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@secondordereffects

Second Order Effects

Cataloging the consequences nobody planned for
Second Order Effects

Decisions have consequences you didn't model.

A rigorous, evidence-based catalog of hidden consequences in systems and decisions.
Evidence-based analyses across 8 categories. Every claim quantified. Every source cited.

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How it works

Every analysis follows the CHAIN Framework:

C — Context       What's the situation? What decision is being made?
H — Hypothesis    What do people expect? (the first-order claim)
A — Actual Chain  What actually happens? (2nd, 3rd, 4th order effects)
I — Impact        Quantified consequences (before/after/delta)
N — Navigation    Don't-if / If-you-must / Alternatives

Every entry includes falsifiability criteria — what would prove it wrong.

This is not a blog. It's a reference library for decision-makers who want to avoid predictable surprises.


Featured Analyses

Decision What You Expect What Actually Happens
Adopt Kubernetes "Better scaling" +40-60% infra team size, 3x onboarding time
Go Microservices "Independent deployments" +300% debugging time, distributed tracing tax
Index Fund Investing "Passive beats active" Correlation → 1 in crashes, reflexivity loops
Remote Work "Flexibility + talent pool" +36% meeting hours, weak ties decay

Categories

Category Prefix Examples
Technology T Kubernetes, microservices, monorepos, serverless
AI & Automation A Copilot skill atrophy, AI content flood, code gen debt
Markets M Index reflexivity, ZIRP zombies, freemium ceilings
Policy P GDPR moats, rent control, Section 230, AI regulation
Organizations O Agile bloat, layoff survivor syndrome, unlimited PTO
Society S Social media loneliness, algorithmic radicalization
Infrastructure I Cloud concentration, EV grid strain, data center water
Science & Health H Antibiotic resistance, replication crisis, AI diagnosis

Repositories

Repository Description Status
content Entry database (CC BY 4.0) — contribute here 🟢 Active
secondordereffects.github.io Rendering engine (soe.lagbase.com) — private 🟢 Active

Contribute

We welcome contributions that meet our quality bar:

  • ✅ Follows CHAIN framework
  • ✅ Includes quantified impact (numbers, not adjectives)
  • ✅ Cites verifiable sources
  • ✅ States falsifiability criteria

See our Contributing Guide or open a Discussion.


Part of the lagbase ecosystem

Research on decision lag and hidden consequences.

lagbase.com · github.com/lagbase


License

Content: CC BY 4.0 · Code: MIT


© 2026 Naveen Reddy · soe.lagbase.com · @lagaboratory

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