System Archetypes
System archetypes are recurring structural patterns—combinations of stocks, flows, feedback loops, and delays—that generate familiar behaviours across wildly different domains. Spotting an archetype lets you skip exhaustive data gathering and move straight to high‑leverage interventions.
Why a whole chapter?
While the Dynamic Behaviour Patterns chapter shows what curves appear (S‑curves, overshoot‑and‑collapse, etc.), archetypes explain why they appear and where to intervene. They are one step closer to the blueprint of a system.
Quick Map of Classic Archetypes
| Family | Name | Signature Behaviour | Classic Pitfall |
|---|---|---|---|
| Growth limits | Limits to Growth | Early exponential rise that flattens or collapses | Fighting symptoms instead of removing the limit |
| Quick fixes | Fixes That Fail | Short‑term relief, long‑term rebound worse than before | Ignoring side‑effects or delays |
| Shifting the Burden | Rising dependence on a symptomatic solution, erosion of fundamental capability | “Addiction” to the quick fix | |
| Resource rivalry | Tragedy of the Commons | Resource depletion despite individual rationality | No shared constraint on use |
| Success to the Successful | Self‑reinforcing advantage, widening gap | Starving late movers of resources | |
| Escalation | Escalation (Arms Race) | Two balancing loops that drive each other upward | Cost spiral with no natural cap |
| Drifting standards | Eroding Goals (Drifting Goals) | Gradual downward reset of targets | Normalising deviance |
| Capacity traps | Growth & Under‑investment | Demand outgrows capacity → service drops → investment delayed | Vicious circle of degradation |
The eight above form the “core set” described by Meadows, Senge, and many others.
1 Limits to Growth
Structure
A reinforcing loop drives growth until a balancing loop—often delayed—kicks in as some “carrying capacity” is approached.
Behaviour
S‑curve saturation or, if the balancing correction is too slow, overshoot‑and‑collapse.
Leverage Points
- Remove or raise the limiting factor (e.g., add production lines).
- Speed up the balancing feedback so action starts sooner (shorter information delay).
Real‑world Signals
“Hyper‑growth SaaS stalls at 80% YoY as customer‑success staffing can’t keep pace.”
“Algae bloom collapses when nutrient supply exhausted.”
Interactive Example
2 Fixes That Fail
Structure
Balancing loop with a quick symptomatic fix. A side‑effect (reinforcing loop) undermines the system later.
Behaviour
Initial improvement followed by equal‑or‑worse relapse.
Leverage Points
- Address the underlying cause rather than symptoms.
- Surface delayed side‑effects (information flow).
Signals
“Cutting maintenance budget boosts quarterly profit; two years later outage costs exceed savings.”
Suggested Example: A small code snippet model could track deferred maintenance cost versus failure rate.
3 Shifting the Burden
(A cousin of Fixes That Fail in which the quick fix becomes addictive.)
Structure
Two balancing loops compete:
- Fundamental Solution (slow)
- Symptomatic Solution (fast) that also erodes the capability to deliver the fundamental one.
Behaviour
Growing dependency on the quick fix; declining core capability.
Leverage Points
- Invest in the fundamental solution early.
- Limit or phase‑out the symptomatic response.
Signals
“Chronic use of sleeping pills reduces natural sleep quality, requiring ever higher doses.”
Interactive Example
Sleep Debt Simulation
A sleep debt simulation tracking caffeine, sleep patterns, and debt buildup.
Level:Intermediate
4 Tragedy of the Commons
Structure
Multiple actors draw from a shared stock. Each reinforcing loop benefits the individual; a single balancing loop (resource depletion) is global and delayed.
Behaviour
Aggregate extraction overshoots renewal → resource collapse.
Leverage Points
- Align individual incentives with collective health (quotas, pricing, tradable permits).
- Improve visibility of the shared stock level.
Signals
“Open‑access fishery collapses despite each boat acting ‘rationally’.”
Interactive Example
Fishery Simulation
A fishery simulation of stocks, flows, and feedback loops managing fish populations.
Level:Beginner
5 Success to the Successful
Structure
Two (or more) actors compete for a shared inflow of resources. Small early advantage loops back to secure even more resources.
Behaviour
Divergence; winner‑take‑all.
Leverage Points
- Cap the reinforcing advantage (e.g., progressive taxation on resources).
- Guarantee baseline access for lagging actors.
Signals
“Streaming platform promotes top shows, making them even more dominant.”
6 Escalation (Arms Race)
Structure
A balancing loop in System A sets a target relative to System B, and vice‑versa. Each action is a negative reference for the other.
Behaviour
Spiral of ever‑increasing effort, cost, or aggression; potential sudden collapse when one party can’t keep up.
Leverage Points
- Break the relative reference (treat own performance as absolute).
- Introduce an external limit (treaty, budget cap).
Signals
“Advertising bids climb quarter after quarter as rivals monitor each other’s spend.”
7 Eroding Goals (Drifting Goals)
Structure
Discrepancy between desired state and actual state is corrected not only by acting on the real system but also by lowering the goal itself.
Behaviour
Gradual performance decay masked by slipping standards.
Leverage Points
- Fix the reference point (hard targets).
- Track and publish gap‑over‑time to expose drift.
Signals
“Delivery SLA redefined from 2 days to ‘fast shipping’ while average actually slips to 5 days.”
8 Growth & Under‑investment
Structure
Reinforcing growth drives demand. Investment in capacity is governed by a balancing loop with delay. If service quality drops, demand slows, cutting appetite for new investment—a vicious circle.
Behaviour
Boom‑stall or boom‑bust depending on delay length.
Leverage Points
- Invest ahead of demand using leading indicators.
- Reduce investment delays (prefab capacity, flexible staffing).
Signals
“Cloud region reaches 90% utilisation; performance lags discourage new tenants, stalling revenue.”
Suggested Example: A queueing model where wait‑time erodes sign‑ups unless capacity expansion triggers soon enough.
Challenge
Pick one archetype you’ve lived through in your domain.
- Draw its causal‑loop diagram.
- Mark at least two high‑leverage interventions—one that changes information flows and one that changes incentives or rules.
- Explain which intervention you would choose first and why.
Next steps:
- Revisit Leverage Points to connect each archetype’s intervention spots to Meadows’ full leverage ladder.
- Try rewiring an archetype in the Playground (e.g., add a reporting delay or change goal‑setting logic) to see how behaviour shifts.