How the Onion Was Discovered

🧅 What Is the Onion?

Understanding how systemic archetypes shape our world today
Developed by Sheila Damodaran, STRLDi
(Building upon the foundational work of Peter Senge and his team at MIT)

A Way of Seeing the Whole

When we want to see smaller details, we reach for magnifying lenses or microscopes.
When we want to see distant stars, we use telescopes.

But what do we use to see the whole system — the patterns that connect our actions, institutions, and crises?

That is the purpose of The Onion.

The Onion is a framework for zooming out — to see how different parts of our world are linked by repeating structures of cause and effect. It allows us to move beyond the “events” that grab headlines, to the underlying patterns of behaviour that shape them over time.

How the Onion Was Discovered

The Onion was not conceived as a theory; it was discovered through years of practice and observation.

While studying persistent issues — unemployment, economic stagnation, policy fatigue, social fragmentation — I began to notice that whenever one systems archetype appeared, another was usually close by.

When a Fixes that Fail dynamic was present, a Shifting the Burden pattern often lay beneath it.

When an Escalation loop emerged, a Success to the Successful structure usually accompanied it.

And wherever we found Limits to Growth, Tragedy of the Commons was never far behind.

These were not isolated patterns; they were connected gears within a single systemic mechanism — a kind of genetic code for how human systems evolve, struggle, and decline.

This became The Onion: a layered sequence of archetypes that unfold through time, revealing how societies move from early competition and growth, through crisis and exhaustion, toward renewal or collapse.

Seeing Through Time, Not Just Space

As Peter Senge wrote in The Fifth Discipline, “Systems Thinking is to discipline us in seeing and understanding patterns – looking beyond events – to deeper “structures” that control events, and discovering the leverage that lies hidden in these structures.”

This means the true signature of a system is not what it looks like — but how it behaves over time.

To see that behaviour, we must connect the dots across time.
Each event is just a single dot; when we connect them, a pattern emerges.

That pattern — what we call the Behaviour Over Time (BOT) or Pattern Over Time (POT) graph — is the system’s fingerprint.

Just as a gardener knows slugs have passed by the glistening trails they leave behind, we can recognise archetype structures by the curves they leave in data — even if the structures themselves are invisible.

Each archetype represents a recurring story of different and distinct human intention and unintended consequence. Peter and his team identified 12 such distinctive structures

Together, these stories form The Onion, which mirrors the way our economic, political, and ecological systems interlock.

Why It Matters

For policymakers, leaders, and citizens, The Onion makes it possible to:
▪️ Identify why reforms backfire even when intentions are good.
▪️ Recognise when short-term fixes are masking deeper systemic imbalances.
▪️ See how one sector’s success (for example, financialisation) quietly constrains another (like manufacturing or agriculture).
▪️ Anticipate how unaddressed limits in one layer of the Onion will later appear as crises in another.

The Discipline of Seeing Patterns

As Peter Senge defined in The Fifth Discipline, a system is “a set of interrelated elements whose behaviour over time produces its own pattern of events.”

To see that pattern in our minds first and then with our eyes is to practise the discipline of systems thinking.

Each “event” we notice — a policy change, a price rise, a public protest — is only a dot. It is when we join these dots over time that a pattern begins to reveal itself. And once the pattern is recognised, we can trace its source — the structure that produced it — just as a gardener traces the silvery trail of slugs back to where they hide.

The structure itself is often invisible; what we can see are the behaviours over time (BOT) or patterns over time (POT) that betray its presence. Each archetype leaves behind a distinct fingerprint — a unique behaviour curve that can be identified in real-world data.

That is why in The Onion we rely not on opinions or isolated incidents, but on time-based patterns. These patterns are our evidence. They tell us not just what is happening, but how the system is thinking.

Methodology: How We Discover That These Patterns Exist All Around Us

Purpose of the Exercise

This exercise was designed to uncover how the world unknowingly reveals its systems thinking through data.

While most researchers and journalists describe events — changes in employment, climate, population, or governance — their graphs of those events over time often unintentionally expose the fingerprints of deeper systemic structures.

Our goal was to find these Behaviour Over Time (BOT) or Pattern Over Time (POT) graphs across public domains — not to explain the archetypes, but to witness them already in action.

Each graph, when viewed through a systems lens, tells the story of an underlying archetype shaping real-world behaviour.

Our Guiding Hypothesis

If a system archetype truly governs the way human and institutional behaviour evolves,
then we should find its signature pattern reflected in the data — even when the author of that data is unaware of the structure at play.

This meant searching not for theory or models, but for natural evidence: graphs that were never drawn to illustrate their persistence but whose behaviour unmistakably matched the logic of the archetypes.

The Search Process

We used a structured set of instruction templates to locate authentic, time-based graphs for each archetype.

Each instruction asked specifically for:

  • Real-world data expressed over time
  • Observable patterns of growth, decline, oscillation, or collapse
  • No theoretical overlays or causal loop diagrams
  • Unintentional reflections of system behaviour — where the reporter or researcher likely did not know they were revealing an archetype

The request template began like this:

“I am looking for real-world BOT (Behaviour Over Time) graphs that reflect the dynamics of the [insert archetype name] archetype.
I am not looking for articles that explain the archetype or show causal loops.
I want actual time-based graphs from real-world issues that show a pattern over time matching the logic of this archetype.”

Each archetype had its own search pattern:

ArchetypeBehaviour We Looked For
1. EscalationTwo or more parties reacting to each other’s actions, amplifying their efforts and driving runaway growth or conflict.
2. Success to the SuccessfulDiverging performance between actors — where initial advantage reinforces future success.
3. Growth and UnderinvestmentGrowth followed by stagnation or collapse due to underinvestment in capacity.
4. Shifting the BurdenInitial improvement from a quick fix, followed by relapse or worsening as root causes remain untouched.
5. Fixes that FailShort-term improvement leading to long-term deterioration or new problems.
6. Drifting GoalsGradual lowering of standards or targets over time — a settling for less.
7. Limits to GrowthRapid early growth that plateaus or declines despite increasing effort — the invisible limit.
8. Tragedy of the CommonsOveruse of shared resources leading to depletion or collapse.
9. Accidental AdversariesTwo well-meaning actors whose efforts unintentionally undermine each other’s success over time.

What We Discovered

Across all archetypes, we found that real-world data mirrors the same curves predicted by system archetypes.

The graphs — drawn from economics, health, education, climate, and governance — show unmistakable patterns:

  • Divergences that echo Success to the Successful
  • Oscillations and collapses that embody Limits to Growth
  • Sudden reversals after quick gains that mark Fixes that Fail
  • Shared resource declines identical to Tragedy of the Commons

What is most striking is that these graphs were created by people who had no intention of illustrating systems thinking. The archetypes revealed themselves naturally — through the behaviour of the dots over time.

Archetype FolderSearch FocusWhat Was RetrievedGraph (BOT)Article SourceArticle SummaryWhat the Article MissedWhat Humanity Lost
EscRunaway “action vs counter-action” between rival actors (arms race)Global military spending time-series and split by countrySIPRI trend lines (1988–2024) showing decade of consecutive global increases; 2024 +9.4% YoY to $2.718T. (SIPRI)SIPRI 2024/25 fact sheet & release; Reuters recap. (SIPRI)Spending rises across all regions, led by U.S., China, Russia; steepest annual rise since end of Cold War. (SIPRI)Press coverage tallies totals but rarely frames the feedback: that each budget uplift is signal and fuel for the other.Opportunity cost: diverted capital from social & climate investment; entrenched security dilemmas become self-fulfilling. (Reuters)
G&UGrowth stalls as capacity lags; investment delayed or deflected.Grids, transit maintenance backlogs, NHS waitlists.
U.S. transit State of Good Repair backlog climbing (2008–2022); NHS waiting list long-run rise. (pew.org)Pew on $140.2B U.S. transit backlog; FTA report; UK Commons Library on waiting lists & missed 18-week targets. pew.org+2transit.dot.gov+2
Demand rises; underinvestment in capacity → delays/decline; the worsening performance reduces perceived need to invest.		Coverage cites “backlog” or “budget constraints,” but misses the core delay loop where poor performance justifies further delay—locking in underinvestment.Lost growth & resilience: slower mobility/productivity, worsening access (health), weakened national competitiveness. (pew.org)
StSDivergence where early advantage compounds (wealth, market share, achievement)World Inequality Database/OWID “top 1% share” time-seriesRising/volatile top 1% income/wealth share charts (1820–2023; by country). (Our World in Data)OWID/WID explorers; Oxfam 2025 synopses on extreme wealth gains. (Our World in Data)Reports show disproportionate gains at the very top; policy debates on taxation and public goods funding. (The Guardian)Many stories treat inequality as static gap, not as a resource-allocation feedback that depresses mobility and future capacity at the bottom.Lost human potential; persistent underinvestment in broad-based capability (education, health), worsening unemployment traps. (OECD)
StBShort-term relief crowding out root-cause work (relapse pattern)Overdose mortality & harm-reduction vs punitive cycles; homelessness services vs housingLong-run U.S. overdose deaths time-series; 2024 drop after harm-reduction scale-up. (CDC)CDC data brief; AP/Reuters on record decline linked to naloxone/treatment funding, with warnings about reversals if funding falls. (CDC)Shifting the Burden BOT: rapid symptomatic actions (e.g., crackdowns) bring brief relief; root-causes (treatment, social drivers) underfunded → relapse.Many pieces celebrate short-term drops without noting that cutting structural funding rekindles the crisis—classic burden shift. (AP News)Lost stability: policy whiplash, preventable deaths when support ebbs; public trust erodes with every relapse. (Reuters)
FtFQuick fix improves KPI, but side-effects worsen core problemAntibiotic overuse → resistance; wildfire max-suppression → mega-firesGlobal AMR rise; modeled evidence that max suppression raises burned area/severity over time. (The Lancet)The Lancet AMR burden (1990–2021) with forecasts; Nature Communications on suppression bias escalating fire severity/burned area. (The Lancet)Fixes that Fail BOT: immediate indicator improves (infection cleared; fire out) → side-effects (resistance; fuel build-up) amplify future crises.Headlines warn of AMR or big fires but often decouple them from the very fixes creating the problem (incentives & protocols). (The Lancet)Lost effectiveness: dwindling antibiotic arsenal; spiraling suppression costs and damage; missed pivot to prevention and stewardship. (Reuters)
DGLowering targets instead of closing the performance gapStandards/targets adjusted downward; persistent under-performancePISA 2022 shows declines in maths/reading; policy responses emphasize “realistic” targets; air-quality rules eased vs WHO. (OECD)OECD PISA Volume I; EU revises PM2.5/NO₂ limits but still above WHO guidelines. (OECD)Drifting Goals BOT: pressure to hit numbers resolved by redefining the number; two O links in B2 hide the reinforcing slide.Reports cover new thresholds but rarely name the goal-lowering feedback that normalizes decline and starves investment in closing the gap.Lost aspiration: systemic mediocrity; cohorts of students or citizens habituated to lower standards become the new norm. (The Systems Thinker)
LtGGrowth plateaus/declines despite rising effortInput effort vs output plateau (ag, health, economy)Classic BOT: effort up, results flatten—capacity/ecological limits bind (e.g., yields vs inputs; service throughput vs backlog). (Use as companion to G&U.) (Datalere)System Thinker explainer; sector examples abound in FAO/health backlogs. (The Systems Thinker)Limits to Growth BOT: reinforcing push meets hidden balancing limit; if the limit is ignored, the “R2 with two O’s” kicks in (push harder → worse).Articles note “diminishing returns” but miss where the limit is and how pushing harder tightens it.Lost timing: late recognition of constraints leads to overrun, brittleness, and costly recoveries.
ToCShared resource overuse and collapseWild fish stocks; aquifers; commons depletionFAO: 35.5% overfished; share overexploited rose over decades; Ogallala/Aral long-term decline charts. (FAOHome)FAO 2025 SoFIA release; OWID fish-stock status; NASA Aral Sea shrinkage. (FAOHome)Tragedy of the Commons BOT: as yield per effort falls, each actor increases effort to maintain income → accelerates depletion.Coverage flags “overfishing” but often omits the per-actor incentive loop that makes voluntary restraint unstable without governance.Lost sustainability: collapsed livelihoods & ecosystems; polarization that seeds the AA split that follows. (Reuters)
AAFormer partners undermine each other via “helpful” fixesTrade wars/sanctions where both sides incur lossesUS–China tariff spiral charts; EU–Russia trade collapse time-series. (PIIE)PIIE tariff tracker; Eurostat/EC explainer on EU–Russia trade shares plunging since 2022. (PIIE)Accidental Adversaries BOT: each side’s fix (tariffs/sanctions) degrades the partner’s process, triggering retaliation that erodes both.Articles tally losses but don’t map the mutual-undermining loop that turns cooperation into a race to the bottom. (imf.org)Lost collaboration and optionality: supply chain fragility, foregone co-innovation, and hardened narratives that block future repair. (TIME)

Why This Matters

This methodology transforms systems thinking from abstraction into evidence. It shows that the archetypes are not theories we impose on reality — they are reality itself, recorded in motion.

By recognising these BOT patterns, we can diagnose systemic structures using data already in the public domain — data that policymakers, economists, and journalists produce every day.

In doing so, The Onion becomes a living bridge between academic systems theory and the real-world pulse of nations — a way for leaders to see what the system is saying, even when no one is listening.

How You Can Replicate This Exercise

This exercise can be done by anyone — from policy analysts and journalists to students and community leaders — who wants to learn how to see systems instead of just events.

It uses publicly available data and simple observation to reveal the patterns that show an archetype at work.

Here’s how you can do it.

Step 1. Choose a Real-World Issue

Pick a problem that has persisted over time — unemployment, inflation, crop yields, water access, energy demand, migration, or school performance.
The longer the time series, the clearer the behaviour pattern will be.

Step 2. Gather Time-Based Data

Look for sources that show the same indicator over multiple years or decades.
Good places to start:

  • National statistics offices and ministries
  • World Bank, IMF, FAO, WHO, UN Data portals
  • Peer-reviewed articles or policy papers with line graphs
  • News media infographics or economic dashboards

You don’t need full datasets — even a simple published chart is enough.

Step 3. Observe the Shape, Not the Story

Ignore the commentary for a moment and look only at the curve:
Is it rising steadily, slowing down, oscillating, or collapsing?
Does one curve diverge from another?
Does a temporary improvement reverse later?
That shape is the system’s Behaviour Over Time (BOT).

Step 4. Compare the Pattern to an Archetype

Match the observed behaviour to the classic archetype patterns.
Use The Onion or any systems-thinking reference to compare shapes:

  • Escalation → runaway competition between two actors
  • Success to the Successful → diverging fortunes
  • Shifting the Burden → improvement then relapse
  • Limits to Growth → growth then plateau or decline
    …and so on.

If the curve fits the archetype’s fingerprint, you’ve likely found its trace in the real world.

Step 5. Ask What the Data Doesn’t Show

Once you’ve identified the archetype, ask:

  • What balancing or reinforcing loops might be creating this shape?
  • Who are the actors caught in it?
  • What mental models or fears might be sustaining it?

This turns observation into insight — from what happened to why it keeps happening.

Step 6. Document and Share

Capture the chart, note its source, and describe the archetype you think it represents.
Explain briefly:

The context (country, sector, time frame)

The observed pattern

The corresponding archetype

When many such graphs are collected side by side, a larger pattern begins to emerge — The Onion itself.

What You’ll Gain

By repeating this exercise, you train your mind to recognise archetypes everywhere — in budgets, rainfall charts, school scores, or corporate performance reports.
You begin to see not isolated problems, but interlocking loops of behaviour that connect across systems and time.

In short, you learn to see the system thinking itself through us.

Learn More

You can now explore each archetype in detail:

In Summary

The Onion gives us a new kind of lens — one that doesn’t just magnify, but reveals how we ourselves are part of what we are trying to change.

It helps us see the forests we live within, and the fingerprints of our own thinking upon them.