Systems Analysis

Overview

Analyze complex systems using causal loop diagrams, stock and flow models, feedback loop identification, and system archetypes

Steps

Step 1: Define system boundary

Establish what is inside and outside the system:

IDENTIFY THE PROBLEM/BEHAVIOR:
- What behavior are we trying to understand?
- What is the problem symptom?
- What pattern do we observe over time?
  - Growth (exponential, linear, S-curve)
  - Decline (gradual, rapid, oscillating)
  - Oscillation (boom-bust, cycles)
  - Equilibrium (stable, stuck)
DEFINE SYSTEM BOUNDARY:
- What elements are “in” the system?
- What is treated as external (given)?
- Time horizon: How far back and forward to consider?
Include in boundary:
- Elements that directly cause the behavior
- Elements affected by the behavior
- Elements involved in feedback loops
Exclude from boundary:
- Elements that are truly external (weather, laws)
- Elements too distant to matter
- Elements we cannot influence at all
IDENTIFY KEY VARIABLES: List the important quantities that change:
- Stocks: Things that accumulate (inventory, reputation, skills)
- Rates: Things that flow (sales per day, hires per month)
- Auxiliary: Factors that influence but don’t accumulate
ESTABLISH TIME SCALE:
- What is the dominant time constant?
- Days, weeks, months, years?
- This affects what feedback matters

Step 2: Map causal relationships

Create a causal loop diagram showing cause-effect relationships:

NOTATION:

Arrow: A → B means “A affects B”
- sign: Same direction (A increases, B increases)
- sign: Opposite direction (A increases, B decreases)

PROCESS:

Start with the problem variable
Ask: “What directly affects this?”
Draw arrows from causes to effects
For each arrow, determine polarity (+/-)
Ask: “Does this effect feed back to earlier causes?”
Continue until major relationships are mapped

CAUSAL LINK TYPES:

Positive link (+): Same direction

“Higher prices → Higher revenue” (+) [if demand holds]
“More training → Higher skill” (+)
“More customers → More word-of-mouth → More customers” (+)

Negative link (-): Opposite direction

“Higher prices → Lower demand” (-)
“More inventory → Less urgency to order” (-)
“More workload → Lower quality” (-)

DIAGRAM ELEMENTS:

Boxes or labels for variables
Arrows showing direction of causation
+/- signs on arrows showing polarity
R/B labels for feedback loops (added in next step)

QUALITY CHECKS:

Each link has clear causal mechanism
Polarity is correct (test: if A doubles, what happens to B?)
No missing intermediate variables
Important delays are noted

Step 3: Identify feedback loops

Find and classify feedback loops in the causal structure:

LOOP IDENTIFICATION:

Start at any variable
Follow arrows until you return to the starting variable
This is a feedback loop
Classify the loop type

LOOP TYPES:

REINFORCING LOOP (R): Self-reinforcing, exponential

Count the negative signs in the loop
EVEN number of negatives (or zero) = Reinforcing
Creates exponential growth OR exponential decline
“Vicious cycle” or “Virtuous cycle”

Example: Compound Interest Savings → (+) Interest Earned → (+) Savings This is reinforcing: growth begets more growth

Example: Bank Run Fear → (+) Withdrawals → (+) Bank Instability → (+) Fear Reinforcing: fear creates more fear

BALANCING LOOP (B): Goal-seeking, stabilizing

ODD number of negative signs = Balancing
Seeks equilibrium, resists change
Creates oscillation if delays exist
“Checks and balances”

Example: Thermostat Temperature Gap → (+) Heating → (+) Temperature → (-) Temperature Gap This is balancing: seeks the goal temperature

Example: Market Price Price → (-) Demand → (-) Inventory → (-) Price Balancing: price adjusts to clear inventory

FOR EACH LOOP:

Name the loop (descriptive)
Classify as R (reinforcing) or B (balancing)
Describe the behavior it drives
Note any significant delays in the loop
Assess loop strength (dominant or secondary?)

Step 4: Model stocks and flows

Identify accumulations (stocks) and rates of change (flows):

STOCKS: Things that accumulate over time

Physical: Inventory, cash, equipment
Intangible: Reputation, skills, relationships
Informational: Knowledge, data, backlog

Properties of stocks:

Change gradually (can’t jump instantly)
Create memory in the system
Create delays between cause and effect
Can only change through flows

FLOWS: Rates that change stocks

Inflows: Add to the stock
Outflows: Remove from the stock

Stock change = Inflows - Outflows

STOCK-FLOW STRUCTURE:

[Inflow] → [STOCK] → [Outflow]

Example: Workforce [Hiring Rate] → [EMPLOYEES] → [Attrition Rate]

Example: Technical Debt [Debt Creation Rate] → [TECHNICAL DEBT] → [Debt Paydown Rate]

FOR EACH STOCK:

Name the stock (noun - what accumulates)
Identify inflows (what adds to it)
Identify outflows (what depletes it)
Estimate typical time to fill/drain
Note current level (high, medium, low)

LINKING TO CAUSAL LOOPS:

Stocks often appear in feedback loops
The stock level affects the flows
This creates the feedback structure

Step 5: Recognize system archetypes

Identify common system behavior patterns:

ARCHETYPE 1: LIMITS TO GROWTH Structure:

Reinforcing loop drives growth
Balancing loop creates limiting constraint
Initially growth dominates
Eventually constraint dominates

Behavior: S-curve growth, then plateau or decline

Example: New product adoption

R: Success → Word of mouth → More customers → More success
B: Market saturation → Fewer remaining prospects → Slower growth

Leverage: Focus on the constraint before it bites

ARCHETYPE 2: SHIFTING THE BURDEN Structure:

Symptom prompts quick fix
Quick fix reduces symptom
But undermines fundamental solution
Dependency on quick fix grows

Behavior: Short-term improvement, long-term degradation

Example: Technical debt

Quick fix: Skip tests to ship faster
Fundamental solution: Build quality processes
Side effect: More bugs → more quick fixes needed

Leverage: Invest in fundamental solution; avoid quick fixes

ARCHETYPE 3: FIXES THAT FAIL Structure:

Fix addresses symptom
Unintended consequence makes problem worse
After delay, problem returns stronger

Behavior: Oscillation, worsening over time

Example: Firefighting mode

Fix: Heroic effort to solve crisis
Consequence: No time for prevention
Delay: Next crisis is worse

Leverage: Break the link to unintended consequences

ARCHETYPE 4: SUCCESS TO THE SUCCESSFUL Structure:

Two activities compete for resources
Success in one attracts more resources
Less resources for the other
Winner takes all

Behavior: One grows, other shrinks; lock-in

Example: Internal project competition

Successful project gets more budget
Other project starves
Eventually only “winner” survives

Leverage: Create separate resource pools; explicit portfolio balance

ARCHETYPE 5: TRAGEDY OF THE COMMONS Structure:

Shared resource benefits individual users
Each user gains by using more
Total use depletes the resource
Everyone loses

Behavior: Resource depletion, collapse

Example: Technical infrastructure

Each team benefits from using shared service
No one invests in maintenance
Service degrades, everyone suffers

Leverage: Regulate the commons; assign ownership; align incentives

ARCHETYPE 6: ESCALATION Structure:

One party acts to gain advantage
Other party responds to restore balance
First party escalates further
Arms race ensues

Behavior: Mutual escalation, exhaustion

Example: Price war

Company A cuts price
Company B matches
Company A cuts more
Both margins destroyed

Leverage: Refuse to play; find non-zero-sum alternatives

ARCHETYPE 7: GROWTH AND UNDERINVESTMENT Structure:

Growth creates demand for capacity
Underinvestment in capacity
Performance degrades
Demand drops, “justifying” low investment

Behavior: Growth stalls, self-fulfilling low expectations

Example: Startup scaling

Growth creates load on infrastructure
Don’t invest in scaling infrastructure
Site becomes slow
Customers leave, growth stops

Leverage: Invest ahead of demand; watch performance standards

Step 6: Identify leverage points

Find high-leverage intervention points:

LEVERAGE POINTS (from least to most powerful):

CONSTANTS AND PARAMETERS Numbers: taxes, subsidies, standards Easy to change but usually low leverage Example: Adjusting price by 5%
BUFFER SIZES Stabilizing stocks relative to flows Example: Increasing inventory safety stock
STRUCTURE OF STOCKS AND FLOWS Physical structure of the system Example: Adding a new warehouse location
DELAYS Length of delays relative to system change rate Example: Faster feedback loops
BALANCING FEEDBACK LOOPS Strength of stabilizing loops Example: Stronger quality control
REINFORCING FEEDBACK LOOPS Strength of growth/decline drivers Example: Reducing viral coefficient
INFORMATION FLOWS Who has access to what information Example: Making performance data visible
RULES OF THE SYSTEM Incentives, constraints, agreements Example: Changing how bonuses are calculated
POWER TO ADD/CHANGE RULES Self-organization, evolution Example: Enabling teams to set their own processes
GOALS OF THE SYSTEM Purpose and direction Example: Shifting from growth to sustainability
PARADIGM/MINDSET Deep beliefs and assumptions Example: From “resources are scarce” to “abundance”
TRANSCENDING PARADIGMS Not being attached to any paradigm Highest leverage, hardest to achieve

FOR THE ANALYZED SYSTEM:

List potential intervention points
Classify by leverage level
Assess feasibility (can we actually change this?)
Assess unintended consequences
Prioritize: High leverage + Feasible + Low risk

Step 7: Analyze interventions

Evaluate potential interventions and predict effects:

FOR EACH POTENTIAL INTERVENTION:

DESCRIBE THE INTERVENTION:
- What specific change would be made?
- Which leverage point does it target?
- What mechanism do we expect?
TRACE DIRECT EFFECTS:
- What variables change immediately?
- What is the expected direction and magnitude?
TRACE FEEDBACK EFFECTS:
- Which feedback loops are affected?
- Do reinforcing loops amplify the change?
- Do balancing loops counteract it?
- How strong are these effects?
CONSIDER DELAYS:
- How long until effects are visible?
- Will there be oscillation during transition?
- Could we declare failure before success appears?
IDENTIFY UNINTENDED CONSEQUENCES:
- What side effects might occur?
- Which archetypes might we trigger?
- Who else is affected and how might they respond?
COMPARE TO DOING NOTHING:
- What happens if we don’t intervene?
- Is the system self-correcting?
- Is patience an option?

INTERVENTION EVALUATION:

Intervention	Leverage	Feasibility	Time to Effect	Confidence	Unintended Risks
…	…	…	…	…	…

RECOMMENDATION:

Which interventions should be pursued?
In what sequence?
What should we monitor to verify effects?

When to Use

When dealing with problems that resist simple solutions
When interventions seem to make things worse
When symptoms keep recurring despite treatment
For understanding organizational dynamics
When there are long delays between action and effect
For strategic planning in complex environments
When multiple stakeholders with conflicting goals interact
For understanding market dynamics and competition
When exponential growth or decline is observed
For policy analysis with broad systemic effects

Verification

System boundary is clearly defined
Causal links have clear mechanisms (not just correlations)
Feedback loops are correctly classified (R vs B)
Stocks and flows are properly distinguished
Relevant archetypes are identified
Leverage points are prioritized by actual leverage
Interventions are traced through feedback effects

Input: $ARGUMENTS

Apply this procedure to the input provided.