#MBO #logic #logic_model The [[Logic Model]] is a versatile framework for capturing the essence of a project or initiative, representing its underlying causal relationships within a structured format. Drawing inspiration from [[Robert Harper]]'s [[Computational Trinitarianism]]/([[Space-time-value machine]]), we can view the Logic Model through the lens of three interconnected categories: Abstract Specification, Concrete Implementation, and Operational Data. **1. Abstract Specification (Logic):** - **Context:** This defines the problem space and the boundaries within which the project operates. It provides the logical foundation upon which the entire model rests, akin to the preconditions in a Hoare triple. - **Goal:** This outlines the desired outcomes and objectives of the project, serving as the command or transformation to be achieved. - **Success Criteria:** These are the specific, measurable conditions that must be met to declare the project successful, acting as the postcondition in the Hoare triple. **2. Concrete Implementation (Languages):** - **Inputs:** These are the resources, activities, and interventions that are employed to bring about the desired change. They represent the actionable steps taken to move the project forward. - **Process:** This describes the sequence and interaction of activities, highlighting how inputs are transformed into outputs. It can be viewed as the operational semantics of the project, defining how actions are executed. - **Outputs:** These are the direct and tangible results produced by the activities and processes. They can be measured and tracked to assess the progress of the project. **3. Realistic Expectations (Categories):** - **Practical Boundaries:** This category captures the boundary conditions under which the project is expected to operate. It includes factors like available resources, external constraints, and potential risks. The meaning of expectations can be encoded using multi-dimensional vectors. (see [[Word2Vec]]) - **Evaluation Data:** This encompasses the data collected during the project implementation, used to assess the effectiveness of the activities and their impact on achieving the desired outcomes. - **Feedback Loops:** These are mechanisms for incorporating learnings from the evaluation data back into the model, facilitating continuous improvement and adaptation. By incorporating the principles of [[Computational Trinitarianism]]/[[Curry-Howard-Lambek isomorphism]]/[[Miner-Trader-Coder Triad]], the Logic Model becomes a powerful tool for not only mapping causal relationships but also for understanding the dynamic interplay between the abstract, concrete, and operational aspects of a project. The use of [[cubical type theory]] can further enhance this representation by capturing the higher-dimensional dependencies between inputs, processes, and outputs, allowing for a more nuanced understanding of how different elements of the project interact over time. In conclusion, the Logic Model, when viewed through the lens of Computational Trinitarianism and Cubical Type Theory, provides a comprehensive framework for planning, implementing, and evaluating projects, ensuring that they are grounded in sound logic, implemented effectively, and continuously optimized based on real-world data. ## A Sample Logic Model Form A visual illustration of [[Logic Model]] is shown below, a reusable Markdown template for this form is available as [[LMTemplate]] and [[Logic Model Generating Prompt]] for [[Obsidian]]. ![[LogicModel_SampleForm.png]] ## The Logic Model's Structure (A Computational Trinity) A logic model typically consists of seven components, mirroring the **[[Computational Trinitarianism]]** of Logic, Languages, and Categories: 1. **[[Context in Logic Model|Context]] (Precondition):** Defines the spatial and temporal environment, setting the stage (Logic). 2. **[[Goal in Logic Model|Goals/Objectives]] (Command):** Broad statements of desired long-term achievements (Languages). 3. **[[Success Criteria]] (Postcondition):** Specific, measurable outcomes resulting from program outputs (Logic). 4. **[[Inputs]]/Resources (Precondition):** Assets (funding, personnel, etc.) dedicated to program implementation (Languages). 5. **[[Activities]] (Command):** Actions taken using resources to produce desired outputs (Languages). 6. **[[Outputs]] (Postcondition):** Direct, measurable products or services resulting from activities (Logic). 7. **[[Realistic Expectations]] (Boundary Conditions):** Statements defining realistic expectations and capturing ongoing experiences, formerly known as **[[Boundary Conditions]]** (Categories). These fields can be expressed in natural or formal languages ([[Gherkin]], [[Zencode]]) akin to [[Behavior-driven development]] ([[BDD]]) and [[Ansible]]. ## Grokking Causation in PKCs Personal Knowledge Containers ([[PKC|PKCs]]) revolutionize note-taking by integrating data encryption, Large Language Models (LLMs), and the Logic Model. Like [[Activity-based Cost Accounting]] ([[ABC Accounting]]) transformed accounting, the Logic Model within PKCs captures causal relationships at the "speed of thought," mirroring complex system dynamics. See [[Grok]]. ### Recording Causations at the Speed of Thought This approach empowers users and the data infrastructure: - **[[Causality]] at the Core:** The Logic Model documents events and their interconnectedness, enabling deep understanding of "why," not just "what." - **Data Integrity:** Encryption secures PKC contents, while LLMs aid organization, turning data into reliable evidence. - **Unified Framework:** The Logic Model provides a consistent structure, fostering a holistic view of knowledge. Thus, PKCs evolve from static notes to dynamic knowledge ecosystems, facilitating the seamless capture of "why" alongside "what." ### Logic Model as Executable Atomic Notes We implement Logic Models as [[Executable Atomic Note|Executable Atomic Notes]] or [[Atomic Essay|Atomic Essays]], based on the [[File]] data abstraction (Unix), enriched with metadata. Each change triggers an event recorded on a blockchain, ensuring integrity. ## Logic Models as a Grokking Tool The Logic Model aids stakeholders in [[Grok|grokking]] a program's theory of change, identifying areas for improvement, and assessing effectiveness. By mapping logic and assumptions, it simplifies measuring progress and making evidence-based decisions. ### Front End to the Logical Kernel: Bridging Abstract and Concrete The Logic Model serves as an interface to an executable logic kernel ([[bridgelet]]). To address the human tendency to lose focus, it provides a project-centric view, binding users to essential data. Two parallel boxes capture connections between the abstract (Context, Goal, Success Criteria) and the concrete (Inputs, Process, Outputs), forming [[Hoare triple|Hoare triples]] (vertical and horizontal compositions in [[Category Theory]]). ### Grokking with Generative AI and Deep Reasoning **Generative AI** assists in filling gaps, suggesting outcomes, identifying missing steps, or generating alternative scenarios within the Logic Model. **Deep Reasoning** analyzes relationships, uncovering patterns, predicting roadblocks, and offering optimization insights. ### Realistic Expectations (Boundary Conditions) Realistic Expectations, formerly Boundary Conditions, are statements defining pre- and post-conditions, crucial for consensus and viewed as [[Yoneda Embedding|Yoneda Embeddings]] in [[Category Theory]]. The **[[Logic Kernel]]**, implemented using PKCs, interprets and executes logic models. This approach, combined with the computational trinitarianism framework, enables a deeper, more intuitive understanding ([[Grok|grokking]]) of complex systems and causal relationships. # References ```dataview Table title as Title, authors as Authors where contains(subject, "Logic Model") or contains(subject, "Atomic Note") or contains(subject, "Smart Contract") or contains(subject, "Agentic Trinitarianism") sort title, authors, modified ```