When we step into the study of systems, it quickly becomes clear that the greatest obstacle isn’t always the system’s complexity, but the lenses through which we view it. Two people can face the same situation, examine the same data, and yet walk away with utterly different conclusions. The reason lies not in the numbers or events themselves, but in the filters of perception—our mental models and the paradigms we inherit. These cognitive filters decide what stands out and what fades, what feels urgent and what feels irrelevant. To work effectively with systems, we must learn to see not only the dynamics themselves, but the ways in which our perceptions shape them.

Mental Models and Paradigms as Filters

Mental Models

Think of mental models as the internal maps each of us carries about how the world works. They are assembled from lived experience, education, and cultural conditioning. Because reality overwhelms us with more detail than we can possibly process, these models filter and simplify. The danger, of course, is that simplification can obscure what matters. A manager who assumes customers are driven solely by price will focus obsessively on competitor pricing but remain blind to signs that loyalty or identity may be driving purchasing decisions just as strongly.

Paradigms

If mental models operate at the individual level, paradigms live in the collective. They are the shared assumptions of a profession, a discipline, or a society. Paradigms set boundaries on what counts as knowledge, what questions are worth asking, and what kinds of solutions seem legitimate. Consider medicine: for centuries, the paradigm of “imbalanced humors” made bloodletting appear rational. Only when the paradigm shifted toward germ theory did bacteria become visible and antibiotics conceivable. Paradigms don’t just color interpretation—they determine which evidence can be seen at all.

Filters in Action

Together, mental models and paradigms act as lenses, shaping not only how people view events but what they believe to be real. This explains why two teams presented with the same data might land on entirely different strategies. They aren’t ignoring the facts; they’re seeing them through different filters.

Why People Reach Different Conclusions from the Same Data

Selective Attention and Framing

Data doesn’t arrive with its meaning attached; meaning is assigned. Selective attention ensures we notice details that align with our expectations while overlooking those that don’t. Framing then takes over, coloring how we interpret what we see. A dip in engagement numbers, for example, might be framed as evidence of a flawed product by one group and as a messaging problem by another. Both interpretations are reasonable within their chosen frames.

Confirmation and Causality

Our interpretations are further reinforced by confirmation bias: we give disproportionate weight to evidence that supports what we already believe. Different fields also bring different causal maps to the table. Where an economist might point to supply shocks as the explanation for inflation, a policymaker might emphasize demand pressures. Both are reading the same indicators but drawing divergent conclusions based on the causal frameworks they trust.

Paradigms of Validity

Finally, paradigms dictate what even counts as proof. One discipline may hold up statistical significance as the gold standard, while another prizes lived experience. When disciplines meet, their debates often mask a deeper tension: competing definitions of credibility.

Managing How We Frame Perspectives

Naming Assumptions

The first step in managing perception is to surface the assumptions shaping our interpretations. Saying aloud, “I’m approaching this as a cost problem,” brings the frame into the open and allows others to examine it. Without this act of naming, frames remain invisible and uncontested.

Reframing Deliberately

Once the frame is visible, we can deliberately test alternatives. Looking at a challenge through the lenses of efficiency, trust, or resilience will highlight different patterns and possibilities. Reframing is less about finding the single correct lens and more about expanding our repertoire of insight.

Inviting Contrasting Lenses

Disagreement should not be dismissed as obstruction. Instead, it can be treated as evidence that different filters are at play. Each contrasting view reveals part of the system that would otherwise remain hidden. In this way, disagreement becomes a source of diagnostic richness.

Making Assumptions Visible

Visualization tools such as causal loop diagrams and stock-flow diagrams are powerful precisely because they externalize what would otherwise remain in people’s heads. By drawing out assumptions and mapping them, groups can test competing frames against system behavior rather than arguing in the abstract.

Shifting Scale

Equally important is the ability to shift scale. Zooming in to see local detail and zooming out to understand broader context ensures we do not mistake a narrow frame for the whole system. This oscillation between scales keeps thinking supple.

Mismatched Perspectives and Recurring Friction

Different Problem Definitions

Recurring friction often arises not from bad faith, but from different definitions of the problem itself. In an organization with slipping sales, finance may declare it a cost issue, marketing may insist it’s an awareness issue, and product may diagnose a features issue. Each is acting rationally within their frame, yet the mismatch prevents alignment.

Conflicting Priorities

Even when groups agree on the problem, their values can diverge. Engineers may emphasize reliability, sales teams may push for speed, and operations may demand efficiency. Each is justified, but the competing priorities generate tension.

Blame Cycles and Communication Breakdowns

Over time, mismatched filters can spiral into blame cycles. Each side interprets outcomes as confirmation that the others are wrong, deepening resentment. Communication breaks down further when differences in perception are mistaken for incompetence or malice.

The Opportunity in Friction

The presence of friction is not a sign of failure—it is evidence that multiple filters are in play. Left unmanaged, these mismatches lock people into stalemates. Surfaced and integrated, they reveal blind spots and expand the field of possible action. The very frictions that feel like barriers can, if engaged thoughtfully, become the seeds of resilience.

Conclusion

Perception shapes systems every bit as much as flows of resources or feedback loops of information. Mental models and paradigms act as filters, highlighting some signals while erasing others, steering our sense of what matters and what action seems sensible. The work of systems thinking is not only to map structures and behaviors, but also to interrogate the lenses through which we view them. By naming, testing, and reframing these lenses, we transform blind conflict into constructive insight. And when recurring friction is recognized for what it is—a clash of filters rather than a clash of facts—it can become not a dead end but a turning point, the very source of breakthrough understanding.

Course Index

• Module 0: Introduction to Systems Thinking
  • Lesson 0.1 — What is a system?
  • Lesson 0.2 — Why Straight-Line Thinking Fails with Complex Problems
  • Lesson 0.3 — How Systems Thinking Turns Frustration Into Design Leverage
  • Lesson 0.4 — Notation primer & reference
• Module 1: Components of Systems
  • Lesson 1.1 — Elements, interconnections, and purpose
  • Lesson 1.2 – Open vs. closed systems
  • Lesson 1.3 — Boundaries and Perspectives
• Module 2: Feedback Loops and Causality
  • Lesson 2.1 — Reinforcing and balancing loops
  • Lesson 2.2 — Delays and non-linearity
  • Lesson 2.3 — Stocks and flows
• Module 3: Mental Models and Paradigms
  • Lesson 3.1 — How perception shapes systems