deciqAI Latticework
Overview
Latticework is the practice of cross-wiring mental models from multiple disciplines on the same situation. Power comes from inter-connection: independent lenses converging = high-confidence signal; lenses diverging = unknown to investigate. When multiple forces align simultaneously they amplify — the lollapalooza effect (Munger, 1994). Composes with first-principles, second-order-thinking, probabilistic-thinking, and map-is-not-the-territory.
When to Use
- Stakeholders keep raising non-overlapping objections — each is right from their model
- Post-mortem shows failure was "outside the model we used"
- "Our analysis is solid" — but only one framework was applied
- Situation looks like a classic X but has anomalous features X cannot explain
- Designing a strategy/product where market, psychology, operations, and incentives all interact
- Judging an AI-boom / AI-adoption / AI-hype bet where "is it a bubble?" and "is it real?" are being argued through one lens each
Not when: problem is contained in one discipline; crisis triage (no time); decision too small for multi-model overhead.
Coaching Novices (Adaptive Front Door)
- Engine mode: user has a concrete case → run The Process directly.
- Coach mode: user is unfamiliar → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-liner: facts don't become knowledge until they hang on a latticework of theory (Munger's rule #1).
- Check fit: has one model already failed or felt incomplete? If yes, proceed.
- Elicit: what models applied so far? What disciplines are missing?
[WAIT — do not advance until user responds]
- Run The Process one step at a time with their input.
[WAIT — do not advance until user responds]
- Close: name the convergence map, blind spots, and any lollapalooza effects found.
[WAIT — do not advance until user responds]
The Process + Output Template
# Latticework Analysis: <situation>
## 1 — Phenomenon
Core question:
Prior single-model framing + its known blind spot:
## 2 — Lenses (3–5, genuinely independent disciplines)
| # | Discipline | Key Prediction | Force (+/-/0) |
|---|-----------|---------------|--------------|
| 1 | Economics | | |
| 2 | Psychology | | |
| 3 | Systems | | |
## 3 — Convergence Map
≥2 lenses agree (higher-confidence):
Lenses disagree (live unknown — investigate):
Lollapalooza: multiplicatively aligned forces?
## 4 — Blind Spots
What no lens covers:
## 5 — Calibrated Conclusion
Recommendation + Confidence:
Key residual uncertainty:
Information that would most change the picture:
→ Method in Action: Charlie Munger 1994 USC Business School Address
→ 2026 lens: Reasoning About the AI Boom (2024–2026) — economics × psychology × systems × game theory on one situation
Pack: Latticework Across Domains
| Domain | Typical single lens | Key missing lens |
|---|---|---|
| Startup PMF | Customer interviews | Systems (adoption loops) + History |
| Pricing | Demand curve | Game theory (competitive response) |
| M&A | Financial synergies | Psychology (culture) + History (base rates) |
Applying It Well
- Independence matters: 3 re-labeled versions of the same model is not a latticework
- Divergence = information; stop adding lenses when marginal new predictions cease (3–5)
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "We already did a full analysis" | One framework applied thoroughly is a single-lens deep dive — not a latticework. |
| [D] "Adding more models adds confusion" | Confusion from diverging models is information — it shows where understanding is incomplete. |
| [D] "We consulted multiple advisors" | If all advisors share the same disciplinary lens, that is triangulation within one model. |
| [D] "The model has worked before" | A model that predicted correctly in past contexts may be in a regime where its assumptions no longer hold. |
| [D] "Convergence is confirmation bias with extra steps" | Confirmation bias seeks evidence for a pre-held view. Latticework compares independent predictions — divergence check is the anti-bias mechanism. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Only one discipline's vocabulary used throughout
- Stakeholder objections dismissed without checking if they represent another model's prediction
- Decision called "rigorous" because the single model was applied thoroughly
- Diverging data forced into the primary model instead of triggering a model-check
- The analysis cannot name its own blind spots
Verification
- ≥3 genuinely independent disciplinary lenses applied
- Each lens produced an explicit, falsifiable prediction (not just "we considered X")
- Convergence zones marked higher-confidence; divergence zones named as live unknowns
- At least one blind spot named (phenomenon no lens covers)
- Lollapalooza check: any convergent forces multiplicatively aligned?
Part of deciqAI Knowledge Skills — 237 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/s/latticework · Built by deciqAI · github.com/deciqAI · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/latticework.json