Systems Thinking

Framework

IRON LAW: First-Order Fixes in Complex Systems Produce Second-Order
Backlash Within 2 Cycles — Map the Feedback Loop BEFORE Intervening

Agents default to "fix the symptom directly" (e.g., high turnover → raise
salaries). In systems with feedback loops, the direct fix triggers a
compensating response that makes the original problem worse OR creates
a new one (raise salaries → budget squeeze → cut training → worse
onboarding → higher turnover). Before recommending any intervention,
draw the causal loop diagram and identify at least one reinforcing and
one balancing loop. If you can't find any, the problem may not be a
systems problem — don't force the framework.

Analysis Steps

references/system-archetypes.md

1. Define the system boundary: What's in, what's out?
2. Map key variables: What are the important stocks (quantities that accumulate)?
3. Identify feedback loops: Which loops are reinforcing? Which are balancing?
4. Find delays: Where is cause separated from effect in time?
5. Locate leverage points: Where would small interventions produce the biggest shift?
6. Check for unintended consequences: What might this intervention break elsewhere in the system?

Output Format

# Systems Analysis: {Problem}

## System Boundary
- In scope: ...
- Out of scope: ...

## Key Variables
- {Variable A}: {description}

## Feedback Loops
- Reinforcing: {A → B → A (amplifying)}
- Balancing: {A → B → C → opposes A (stabilizing)}

## Delays
- {Input} → {Effect} (delay: {timeframe})

## Leverage Points
1. {where small change = big impact}

## Unintended Consequences Risk
- If we {intervention}, it might also {side effect} because {loop/connection}

Examples

Correct Application

Incorrect Application

• "Revenue is down. Increase marketing spend." → Linear, single-cause thinking. Ignoring: Why is revenue down? Is it demand (balancing loop from saturation)? Is it churn (reinforcing loop of poor quality → complaints → more churn)? Different root causes require different interventions.

Gotchas

• Systems resist change: Balancing feedback loops maintain the status quo. Pushing against them without addressing the loop structure leads to "fixes that fail."
• Mental models are partial: Everyone's mental model of a system is incomplete. Mapping the system with diverse stakeholders reveals blind spots.
• Unintended consequences are the norm, not the exception: In complex systems, interventions always produce side effects. The question is whether you've identified the important ones.
• Not everything is a system: Simple problems with clear cause-and-effect don't need systems thinking. Use it for problems where linear thinking fails.

References

• For system archetypes (Limits to Growth, Shifting the Burden, etc.), see

  references/system-archetypes.md