge, a.k.a. knowledge graph, a logical schema that enforces the logic expressed within that knowledge graph, and a high-level model that can be used by software to process the knowledge graph (rather than forcing business professionals to work with the graph hairball).
Those knowledge graphs can take on many different physical forms. There will very likely be hundreds of different types of knowledge graphs. Some knowledge graphs will be more formal than others. Financial report knowledge graphs will be more on the formal, professional side because they are important tools. (See professional knowledge graphs.)
We communicate using knowledge graphs all the time and tend to not realize it. When you go to a whiteboard and draw circles and squares and connect them with lines with arrows you are drawing a graph and communicating knowledge. Those circles, squares, lines, and arrows are intuitively understandable and very expressive. These informal knowledge graphs have been used by humans to communicate information for quite some time.
Most accountants are familiar with flowcharts. You can think of a knowledge graph as a type of flowchart that is readable by both humans and computers.
Many accountants today are becoming "data janitors" in an age of very poor "data hygiene". Basically, they are fighting the symptoms of a problem that exists. But there is another alternative. Accountants can become the freemasons of the information age and build out the tools that will be used to transition from our industrial economy to our information economy.
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