{"slug": "coase-information-theory-for-ai-orgs", "title": "Coase-Information Theory for AI Orgs", "summary": "Galatheus Labs released a working paper and interactive simulation introducing Coase-Information Theory, which posits that firms and AI agents form boundaries based on a trade-off between shared representation reducing surprise and coordination costs. The theory models organizations as information architectures and provides a phase map for determining optimal organizational granularity across scenarios like microservices vs. monoliths and vendor dependencies.", "body_md": "**▶ Live interactive companion → https://galatheus-labs.github.io/coase-info-theory/** ·\n\n[Paper (PDF)](/galatheus-labs/coase-info-theory/blob/main/paper/coase-information-theory.pdf)·\n\n[Paper (TeX)](/galatheus-labs/coase-info-theory/blob/main/paper/coase-information-theory.tex)·\n\n[Galatheus](https://galatheus.com)\n\nThis repo packages a small set of static web apps plus formal paper material for the **Coase–Information Theory** project.\n\nThe central claim is:\n\nFirms are a special case of agent-boundary formation. Agents coalesce when shared representation reduces surprise and action loss more than it raises coordination cost, and split when modularity lowers cost more than it raises interface surprise.\n\nThe practical implication:\n\nOrganizations are information architectures. Their economic performance depends on how efficiently they convert distributed, noisy signals into coordinated action under latency, cost, and distortion constraints.\n\nThe browser demo translates that claim into tangible software.\n\nThe companion page opens with a 60-second animated walkthrough of the thesis — a firm is an agent in a market, and is itself made of agents — then lets you drive the model across concrete \"current problems\":\n\n- monolith vs microservices (the granularity sweet spot,\n`m* = √(A/B)`\n\n), - managed database / vendor dependency,\n- support triage agent,\n- coding maintenance agent,\n- security alert triage,\n- procurement and AP automation.\n\nA boundary **phase map** shows which architecture wins across protocol quality and task interdependence, with the `R* = κ`\n\ncapacity threshold drawn in.\n\n`index.html`\n\n— the canonical browser simulation. It runs the scenario-driven Monte Carlo model where paper use cases map into the same abstract boundary levers: protocol quality, interdependence, observability, agent coverage, governance, market friction, volatility, and planning depth.\n\nThe earlier prototype pages now redirect to `index.html`\n\nso old links do not open stale concepts.\n\n`paper/coase-information-theory.tex`\n\n— canonical working-paper source`paper/coase-information-theory.pdf`\n\n— rendered working-paper PDF`paper/coase-information-theory-draft.md`\n\n— earlier prose draft, retained for reference`docs/formal-paper-analysis.md`\n\n— original mathematical framing, propositions, agent extension, and completion plan`docs/formal-note.md`\n\n— compact note tying the apps back to the theory`docs/paper-completion-plan.md`\n\n— concrete path from note to finished paper`docs/review-and-improvement-plan.md`\n\n— review of the current work and prioritized improvements`docs/measurement-playbook.md`\n\n— how to estimate theory quantities from workflow traces`docs/app-model-assumptions.md`\n\n— assumptions and calibration plan for the demo apps\n\n```\n@misc{guarraci2026boundaryagent,\n  author = {Guarraci, Brian},\n  title = {From the Boundary of the Firm to the Boundary of the Agent: Coase-Information Theory for AI-Mediated Organizations},\n  year = {2026},\n  howpublished = {Working paper},\n  url = {https://github.com/galatheus-labs/coase-info-theory}\n}\n```\n\n`sample-data/incident-trace.csv`\n\n`sample-data/support-ticket-trace.csv`\n\nThese samples are retained for future calibration work.\n\nThe companion runs live at ** https://galatheus-labs.github.io/coase-info-theory/**. To run it locally, open\n\n`index.html`\n\ndirectly in a browser or serve the repo:\n\n```\npython3 -m http.server 8000\n```\n\nThen open `http://localhost:8000/`\n\n.\n\nThe paper is strongest when it does five things together:\n\n**Formalizes agents** as bounded information-processing units: individuals, teams, firms, vendors, and software agents.**Explains boundaries** as coalescing/splitting choices around surprise reduction and coordination cost.**Defines agility** as the rate at which information becomes coordinated action.**Shows executable examples** where software agents shift latency, distortion, monitoring, and the effective boundary of the firm.**Runs a simulation** where the formal objective produces boundary phase behavior across protocol quality and task interdependence.\n\nThe simulation is deliberately simple. It is not a calibrated causal estimate. It is an executable illustration and measurement scaffold.\n\nChoose one empirical spine — preferably incident response or support routing — and replace the current stylized coefficients with real trace calibration.\n\nThis repo should support a public paper/essay package:\n\n- arXiv-style working paper,\n- companion demo repo,\n- short founder essay,\n- video walkthrough,\n- one trace-based case study.\n\nThe thought-leadership sentence:\n\nAI-native companies are not just companies with AI tools. They are organizations whose sensing, routing, decision, and execution loops are increasingly software-mediated.", "url": "https://wpnews.pro/news/coase-information-theory-for-ai-orgs", "canonical_source": "https://github.com/galatheus-labs/coase-info-theory", "published_at": "2026-06-15 14:29:57+00:00", "updated_at": "2026-06-15 14:38:10.657860+00:00", "lang": "en", "topics": ["ai-research", "ai-agents", "ai-infrastructure", "artificial-intelligence"], "entities": ["Galatheus Labs", "Brian Guarraci", "Coase-Information Theory"], "alternates": {"html": "https://wpnews.pro/news/coase-information-theory-for-ai-orgs", "markdown": "https://wpnews.pro/news/coase-information-theory-for-ai-orgs.md", "text": "https://wpnews.pro/news/coase-information-theory-for-ai-orgs.txt", "jsonld": "https://wpnews.pro/news/coase-information-theory-for-ai-orgs.jsonld"}}