Rice & Rubber: Rethinking Optimization in an Uncertain World

Katie Shearn
May 20
6 min read

Updated: May 20

We live in a world that rewards control. We're taught to set goals, define metrics, streamline processes—and if we can just optimize enough, the thinking goes, we’ll get where we want to be.

I’ve been part of that mindset for years. I’ve built systems and strategies designed to improve performance and predict outcomes. And for a while, it worked. Efficiency did what it was supposed to do—especially in closed systems. Things became clearer, faster, more manageable.

But then life came out of left field.

Suddenly, I was facing a more uncertain path. Not because efficiency stopped working, but because my life wasn’t a closed system—and the tools of predictability I had relied on weren’t helping me meet the deeper shifts I was going through.

What I was learning in real time was this: predictability is a tool, not a goal.

So what happens when the thing you’re optimizing refuses to follow a pattern?What do you do when your carefully planned inputs don’t lead to the outcomes you expected?

That’s when a deeper question starts to surface: What if the world isn’t a machine—but a living, breathing, unpredictable ecosystem?

That’s the question I keep returning to—and one that Brian Klaas beautifully explores in his book Fluke: Chance, Chaos, and Why Everything We Do Matters. He introduces a metaphor that helped me put language around something I had lived—but couldn’t always articulate:

The difference between rubber problems and rice problems.

Rubber Problems: Built for Optimization

Brian Klaas tells the story of the Cantù people, who live on a small Indonesian island and make their living through both natural rubber harvesting and rice farming. These two livelihoods might seem similar on the surface—but one offers predictability, and the other demands creative problem-solving.

Rubber harvesting is straightforward. When tapped properly and maintained under stable conditions, rubber trees behave reliably. The process is mostly unaffected by unpredictable variables like weather, pests, or soil changes. It functions as a closed system, where cause and effect are clear, and efficiency makes sense.

In systems terms, the fewer external forces that impact a system, the more “closed” it becomes. Rubber production is reinforced by stable feedback loops, making it a great fit for optimization, consistency, and linear thinking.

Rubber problems are problems of precision. Their boundaries are defined. Their behavior is consistent. This is where control works best—its natural home is inside closed systems.

Rice Problems: Shaped by Randomness

Rice, however, is a different story.

For the Cantù people, growing rice means navigating a tangle of unpredictability. Rainfall, insect populations, soil shifts, and seasonal timing all shape the outcome. Even small changes can create cascading effects. These aren’t just external “disruptions”—they’re part of the living, open system that rice production belongs to.

Instead of relying solely on tools or data, the Cantù turn to a more intuitive and ecological method: observing sacred birds. These birds respond to the same environmental cues that affect rice viability. Their behavior signals when and where to plant.

What might look like superstition is actually a deep form of pattern recognition in an unpredictable environment.

What’s most powerful here is that the Cantù people didn’t try to force control within the rice system. Instead, they zoomed out—looking beyond the field to a wider system that included weather, animal behavior, and seasonal rhythms. By recognizing what was within their influence and what wasn’t, they learned to adapt without overreaching.

They worked with uncertainty—rather than against it.

Klaas uses this to make a powerful point: most of life’s important problems are rice problems. And yet, we often treat them like rubber ones—applying tools of control and linear logic to systems shaped by randomness, emergence, and interdependence.

When we do that, it’s not that we’re failing. It’s that we’re misreading the nature of the system—and using the wrong tool for the job.

Why Optimization Breaks in Open Systems

The deeper takeaway is this: optimization is a tool designed for closed systems. It’s meant for problems with clear inputs, stable rules, and predictable outcomes. That’s why it works so well in manufacturing, logistics, or processing—environments where cause and effect are consistent and measurable.

But in open systems—like ecosystems, economies, organizations, and human lives—randomness isn’t a flaw. It’s a feature.

These systems are constantly in motion, shaped by feedback loops, external forces, and interactions that can’t be reduced to neat formulas. Trying to optimize in these contexts often leads to rigid, brittle solutions that ignore the complexity of what’s actually unfolding.

A Real-Life Reflection

When I say, “Life is a rice problem—not a rubber one,” I’m speaking from experience.

I’ve lived through uncertainty—professionally, emotionally, and financially. I’ve tried to organize chaos, out-think timing, and map my way through life as if it would follow the plan. I’ve followed countless frameworks, life advisors, and coaching models. And when the outcomes didn’t align with the effort I was putting in, it was easy to start questioning my worth.

But over time—and especially through my work in systems thinking—I started to see things differently.

The “failures” weren’t reflections of my inadequacy. They were signs that I was using the wrong tool for the kind of system I was navigating.

What I’ve learned is that control doesn’t always create clarity, and more metrics don’t always lead to better decisions.

There’s a different kind of intelligence needed in open systems—one that doesn’t fight randomness, but learns how to move with it. Like the Cantù people watching the sacred birds, I’ve had to learn how to tune into signs, sense subtle shifts, and respond in ways that are relational, not mechanical.

I’ve applied this lesson in both my professional systems work and in the deeply personal spaces of my life. It’s the reason I’m building my website—to share what I’ve learned through lived experience, through real complexity, and through people like Brian Klaas who remind us that life isn’t a box to be measured and optimized. It’s an ecosystem to be navigated with care.

And in that kind of system, energy is sacred—and it should be spent on what’s authentic, not just what’s efficient.

The Hidden Cost of Efficiency

Efficiency is seductive. It promises speed, consistency, and control. But when we optimize only for what’s measurable, we often lose sight of the whole picture.

Efficiency is inherently linear. It requires us to break systems down into parts, identify bottlenecks, and engineer smoother paths. That works well in closed systems—where relationships between inputs and outputs are stable and predictable.

But as the examples above show, life isn’t a neatly measurable box. It doesn’t always yield to breakdown-and-fix logic. In complex, living systems, efficiency alone won’t carry us through.

And to be clear—I love efficiency.It’s always going to be part of my toolbox. It’s wired into how I think. I’m the kind of person who instinctively looks for the fastest route, the fewest steps, the cleanest solution. I’ve even joked, “I’m so efficient, I’m lazy!”—but let’s be honest: I’m definitely not lazy.

The difference now is, I’ve grown to see where efficiency thrives—and where it misleads. I no longer mistake it for the full answer. Instead, I’ve learned how to use it as a tool, not a crutch.

Because when we lean on optimization alone, we tend to lose something essential: Connection. Creativity. Curiosity.

None of those fit neatly into a spreadsheet, yet they’re central to what makes life meaningful. In the name of progress, we risk flattening the very richness we’re meant to preserve.

Reframing the Work!

This journey—from rubber to rice, from control to complexity—has helped me reshape not just how I think, but how I live.

Systems thinking has taught me to step back and ask: What kind of system am I in right now? Is this a place for optimization—or for openness? For efficiency—or emergence?

Because not all problems are meant to be solved by breaking them down. Some are meant to be witnessed, participated in, and slowly shaped through presence, pattern, and trust.

The more I live, the more I understand that success isn’t always a straight line, and certainty isn’t always the prize. In fact, some of the most meaningful moments in my life—personally and professionally—came from listening to uncertainty rather than trying to conquer it.

So now, when I find myself reaching for control, I pause.And I ask:

Am I solving a rubber problem—or a rice problem?
Is this a system that needs precision—or permission to unfold?
What’s trying to emerge that I haven’t measured yet?

These questions remind me that the world is not a closed system, and neither am I. We are part of something much more dynamic, relational, and alive.

And that, to me, is not a loss of control—it’s a gain of perspective.