Good Enough Data & Systems Lab

Mathematical Proofs Implemented in Computational Systems

Order from the chaos of existing development, without interfering with team workflow.

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The Good Enough Data & Systems Lab collaborates by computationally applying category theory and abstract mathematics to codebases and digraph-oriented development (e.g., JIRA) plans. Any graph-oriented or flat data structure has potential to be aggregated.

The Lab uses a mix of category theory, visualisation, and data simulation to explore frameworks such as

• user behaviour
• developer wellbeing
• efficiency
• reliability
• trustworthiness
• expectation
• and most of all, human intentionality in a chaotic system.

A key priority is to to have no disruption on developer workflow, the Lab works at a metalevel on existing plans, well-masked human resource database extraction, and codebase metadata to provide strategic analyses.

The Good Enough Data & Systems Lab develops and publishes on a categorically-defined structured intelligence governance framework with collaborators, creating a virtuous cycle between research and industry.

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Under development - here’s what’s coming:

• data simulation animations of singularities failing
• time-series analysis on real-world data
• two papers
• slides
• a narrative adventure game
• simulations on the narrative adventure game
• category-theoretic analysis on development of the narrative adventure game
• proof-sketches to discuss with applied category theorists

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