I used to think understanding meant knowing all the pieces. If I could master each component and parse through the "hidden fog," I believed I could understand the whole.
Inevitably, this fragmented my attentional resources and undermined my ability to adequately absorb and synthesize material.
Then... I started studying complex systems.
What I discovered inverted everything: the whole is not just more than the sum of its parts, it is fundamentally different from its parts. You can know every neuron in a brain and still not understand consciousness. You can know every player on a team and still not predict how they'll perform together at game time. You can memorize every fact in a field and still miss the underlying structure. I feel like my own knowledge of neuroscience, despite having a doctorate, to be very limited in scope.
Instead of feeling down on my own ability to synthesize the universe's knowledge bank, instead I began to focus on some of the more interesting properties, the ones that matter, emerge from relationships, and not individual components themselves.
The Myth of the Individual Expert
We live in a world obsessed with expertise. The thought leader. The guru. The person who has all the answers. We build entire industries around individuals who supposedly "know everything" about their domain.
Thing is, no one knows everything. Not even close. It's more the illusion of knowing and sometimes a very well-rehearsed and repeated story that we just happened to listen to or hear for the very first time.
Herbert Simon won a Nobel Prize partly for formalizing why this must be true. He called it "bounded rationality" where human cognitive capacity is fundamentally limited. We can't process all available information, nor can we consider all possibilities. And we definitely can't maintain perfect recall...
And that's not a limitation. It's a feature.
Knowledge as a Network, Not a Container
In network science, we model systems as nodes (points) connected by edges (relationships). Social networks. Neural networks. Information networks. The same mathematical principles apply across scales.
When we apply this lens to knowledge, something fascinating emerges: knowledge doesn't live in isolated minds. It lives in the connections between them, the emergence of a collective knowledge that we all get to drawn from, despite only holding pieces of the universal puzzle.
Philosophers like Andy Clark and David Chalmers formalized this as the "extended mind" thesis. To their point, they consider that our minds don't stop at our skulls, but extend into our tools, our notebooks, our colleagues, our networks. Cognition is distributed across the system.
Think about it:
- You know A, B, and C
- I know C, D, and E
- Someone else knows E, F, and G
Individually, we each have limited knowledge. But collectively? We have access to A through G, and more importantly, we have the connections between these domains that none of us would see alone.
The real magic isn't in what each node knows. It's in what the network knows.
The Power of Weak Ties
One of the most counter-intuitive findings in network science comes from sociologist Mark Granovetter's work on "the strength of weak ties", published in 1973. His research showed that the people you're loosely connected to (weak ties) are often more valuable for information flow and new opportunities than your close connections (strong ties).
Your close friends are probably more willing to help you than casual acquaintances, so it doesn't really have anything to do about motivation. What's underlying that is another layer.
The issue is actually structural: your strong ties swim in the same information pools you do. They know what you know, run in your circles, encounter the same opportunities. Weak ties, by contrast, bridge to entirely different network clusters. They have access to genuinely novel information that simply doesn't exist in your immediate circle, no matter how helpful those close connections want to be.
This is why:
- The person you met once at a conference might have the perfect solution to your problem
- A casual conversation can spark breakthrough insights
- Diverse teams outperform homogeneous "expert" teams on complex problems
The connections themselves carry information that no single node possesses.
Knowledge Transfer as Transformation, Not Transmission
Traditional models of knowledge transfer treat it like moving objects: expert has knowledge → expert transmits → recipient receives → knowledge transferred. Simple, linear, one-directional.
But systems thinking reveals something more interesting. When knowledge moves between minds, it doesn't copy. It transforms. Here's what actually happens:
- Activation: Your idea activates patterns in my existing knowledge network
- Integration: It combines with my unique context, experience, and constraints
- Transformation: Something genuinely new emerges from the collision
- Propagation: This transformed understanding moves outward through my connections, where it transforms again
Each person in the network isn't a passive repository. They're an active transformation node. The "same" knowledge moving through different minds becomes different knowledge, shaped by each cognitive context it passes through.
This is why teaching deepens your own understanding. Why explaining your work to someone from a different field generates new insights. Why the best collaborations feel like you're discovering ideas together rather than exchanging pre-formed thoughts.
The knowledge that emerges from collective interaction often surprises everyone involved. No one person created it. It arose from the interaction itself: a genuinely emergent property of the system.
Building Better Knowledge Networks
If knowledge lives in networks, then the quality of our collective intelligence depends on the structure of our networks. Here are a couple key things we need to keep in mind from network science:
Diversity beats homogeneity. Networks with high cognitive diversity (different backgrounds, expertise, perspectives) don't just have more information. They create more productive collisions between ideas. Mathematician Scott Page proved this rigorously: diverse groups consistently outperform homogeneous groups of higher-ability individuals on complex problem-solving tasks.
Bridge builders enable synthesis. The people who connect different clusters, who translate between domains and speak multiple "languages," don't just pass information along. They create the conditions for synthesis.
Redundancy creates resilience. In robust networks, multiple paths connect any two points. Instead of thinking of this as a form of wasteful duplication, think of it more like a strategic architecture.
The social network becomes a distributed memory system, just like your brain. And just like your brain, the power isn't in perfect storage, but in the connections that allow for its reconstruction, and beyond.
I don't know everything. Neither do you. And that's precisely the point.