What Is Systems Thinking? A Beginner's Guide
Most problem-solving follows a simple pattern: identify the problem, find the cause, fix it. Traffic congestion? Build more lanes. Healthcare costs rising? Cap prices. Low employee morale? Introduce incentive programs. This linear thinking—tracing straight lines from cause to effect—feels intuitive and appears logical.
But reality rarely cooperates. The new highway lanes fill with traffic within months. Price caps create shortages and black markets. Incentive programs produce gaming and resentment. The "solutions" sometimes make problems worse, or create entirely new problems elsewhere in the system. Linear thinking fails because real-world problems don't exist in isolation—they're embedded in systems where everything connects to everything else.
Systems thinking offers a different approach: instead of treating problems as isolated events with simple causes, it examines the relationships, patterns, and structures that produce behavior over time. Rather than asking "What caused this?" it asks "What pattern of interactions creates this behavior?" This shift in perspective—from parts to wholes, from linear chains to circular loops, from events to patterns—fundamentally changes how we understand and intervene in complex situations.
This guide introduces systems thinking fundamentals for people new to the concept. We'll explore what systems are, how they behave, why linear thinking fails, and how to develop a systems perspective. The goal isn't mastery—that takes years—but familiarity with core concepts and the beginning of a different way of seeing.
What Is a System?
A system is a set of interconnected elements organized to achieve a purpose. Three components define any system:
- Elements: The parts or components (people, institutions, physical objects, information)
- Interconnections: The relationships, flows, and feedback loops linking elements
- Purpose: The system's function or goal (which may differ from stated intentions)
The human body is a system: organs (elements) connected by blood vessels and nerves (interconnections) to maintain life (purpose). A business is a system: departments (elements) connected by workflows and information (interconnections) to create value (purpose). A forest is a system: trees, soil, fungi, animals (elements) connected by nutrient cycles and ecological relationships (interconnections) to sustain and reproduce life (purpose).
What Makes Systems Interesting
Systems have properties that their parts don't have individually—properties that emerge from interactions:
- Water emerges from hydrogen and oxygen atoms arranging in particular ways; neither hydrogen nor oxygen is wet
- Traffic jams emerge from individual drivers' behavior; no single driver creates the jam
- Economic recessions emerge from interactions of millions of decisions; no single person decides "let's have a recession"
- Culture emerges from repeated social interactions; no individual embodies the entire culture
Systems also exhibit behaviors that seem puzzling when viewed through linear cause-effect lenses:
- Delays: Actions and consequences separated in time create confusion about causation
- Feedback: Effects circle back to influence causes, creating self-reinforcing or self-correcting dynamics
- Leverage: Small changes in certain places produce large effects; big efforts elsewhere accomplish little
- Resilience: Systems maintain function despite disruption, but may suddenly collapse when pushed too far
- Unintended consequences: Interventions produce unexpected effects because we missed how parts connect
Understanding these properties requires seeing systems as wholes, not collections of parts.
Linear vs. Systems Thinking
Linear thinking traces straight lines from causes to effects:
A causes B → B causes C → C causes D
This works reasonably well for simple, predictable situations with few interacting parts. If you push a button (A) and a light turns on (B), linear thinking suffices. The relationship is direct, immediate, and consistent.
But most situations involve circular causality—effects feeding back to influence causes:
A influences B
↑ ↓
D ←——— C
Changes propagate through loops, creating behavior that linear thinking can't explain or predict.
Example: Diet and Weight
Linear thinking: "I'm gaining weight because I eat too much. I'll eat less and lose weight."
This might work briefly, but often weight returns or the person regains more than they lost. Linear thinking blames "lack of willpower" and prescribes "trying harder."
Systems thinking identifies interconnected factors:
- Metabolism: Reduced eating triggers metabolic slowdown (the body adapts to perceived scarcity)
- Hunger signals: Lower calorie intake increases hunger hormones (biological compensation)
- Psychological factors: Restriction creates cravings and stress eating (behavioral compensation)
- Social environment: Food-centered social situations create pressure to eat (environmental factors)
- Time delays: Weight loss initially succeeds, creating false confidence; metabolic adaptation takes weeks to manifest
These factors form feedback loops:
Restrict eating → Initial weight loss → Increased confidence → Relaxed restrictions
↓
Metabolic slowdown → Greater weight gain ← Return to normal eating ←——┘
Linear thinking sees "eating causes weight" and prescribes "eat less." Systems thinking sees feedback loops between eating, metabolism, psychology, and environment, recognizing that simple restriction often triggers compensatory mechanisms that defeat the original goal. Effective interventions address multiple leverage points: not just calories, but metabolic health, hunger regulation, stress management, and environmental design.
The Limits of Linear Thinking
Linear thinking fails when:
- Feedback loops dominate: Effects circle back to influence causes
- Delays obscure connections: Causes and effects separated in time
- Multiple interactions occur: Many elements influence each other simultaneously
- Emergence creates surprises: Interactions produce unexpected system-level behavior
- Interventions have ripple effects: Changing one part affects distant parts
In these situations—which describe most real-world problems—linear thinking produces incomplete analysis and ineffective interventions.
Core Systems Thinking Concepts
Feedback Loops
Feedback loops occur when change in one element eventually influences itself through a chain of connections. Two types dominate:
Reinforcing (positive) feedback amplifies change, creating growth or collapse:
- Compound interest: Money earns interest → More money → More interest earned → Even more money (exponential growth)
- Viral spread: Infected people spread disease → More infected people → Even more spreading → Epidemic (exponential growth)
- Panic selling: Stock drops → Fear increases → More selling → Stock drops further → Greater fear (collapse)
- Skill development: Practice improves skill → Better results → More motivation → More practice → Greater skill (virtuous cycle)
Reinforcing loops explain rapid change—both growth and decline. They're inherently unstable: they accelerate until something limits them.
Balancing (negative) feedback counteracts change, creating stability or resistance:
- Thermostat: Temperature rises → Heating turns off → Temperature drops → Heating turns on (maintaining target temperature)
- Inventory management: Stock runs low → Order more → Stock rises → Stop ordering (maintaining buffer)
- Hunger regulation: Blood sugar drops → Feel hungry → Eat food → Blood sugar rises → Stop feeling hungry (maintaining energy)
- Market prices: Prices rise → Demand falls → Prices drop → Demand increases (seeking equilibrium)
Balancing loops explain stability and resistance to change. They maintain conditions within ranges, creating equilibrium-seeking behavior.
Real-World Example: Housing Market Boom and Bust
Reinforcing loops during the boom (2003-2006):
Rising prices → Buyers rush to buy before further increases → More demand → Prices rise faster
↓
Homeowners refinance → Extract equity → Spend money → Economic growth → Easier lending
↓
Rising prices → Banks see housing as safe → Lend more easily → More buyers → Prices rise
Balancing loops emerge eventually:
Rising prices → Homes become unaffordable → Fewer buyers → Prices stabilize or fall
↓
Falling prices → Homeowners underwater → Defaults increase → Foreclosures → More supply → Prices fall further
↓
Falling prices → Banks tighten lending → Fewer buyers → Prices continue falling
The reinforcing loop drove rapid growth; the balancing loops created the bust. Systems thinkers recognize that every boom contains the seeds of its correction because reinforcing growth eventually triggers balancing forces (affordability limits, resource constraints, saturation).
Stocks and Flows
Stocks are accumulations—quantities that can be measured at any moment:
- Water in a bathtub
- Money in a bank account
- CO₂ in the atmosphere
- Knowledge in a person's mind
- Trust in a relationship
Flows are rates of change—quantities per time period:
- Water flowing into or out of tub
- Income flowing in, expenses flowing out of account
- Emissions flowing into atmosphere, absorption flowing out
- Learning new information, forgetting old information
- Acts that build or erode trust
Stocks change through flows. Understanding this distinction clarifies many confusions:
Traffic congestion (a stock) builds when inflow (cars entering highway) exceeds outflow (cars exiting). Building more lanes increases outflow capacity temporarily, but if this makes driving more attractive, it also increases inflow, eventually refilling the expanded highway. The stock (congestion) returns to equilibrium determined by balancing forces.
Government debt (a stock) changes based on deficits (inflow when spending exceeds revenue) and surpluses (outflow when revenue exceeds spending). Reducing debt requires sustained outflow (surpluses) over time; a single year's surplus barely affects the accumulated stock.
Climate change (stock of atmospheric CO₂) continues even if emissions (inflow) drop, because the existing stock persists. Stabilizing climate requires reducing inflow to match outflow (natural absorption), then sustaining that balance for decades while the accumulated stock slowly decreases.
Stocks create inertia and momentum—they change slowly even when flows change quickly. This explains why problems persist long after we "fix" the immediate cause.
Delays
Delays—gaps between actions and consequences—create misunderstanding and poor decisions:
Example: Steering a boat: When you turn the wheel, the boat doesn't immediately turn. Inexperienced sailors turn harder, then the delayed effect kicks in and the boat overturns. Experienced sailors turn gently, wait for the delayed response, then adjust. They've learned to anticipate delays.
Example: Antibiotic resistance: Overprescribing antibiotics seems harmless in the short term—patients feel better. The consequence (resistant bacteria) appears years later through a different mechanism (evolutionary selection). The delay between action and consequence obscures the causal connection, allowing harmful behavior to continue until the delayed cost becomes unavoidable.
Example: Infrastructure decay: Deferred maintenance saves money immediately. Consequences (collapsed bridges, failed water systems) emerge 10-20 years later. The delay allows the choice to seem costless, creating persistent underinvestment until catastrophic failure forces action.
Delays cause several problems:
- Oscillation: Overreacting to delayed feedback creates swings above and below the target
- Missed connections: Long delays hide causal links, making problems appear unrelated to earlier decisions
- Short-termism: Immediate benefits with delayed costs incentivize harmful choices
- Sudden crises: Slow accumulation of unseen problems produces sudden, seemingly unexpected collapse
Effective systems thinking accounts for delays, resisting the urge to push harder when effects don't appear immediately.
Leverage Points
Leverage points are places where small changes produce large effects. Not all interventions are equally effective; some touch sensitive parts of system structure while others push against powerful resistance.
Donella Meadows identified a hierarchy of leverage points from weakest to strongest:
Weak leverage (common but ineffective):
- Numbers (subsidies, taxes, standards): Easy to adjust, but system structure limits impact
- Buffer sizes (reserves, inventories): Stabilize against fluctuation but don't change behavior
- Physical structure (building infrastructure): Visible and expensive but constrained by existing rules
Moderate leverage:
- Delays (relative to rates of change): Shortening delays often improves responsiveness
- Balancing feedback strength: Faster correction loops improve stability
- Reinforcing feedback strength: Slowing harmful growth loops, accelerating beneficial ones
Strong leverage (rare but powerful):
- Information flows: Making consequences visible changes behavior (posting energy usage reduces consumption)
- System rules: Changing who decides, what counts, what's rewarded fundamentally alters behavior
- System goals: Redefining success reshapes everything that follows
- Paradigm shifts: Changing the fundamental assumptions underlying the system structure
Example: Reducing road deaths
Weak leverage: Public awareness campaigns, tougher penalties for infractions. These add forces but fight against existing system structure (cars, roads, human reaction times, competing incentives).
Moderate leverage: Improved emergency response (shorter delays between crash and treatment), better vehicle safety features (balancing feedback against injury).
Strong leverage: Redesigning roads to prevent high-speed crashes (roundabouts instead of intersections eliminate deadly T-bone collisions), separating vulnerable users (bike lanes, pedestrian zones), setting speed limits based on physics of collision survivability. These change the system structure itself rather than adding forces within existing structure.
Paradigm leverage: Redefining success from "moving cars quickly" to "safe mobility for all users." This fundamentally reshapes goals, which reshapes rules, which reshapes everything else.
Finding leverage requires understanding system structure—where the feedback loops are, what drives behavior, which goals dominate decisions.
Thinking in Systems: Practical Application
From Events to Patterns to Structure
Systems thinking moves through three levels of understanding:
1. Events: What just happened? (linear, reactive thinking)
- "Traffic was terrible today"
- "Our sales dropped this quarter"
- "The project missed its deadline"
This level offers no insight into causes or prevention. It's purely reactive.
2. Patterns: What trends over time? (seeing longer dynamics)
- "Traffic is consistently bad during rush hour"
- "Sales drop every fourth quarter"
- "Projects consistently miss deadlines"
Patterns reveal that events aren't isolated incidents but expressions of ongoing dynamics. This suggests the behavior comes from something deeper than random fluctuation.
3. Structure: What causes these patterns? (understanding system organization)
- "Traffic patterns reflect feedback between road capacity, housing density, and job locations—building more roads encourages distant housing, increasing total vehicle miles traveled"
- "Fourth quarter sales drops reflect yearly budget cycles where customers delay purchases knowing discounts will come"
- "Deadline misses reflect feedback between initial underestimates, scope creep, and poor visibility into actual progress"
Understanding structure reveals leverage points for intervention. Instead of responding to individual events or even patterns, you can modify the system structure producing the patterns.
Questions Systems Thinkers Ask
Rather than "What caused this event?" systems thinkers ask:
About relationships:
- What elements does this connect to?
- What influences this? What does this influence?
- Are there feedback loops? Reinforcing or balancing?
- Where are the delays between cause and effect?
About patterns:
- Has this happened before?
- What's the trend over time?
- Are there cycles, oscillations, or exponential changes?
- What patterns do we see across multiple events?
About structure:
- What system structure produces this behavior?
- What are the feedback loops driving this pattern?
- Where are stocks accumulating or depleting?
- What goals or incentives drive decisions?
- Where are leverage points for intervention?
About interventions:
- What might be unintended consequences?
- How might the system compensate for our intervention?
- Are we addressing symptoms or structure?
- What's the simplest intervention that touches high leverage?
Example: School Performance Problems
Event thinking: "Test scores dropped this year. We need to fire low-performing teachers and implement merit pay."
Pattern thinking: "Test scores have declined for five years. There's also rising teacher turnover, increasing class sizes, and declining student engagement. These patterns suggest systemic issues beyond individual teacher performance."
Structure thinking: "Several reinforcing loops are at work:
- Funding tied to test scores → Poor-performing schools lose funding → Larger classes, fewer resources → Worse performance → Less funding (vicious cycle)
- High-stakes testing → Teachers teach to test → Curriculum narrows → Students disengage → Performance suffers → More pressure to teach to test (vicious cycle)
- Low pay and poor conditions → High teacher turnover → Inexperienced staff → Worse student outcomes → More blame on teachers → Even worse conditions → Higher turnover (vicious cycle)
The system structure creates feedback loops that amplify rather than correct problems. Individual teacher performance exists within this structure but doesn't drive it."
Systems intervention: Rather than blaming individuals, change system structure:
- Break funding loop: Increase funding to struggling schools to reverse the vicious cycle
- Reduce testing pressure: Broader assessment methods reduce teaching to test, allowing richer curriculum
- Improve teacher conditions: Better pay and support reduce turnover, building experienced staff
- Strengthen balancing loops: Early intervention for struggling students, before problems compound
These interventions address system structure, not individual events.
Common Systems Thinking Mistakes
Mistake 1: Optimizing Parts Instead of the Whole
The error: Improving individual components without considering how they interact.
Example: Hospital emergency department trying to reduce wait times by speeding up triage. This creates a bottleneck at the next step (examination rooms) because faster triage fills rooms before patients can be treated and discharged. The constraint moved but overall wait time increased because the system wasn't optimized as a whole.
Systems approach: Identify the actual bottleneck constraining overall flow (in this case, treatment and discharge capacity), then optimize there. Optimizing non-constraints just moves the bottleneck without improving total throughput.
Mistake 2: Ignoring Delays
The error: Expecting immediate results from interventions and abandoning them when effects don't appear quickly.
Example: Education reforms implemented one year, judged failures the next year when test scores don't improve. But education has long delays—students currently being tested were educated under the old system. Improvements appear years later, after students have been educated under the new system for their entire schooling.
Systems approach: Map delays between intervention and outcome. Monitor leading indicators (are new practices being adopted?) rather than only lagging outcomes. Maintain interventions long enough for delayed effects to appear.
Mistake 3: Fighting Symptoms Instead of Causes
The error: Treating symptoms while leaving underlying structure intact, creating recurring problems.
Example: Company with employee burnout offers stress management workshops and mindfulness programs. Burnout briefly improves, then returns. The workshops treat symptoms (individual stress) while leaving causes intact (unrealistic workload, lack of control, insufficient resources). Without addressing system structure, problems recur.
Systems approach: Distinguish symptoms from root causes. Ask "What system structure produces this symptom?" Address feedback loops and incentives driving problematic behavior, not just the behavior itself.
Mistake 4: Creating New Problems While Solving Old Ones
The error: Failing to anticipate how interventions ripple through the system, creating unintended consequences.
Example: Pesticides introduced to solve insect crop damage. Initially effective, but killed natural predators along with pests. Pests developed resistance faster than predators could recover. Within years, pest problems worse than before pesticide use, and new pests emerged in absence of their predators. The "solution" created new, worse problems.
Systems approach: Before implementing solutions, ask "How might this intervention change feedback loops? What balancing forces might it trigger? Who/what benefits and who/what pays? What's the second-order effect?" Test interventions on a small scale to observe unintended consequences before full deployment.
Mistake 5: Seeking Simple Solutions to Complex Problems
The error: Demanding single interventions that "fix" problems created by complex system dynamics.
Example: Urban homelessness attributed to "mental illness and drug addiction" with proposed solution of mandatory treatment. But homelessness emerges from feedback between housing costs, wages, healthcare access, social support, employment opportunities, and individual vulnerabilities. "Fixing" one factor (individual treatment) while leaving system structure intact (unaffordable housing, inadequate wages, poor social safety net) yields minimal improvement because the system structure continues producing homelessness.
Systems approach: Recognize that complex problems have no simple solutions. Multiple interventions at different leverage points, sustained over time, produce gradual improvement. Seek "good enough" interventions that push the system toward better states rather than "perfect" solutions that don't exist.
Developing Systems Thinking Skills
Systems thinking is a practice developed over years, not a technique learned in a week. Here are entry points:
Practice 1: Identify Feedback Loops
For any situation, ask:
- What reinforcing loops might be operating? (growth or decline spirals)
- What balancing loops provide stability or resistance?
- Are there delays between cause and effect?
Exercise: Choose a current problem (personal or professional). Draw the feedback loops you can identify. Include both reinforcing loops (amplifying change) and balancing loops (resisting change).
Practice 2: Look for Patterns Over Time
Instead of reacting to individual events, track behavior over weeks, months, or years:
- Are there trends (steady increase/decrease)?
- Cycles (regular fluctuations)?
- Thresholds (sudden shifts after gradual build-up)?
Exercise: Take data you track regularly (spending, exercise, work hours, mood). Graph it over six months. What patterns emerge? What might explain them?
Practice 3: Distinguish Structure from Blame
When problems occur, resist blaming individuals. Ask instead:
- What system structure makes this behavior logical or inevitable?
- What feedback loops and incentives drive decisions?
- How would someone in this role be expected to behave?
Exercise: Think of a recurring workplace problem often blamed on individuals (missed deadlines, quality issues, communication breakdowns). What system structure (workload, processes, incentives, information flows) makes this outcome likely regardless of who fills the role?
Practice 4: Consider Unintended Consequences
Before implementing solutions, imagine how the system might respond:
- What balancing forces might oppose your intervention?
- What might be indirect effects on distant parts of the system?
- Could the intervention shift the problem elsewhere or create new problems?
Exercise: For any planned change (policy, process, habit), brainstorm at least five potential unintended consequences—ways the intervention could fail or backfire.
Practice 5: Find Leverage Points
Not all interventions are equally effective. Before acting, ask:
- Am I pushing against strong resistance or working with system dynamics?
- Am I treating symptoms or addressing underlying structure?
- Where might small changes produce large effects?
Exercise: For a persistent problem, list ten possible interventions. For each, assess: Is this high, medium, or low leverage? Am I changing numbers, flows, feedback loops, rules, or goals? Focus on the highest-leverage options.
Practice 6: Embrace Humility and Experimentation
Systems thinking reveals complexity and uncertainty—we can't predict all consequences of interventions. This requires:
- Humility: Acknowledging we don't fully understand the system
- Small experiments: Testing interventions on a limited scale first
- Monitoring: Watching for unexpected effects
- Adaptation: Adjusting based on what we learn
Exercise: For your next planned change, design a small-scale test. What will you measure to evaluate success? What would indicate unintended consequences? How will you adjust based on results?
When to Use Systems Thinking
Systems thinking isn't needed for every problem—sometimes linear approaches work fine. Use systems thinking when:
1. Problems recur despite "fixes": If the same issue returns repeatedly, you're treating symptoms while underlying structure persists. Systems thinking identifies structural causes.
2. Solutions create new problems: If interventions produce unintended consequences, you're missing feedback loops and interconnections. Systems thinking maps ripple effects.
3. Multiple stakeholders with conflicting views: If different people see the same situation completely differently, they're viewing different parts of a larger system. Systems thinking integrates perspectives.
4. Delayed consequences obscure causes: If actions and effects separate in time (years between policy change and outcome), linear thinking misses connections. Systems thinking accounts for delays.
5. The problem feels "complex": If you intuitively sense the situation involves many interacting factors without clear causal chains, systems thinking offers appropriate tools.
Conversely, don't use systems thinking for:
- Simple, direct cause-effect relationships with immediate feedback
- Problems with clear, isolated causes amenable to straightforward solutions
- Situations where quick action matters more than deep understanding
- Cases where the system is well understood and linear interventions reliably work
Systems thinking is powerful but comes with costs (time, mental effort, dealing with uncertainty). Use it strategically for problems where its unique value justifies the investment.
Key Takeaways
Systems thinking fundamentals:
- Systems are interconnected elements organized for a purpose, producing emergent behavior from interactions
- Feedback loops (reinforcing and balancing) drive system behavior over time
- Delays between cause and effect obscure connections and create poor decisions
- Stocks and flows explain inertia—why problems persist even after "solutions" are implemented
- Leverage points vary greatly—some interventions accomplish much with little effort; others waste effort fighting resistance
How systems thinking differs from linear thinking:
- Sees circular causality instead of straight-line cause-effect
- Focuses on patterns over time rather than isolated events
- Examines system structure producing behavior rather than blaming individuals
- Anticipates unintended consequences and ripple effects
- Seeks to understand before intervening, embracing complexity rather than demanding simple solutions
Common mistakes to avoid:
- Optimizing parts instead of the whole
- Ignoring delays between action and consequence
- Fighting symptoms while leaving structure intact
- Creating new problems while solving old ones
- Seeking simple solutions to complex problems
Developing systems thinking skills:
- Identify feedback loops (reinforcing and balancing)
- Look for patterns over time (trends, cycles, thresholds)
- Distinguish system structure from individual blame
- Consider unintended consequences before intervening
- Find high-leverage points for intervention
- Embrace humility, experimentation, and learning
Systems thinking doesn't guarantee correct answers—complex systems remain uncertain. But it provides a richer, more realistic understanding of how change happens, why problems persist, and where interventions might work. Over time, this perspective becomes a habit: automatically looking for connections, patterns, and feedback loops; questioning simple explanations; and expecting that today's solutions might become tomorrow's problems.
The journey from linear to systems thinking involves learning to see complexity without feeling overwhelmed by it, developing comfort with uncertainty, and embracing the reality that most meaningful change is gradual, multi-faceted, and requires sustained attention to system structure rather than quick fixes aimed at symptoms.
References and Further Reading
Meadows, D. H. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing. DOI: 10.1057/palgrave.emr.1500093
Senge, P. M. (1990). The Fifth Discipline: The Art & Practice of the Learning Organization. Doubleday.
Sterman, J. D. (2000). Business Dynamics: Systems Thinking and Modeling for a Complex World. McGraw-Hill. DOI: 10.1108/13673270210417646
Forrester, J. W. (1961). Industrial Dynamics. MIT Press.
Ackoff, R. L. (1999). Ackoff's Best: His Classic Writings on Management. John Wiley & Sons. DOI: 10.1002/sres.3850160110
Kim, D. H. (1999). "Introduction to Systems Thinking." Pegasus Communications, Waltham, MA.
Richmond, B. (1993). "Systems Thinking: Critical Thinking Skills for the 1990s and Beyond." System Dynamics Review 9(2): 113-133. DOI: 10.1002/sdr.4260090203
Stroh, D. P. (2015). Systems Thinking for Social Change. Chelsea Green Publishing.
Meadows, D. H. (1999). "Leverage Points: Places to Intervene in a System." The Sustainability Institute. DOI: 10.1080/13504509709469924
Checkland, P. (1999). Systems Thinking, Systems Practice. John Wiley & Sons. DOI: 10.1002/sres.3850120211
Jackson, M. C. (2003). Systems Thinking: Creative Holism for Managers. John Wiley & Sons. DOI: 10.1002/sres.2290050110
Gharajedaghi, J. (2011). Systems Thinking: Managing Chaos and Complexity: A Platform for Designing Business Architecture (3rd ed.). Morgan Kaufmann. DOI: 10.1016/B978-0-12-385915-0.00001-1
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