I often find myself musing about religions and belief systems: what is professed by a church versus what its individuals actually believe, how religions split into sects, borrow ideas from each other, and how doctrines evolve over time.

As I’ve been reading Building SimCity: How to Put the World in a Machine, I felt inspired to try my hand at creating a simulation game of my own. Naturally, I started with what I enjoy most: building a data model for simulating the relationships and histories of religions and faiths.

While I haven’t actually built the simulation yet (and possibly never will), the data modeling exercise was a lot of fun, and I wanted to share my progress and thoughts.

The Idea

This project began as a data modeling exercise with the hope of turning it into a simulation game.

There are plenty of simulations out there, but I wanted to create something that reflects my personal interests. I also wanted to focus on the data-modeling aspect, using tools like Django and SQL, rather than getting caught up in graphics or animations.

What came out of this effort is a data model designed to simulate the relationships and histories of religions and faiths.

The idea captivated me because it delves into the complexity of human connections with belief systems, organizations, and communities—and how these relationships shift and evolve over time.

The Result - A UML File

I didn’t dedicate a lot of time to this project, but I’m satisfied with the progress I made on the data model. If inspiration strikes later, I might extend it into a UI, game, or full simulation. For now, my focus was on enjoying the process and documenting my thoughts.

UML Diagram

See UML: https://github.com/david-wolgemuth/religion-sim-data-model/blob/main/data-model.puml

The model centers around key entities like individuals, doctrines (abstract ideas or principles), organizations, faiths (systems of doctrines), and locations.

Relationships between these entities are represented using join tables, which include attributes like:

  • Sentiment: Captures the strength of alignment (e.g., strongly agree, neutral, strongly disagree).
  • Weight: Represents the relative importance of a connection (e.g., an individual’s affiliation with multiple organizations).

I also would plan to incorporate django-simple-history to track dynamic relationships and historical changes across all entities.

Key Features of the Data Model:

  • Core Entities: Individuals, doctrines, organizations, faiths, and locations.
  • Dynamic Relationships: Tracks nuanced connections, such as individual beliefs or organizational affiliations.
  • Historical Tracking: Uses django-simple-history for versioning and event tracking.

The Process

Initial Concept

I wanted to model the following:

  • Faiths: Representing religions, cults, and sects as Faith entities.
  • Individuals: Each person holds their own beliefs, belongs to multiple organizations, and interacts with locations. I chose Individual to avoid pluralization quirks like Person/People.
  • Doctrines: Abstract principles that both individuals and faiths can align with, even when they don’t fully overlap.
  • Organizations: Groups structured around shared doctrines or faiths. These can span multiple locations and sometimes bridge different faiths.
  • Events: To track how relationships and histories evolve over time.

Tools & Setup

I started with Django for its ORM and natural fit for relational databases. As the project shifted toward pure data modeling, I began using PlantUML to visualize relationships. PlantUML stood out for being:

  • Keyboard-friendly (i’m a keyboard / vim guy).
  • Easy to integrate with version control.
  • Simple to share and refine with AI chat tools.

This project also became an experiment in leveraging AI for productivity. At each step, I used AI chat to refine my ideas, copy-pasting questions and answers into markdown files. I iterated by editing and re-submitting these responses until the data model felt complete. (see the end of this post for a peek into this process).

Challenges

  • Balancing Complexity: Adding detailed attributes like sentiment while ensuring the model stayed efficient and maintainable.
  • Capturing Dynamic Relationships: Designing flexibility for relationships like “disagreeing” with a belief without introducing unnecessary complexity.
  • Tracking Events: Evaluating different approaches to aggregate historical data (e.g., SQL views, centralized logs, or polymorphic models).

Reflection & Next Steps

I’m pleased with the data model I’ve built. It feels like a solid foundation for a simulation game. If I continue, my next steps would include:

  • Building a functional simulation.
  • Adding analytics or visualizations to track how relationships evolve over time.

Even if this project stays a data-modeling exercise, the process was rewarding. It sharpened my skills and gave me a creative outlet. I hope you found reading about it as enjoyable as I found building it!

Bonus: Using AI for Data Model iteration and Blog Post writing

This post and the data model were crafted with the help of AI chat tools. I used AI to:

  • Refine ideas and brainstorm solutions for dynamic relationships and event tracking.
  • Generate initial drafts for this blog post and iterate on the writing process.

See the chats and iteration as I was modeling: https://github.com/david-wolgemuth/religion-sim-data-model/tree/main/docs/chatlog

I’ve also included key parts of the chats specific to writing this blog post below:

what are the best ways to outline a blog post going over a project idea i had and worked on for a few hours? Outlining a blog post about a project idea you had and worked on for a few hours should balance personal storytelling with actionable insights. Here’s a structured approach to outline your post: 1. Introduction • Hook: Start with an engaging question, anecdote, or statement that connects the reader to your inspiration for the project. • Context: Briefly describe the origin of the idea. What inspired you to start this project? • Preview: Give a quick overview of what the post will cover (e.g., the idea, how you tackled it, challenges, results, and takeaways). 2. The Idea • What: Describe the idea in detail. What problem does it aim to solve, or what opportunity does it explore? • Why: Explain why you found this idea compelling. Why was it worth a few hours of your time? • Scope: Define the limits you set for yourself in this project. 3. The Process • Tools & Setup: Mention the tools, frameworks, or resources you used and why you chose them. • Steps Taken: Outline the major steps you followed to bring the idea to life. • Example: 1. Brainstormed features. 2. Created a quick prototype. 3. Tested functionality. • Challenges: Highlight any roadblocks or surprising discoveries along the way. • Time Spent: Briefly discuss how much time you dedicated and how you managed it. 4. The Result • Outcome: Share what you built, even if it’s incomplete or a proof of concept. • Demo or Visuals: If possible, include screenshots, code snippets, or even a short video to show off your work. • Unexpected Insights: Talk about anything you learned or realized that wasn’t part of the original plan. 5. Reflection • What Worked: Discuss what went well and what you’re proud of. • What Didn’t: Be honest about the limitations or aspects that didn’t meet your expectations. • Lessons Learned: Share the takeaways that could help others attempting similar projects. 6. Next Steps • Future Possibilities: If you were to expand this project, what would you add or change? • Opportunities for Others: Invite readers to contribute, improve, or adapt the idea. 7. Conclusion • Recap: Summarize the journey in one or two sentences. • Call to Action: Encourage readers to try something similar, share feedback, or discuss related ideas. 8. Optional Extras • Links: Provide links to tools, repositories, or resources you mentioned. • Code: Include a GitHub link or embed code snippets for readers to explore. • Credits: Acknowledge any resources or inspirations. This structure ensures your blog post is engaging, informative, and useful for readers while showcasing your creativity and effort.
This is what i have so far: ... I'd like you to fill out the following: ... The Process Tools & Setup • Tools: • Django: Chosen for its robust ORM, which simplifies data modeling and relationship management. • django-simple-history: Selected for tracking historical changes in model data, allowing for versioning and audits. • PlantUML: Used for creating clear and concise UML diagrams to visualize the data model and relationships. • Python: Core programming language for the backend due to familiarity and its synergy with Django. • PostgreSQL: Database chosen for its support of advanced features like JSON fields and views, enabling flexible queries. • MIT Press Book on SimCity: Inspiration for structuring complex simulations and dynamic systems. Steps Taken 1. Brainstormed Features: • Identified key entities (e.g., individuals, doctrines, organizations, faiths, locations) and their relationships. • Discussed the need for attributes like sentiment and weight in join tables to capture dynamic relationships. 2. Designed Initial Data Model: • Created UML diagrams to visualize core entities and their relationships. • Focused on simplicity and flexibility, keeping attributes to a minimum in the early stages. 3. Integrated Historical Tracking: • Chose django-simple-history for tracking changes and ensuring data integrity over time. • Discussed options for centralized change logs across all models. 4. Explored Unified Event Tracking: • Considered various methods (SQL views, centralized logging tables, dynamic aggregation) for aggregating history across models. 5. Refined the Model: • Iteratively updated the data model based on feedback and discussions, balancing complexity and scalability. • Added join table attributes like sentiment and weight to enrich relationships. Challenges • Complexity vs. Simplicity: Struggled to balance a flexible model with the simplicity needed for efficient queries and management. • Event Tracking: Debated multiple approaches (e.g., SQL views, polymorphic tables) to create a unified history log across models, each with its pros and cons. • Dynamic Relationships: Capturing nuanced relationships (e.g., individual sentiment toward doctrines) while avoiding redundancy was a key challenge. • Inspiration from SimCity: Translating ideas from the book into practical design choices required careful thought about scalability and realism. Time Spent • Brainstorming and Research: ~3 hours, including reading and exploring simulation-based inspirations. • Data Modeling: ~4-5 hours, iterating through different designs and UML diagrams. • Historical Tracking Discussions: ~2 hours, evaluating tools and strategies for tracking changes across models. • Total Time: ~10-12 hours so far, spread over several sessions to allow for iterative refinement and reflection. Time management was guided by prioritizing clear deliverables for each session (e.g., finalize entities, test historical tracking).
summarize the project i'm working on, and the idea The Idea • What: This project is a data modeling and tracking system for simulating relationships and histories in a religion or faith-based context. The model includes entities such as individuals, doctrines (abstract ideas or principles), organizations, faiths (systems of doctrines), and locations. Relationships between these entities are expressed using join tables, allowing for attributes like sentiment (e.g., strongly agree/disagree) or weight (e.g., importance). The project aims to track dynamic relationships, sentiments, and historical changes across all entities, leveraging tools like django-simple-history for versioning and centralized logging for unified insights into changes across all models. • Why: The idea is compelling because it explores the complexity of human relationships with belief systems, organizations, and communities, and how these evolve over time. This is particularly relevant for creating simulations, games, or research tools where faith and societal structures play a central role. The project offers a unique intersection of data modeling, historical tracking, and behavioral simulation, making it an exciting technical challenge. • Scope: The focus is on designing a flexible, scalable data model that prioritizes: • Key entities (individuals, doctrines, organizations, faiths, locations). • Dynamic relationships (e.g., individual alignment with doctrines, organizational affiliation). • Historical tracking of changes across entities using django-simple-history. • A unified way to view all historical changes across the system, either via SQL views, a centralized logging table, or dynamic aggregation in the application layer. Visualizations, UI/UX, and advanced analytics are out of scope for now.
above is a blog post i'm working on about this process ; review and suggest changes (indicate the importance level, and also the amount of work to make suggested change) then output the post, making all minor changes suggested ( i will review larger / systematic changes before implementing) Introduction 1. Clarify Hook (Medium Importance, Low Effort): • Add a specific engaging hook to draw the reader in. Example: “Have you ever wondered how religions and belief systems evolve over time? What if you could simulate that complexity in a digital world?” • Reason: The current introduction is engaging but could use a stronger hook. 2. Combine Context and Preview (Low Importance, Low Effort): • Streamline by briefly mentioning what the project is and previewing the post’s structure earlier. The Idea 3. Consolidate Repeated Mentions of SimCity Book (Low Importance, Low Effort): • The book is mentioned twice in quick succession. Combine into one reference for conciseness. • Example: “Inspired by Building SimCity: How to Put the World in a Machine, I wanted to build a simulation game.” The Process 4. Expand Tools Section (Medium Importance, Medium Effort): • Add brief context for django-simple-history, PlantUML, and why they were chosen, as they are central to the project. • Reason: This gives non-technical readers more understanding of the tools used. 5. Clarify Challenges (High Importance, Medium Effort): • Explain “complexity vs. simplicity” and “dynamic relationships” with examples (e.g., balancing detailed attributes like sentiment with query performance). The Result 6. Visual Representation (High Importance, Medium Effort): • Add a placeholder or description for the PlantUML diagram since the visual element is emphasized in the post but missing from the draft. 7. Explain Join Table Benefits (Medium Importance, Medium Effort): • Add a brief explanation of why join tables with sentiment and weight fields are crucial. For example: “This approach ensures flexibility in modeling complex relationships while keeping the data normalized.” Reflection & Next Steps 8. Add Specific Reflection (Low Importance, Low Effort): • Reflect on a specific insight gained or a skill honed during the project. • Example: “This exercise improved my ability to model abstract concepts in a relational database.” ............ Summary of Changes Made • Added a stronger hook in the introduction. • Consolidated duplicate mentions of the SimCity book. • Clarified why certain tools were chosen. • Highlighted join table benefits and their role in the data model. • Improved readability and flow in the “Challenges” and “Reflection” sections.
I think I want it to sound less like a child wrote it... though i don't know what changes might make it more "eloquent & witty" without being stupidly overdone ... (I should note - ended up way to eloquent, and I ripped a bunch out that it had added) ... Key Changes Made: 1. Revised Sentence Structure: • Added rhythm and flow to the first paragraph, connecting ideas more seamlessly. 2. Replaced Informality: • Changed “While I have not actually built the simulation yet (and possibly never will)” to “Though the simulation itself remains theoretical.” 3. Added Subtle Wit: • Phrased “faith and doubt, unity and divergence” to evoke a poetic tone without being overdone. 4. Streamlined: • Simplified transitions to make the text feel more concise and polished.
I found your response a bit to "poetic" and formal, but updated mine to be ... Key Adjustments: 1. Improved Flow: • Adjusted “borrow ideas from each others” to “borrow ideas from each other” for grammar and smoother reading. • Combined the second paragraph for clarity and streamlined transitions between reading SimCity and working on the project. 2. Removed Parentheses from “Possibly Never Will”: • Replaced with a more fluid phrasing to keep the tone casual but polished. 3. Polished Grammar: • Capitalized “I” in “While i have not actually built.” • Used “versus” instead of “vs” for consistency with the tone.
restate the requests i gave about the voice ; and provide another update. be sure to output as markdown. also add back