Litany
The surface news. The headline, the slogan, the plain statement of the problem. Most decisions live here, which is why so many of them feel shallow.
Causal Layered Analysis
Most AI answers stay at the surface of a question. Causal Layered Analysis is a futures-studies method for working past that surface into the systems, worldviews, and deeper stories that actually drive the choice. Manwe uses it inside serious decision records, whether you start in the web app or take the separate Mac beta path.
Endorsed by the creator of Causal Layered Analysis
Brilliant stuff here. Just love it. You are on to something. Congrats.
Manwe implements Inayatullah’s Causal Layered Analysis and the Deeper Story synthesis. That matters because the product does not stop at advice: it studies the assumptions underneath the decision and the futures the decision could open.
The four layers
The surface news. The headline, the slogan, the plain statement of the problem. Most decisions live here, which is why so many of them feel shallow.
The structure underneath the headline: incentives, laws, markets, technology, supply chains. The machinery that makes the litany look the way it does.
The assumptions and beliefs that make the system feel natural or inevitable. What you take for granted. What the room has quietly agreed not to question.
The deep story underneath everything else. The recurring drama, the invisible script, the roles we play without realising we chose them.
Why it matters
A decision made only at the litany level, the surface, is a decision that treats the symptom. It can feel fast, clear, and complete, and then quietly fail because the system, the worldview, or the myth underneath was never inspected.
Working down the layers is slower. It is also the difference between an answer that sounds right and a decision that holds up when reality argues back.
How Manwe uses it
Toward the end of a serious run, Manwe stops asking advisors for conclusions and asks them to inspect their own assumptions. Each advisor names what they took for granted and how it shaped the advice they gave earlier.
Then, independently, they surface the deeper story they see running underneath the question: the recurring drama, the invisible role, the plot the room has been performing without naming. A synthesis layer turns those into a single "Deeper Story" that sits alongside the practical record.
This is also where Future Paths begin. The deeper story feeds into how Manwe projects divergent futures in Pro records and how the Mac beta can keep those branches visible beside the decision.
Lineage
Causal Layered Analysis was introduced by Prof. Sohail Inayatullah, UNESCO Chair in Future Studies at IIUM Malaysia, in his 1998 paper Causal layered analysis: Poststructuralism as method (Futures). It has since been used in government, corporate, and academic workshops for more than two decades, and featured alongside other methods in his 2008 paper Six Pillars: futures thinking for transforming. His institutional site at metafuture.org collects his ongoing work.
The endorsement above matters because CLA is not just a label for Manwe. It is part of how the record inspects assumptions before projecting futures.
FAQ
Causal Layered Analysis is a futures-studies method that examines a problem at four levels: surface events, systems, worldviews, and deeper myths or stories.
Manwe uses CLA near the end of serious runs to inspect assumptions, surface the deeper story underneath the question, and connect the practical decision to possible future paths.
No. CLA is useful whenever a decision is shaped by hidden assumptions, social scripts, incentives, identity, or long-term consequences.
Normal AI reasoning often stays at the explicit question. CLA asks what system, worldview, and deeper story make the question feel natural in the first place.
Try it
Serious Manwe records include a CLA pass and a Deeper Story synthesis. The web app is the main path for Quick and Pro runs; the Mac beta remains available if you want a local-first workflow with Worlds, memory review, and scenario testing.