Creating a Deductive Apparatus using XBRL

A model-based financial report created using the Seattle Method or the forthcoming Standard Business Report Model (SBRM) is a deductive apparatus.

A deductive apparatus (a.k.a. formal system, axiomatic system, formal theory) is a formally specified, well-formed, and completely described system that is understandable by both humans and by machines.  A deductive apparatus is a tool.  That tool helps the user of that system to draw correct conclusions using that system. A deductive apparatus is a problem solving system.

A well-formed system is a system that adheres to specific rules, standards, or conventions. This ensures consistency, predictability, and interoperability.

Effective automation (i.e. human-task performance), such as algorithmic regulation, can be achieved using a well-formed and completely described deductive apparatus. Such a deductive apparatus consists of a particular set of axioms, a set of inference rules that enable derivation of theorems from the provided set of axioms, and a set of assertions that any axiom or theorem within the formal system must not contradict. These axioms, inference rules, derived theorems, and  assertions provide formal provability (formal proof, formal verifiability) within the specific provided boundaries of the well-formed system per incompleteness theorems and refutation proofs.

To be clear; consistency, predictability, and interoperability relate to information within the system to the extent that rules exist that can be systematically provided to prove that specific system.  No system can provide a guarantee that every conceivable rule (i.e. even those rules not provided) or prove the system is absolutely correct within every possible context (i.e. within every possible other external systems). There are limits. But with care, useful deductive apparatus can be effectively created and used successfully to automate tasks and practical, useful model-based financial reports can be created.

My PROOF and SUPER PROOF are examples of the capabilities of a deductive apparatus specified by the Seattle Method.  A more full set of capabilities can be seen in the Showcase of Reports.

Auditchain's Luca is an existing cloud-based software application that enables the creation of such a deductive apparatus. Auditchain Pacioli is a SWI-Prolog based engine that can prove that a deductive apparatus is well-formed syntactically and semantically and properly functioning. XBRL is a global standard technical format for creating and exchanging such a deductive apparatus.

Additional Information:


Comments

Post a Comment

Popular posts from this blog

Getting Started with Auditchain Luca (now called Luca Suite)

Image
(Note that a newer, production version as opposed to only the development version , of Luca Suite is available) The Innovator's Dilemma points out that disruptive innovations often look like toys that are doing everything wrong. Auditchain Luca , which is part of the Auditchain Suite , is a unique approach to creating a knowledge graph .  To experience using Auditchain Luca, have a look at the Getting Started with Auditchain Luca instructions. Be sure to grab the Excel and JSON import files used for this getting started exercise.   This getting started exercise uses one of several different approaches to getting information into a tool for constructing and viewing professional quality knowledge graphs .  Additional getting started guides will be made available over time. Auditchain Luca might not make sense to you if you don't understand the paradigm shift that is occurring and if you have not shifted your mental map. For the next step in your journey, consider t...
Read more

Relational Knowledge Graph System (RKGS)

Image
Relational Knowledge Graph System (RKGS) is an interesting idea that combines the relational paradigm and the knowledge graph paradigm.  This documentation, Why RKGS , explains the idea. This gets even more interesting with the notion of "relational AI".  RelationalAI is described as follows by the company of the same name, Relational.ai : “RelationalAI is a cloud-based relational knowledge graph management system, with state of the art probabilistic processing and declarative reasoning at scale to make developing Data Applications a superpower for your business.” This notion of knowledge graphs represented in relational databases fits into my belief that knowledge graphs have two aspects: the expression SYNTAX and the logic of what is expressed or SEMANTICS. While the LOGIC of a knowledge graph is always the same regardless of technical syntax used to express that logic; there are many, many different technical syntaxes that can be used to represent a knowledge graph. There...
Read more

Professional System for Creating Financial Reports Leveraging Knowledge Graphs

Image
The document Professional System for Creating Financial Reports Leveraging Knowledge Graphs explains a new approach, a new paradigm, for creating financial reports.  The document provides a practical, nontechnical description of a professional system for creating provably high-quality financial reports where the report creator is using financial reporting schemes (such as US GAAP and IFRS) where report creator is permitted to modify the report model which leverages global standard knowledge graphs represented using XBRL. This new approach is useful, it is highly novel, and the details were highly complicated to pull together.  This is certainly not an incremental innovation.  It is definitely a disruptive innovation and it could very well be a foundational innovation. You can decide which for yourself. Foundational technologies have two dimensions that need to be considered: novelty and complexity. The more novel the technology is, the more effort needed to ensure users ...
Read more