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.

1. One-liner: facts don't become knowledge until they hang on a latticework of theory (Munger's rule #1).
2. Check fit: has one model already failed or felt incomplete? If yes, proceed.
3. Elicit: what models applied so far? What disciplines are missing?

[WAIT — do not advance until user responds]

4. Run The Process one step at a time with their input.

[WAIT — do not advance until user responds]

5. 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?

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Part of deciqAI Knowledge Skills — 227 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/c/latticework · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.

Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/latticework.json