A few months ago, I decided to learn to play the ukulele.

I imagined myself sitting on the patio on a warm summer evening, effortlessly strumming familiar songs. Reality turned out to be a little different.

One of the first chords I learned was C. It was almost disappointingly easy — one finger on one string. Within a few minutes I could make it ring clearly.

Then came G7.

Three fingers.

Three strings.

Three opportunities to get something wrong.

What surprised me wasn’t that G7 was harder. It was that I could switch easily from G7 back to C, but going the other direction — from C to G7 — felt like trying to solve a tiny puzzle every single time. My fingers knew where they wanted to go individually, but they refused to arrive there together.

I practiced.

I adjusted my hand position.

I even wondered whether I was holding the ukulele incorrectly.

Some evenings I spent fifteen minutes working on nothing but that one transition.

The strange part was that the harder I tried to make G7 perfect, the less progress I seemed to make.

Then one evening I stopped chasing perfection.

Not because I had mastered G7.

I hadn’t.

But I had learned it well enough.

So I turned the page in my lesson book.

I learned another chord.

Then another.

Before long I wasn’t just practicing isolated chords anymore.

I was playing songs.

Something unexpected happened.

Learning became fun again.

Looking back, I realized my mistake wasn’t practicing too little.

It was confusing perfecting a chord with learning an instrument.

That realization stayed with me because I’ve begun noticing the same pattern in places that have nothing to do with ukuleles.

One of my favorite books is Algorithms to Live By, by Brian Christian and Tom Griffiths.

In one chapter, the authors describe a study of life satisfaction during the first ten years of marriage. Researchers in Germany collected years of survey data from newly married couples and asked a deceptively simple question:

Given these observations, what mathematical model best predicts what will happen next?

At first glance, the answer seemed obvious.

Draw the model that best fits the data.

The original observations looked like this.

Figure 1. Life satisfaction during the first ten years of marriage.

A simple one-factor model captured the overall trend, but it clearly missed some of the rises and falls.

Adding a second factor produced a much better fit.

Then the researchers built a much more sophisticated model using nine different factors.

At first glance, it looked brilliant.

It hugged almost every point on the graph.

Every bump.

Every dip.

Every little wiggle.

If your goal was simply to explain the past, you probably would have declared it the winner.

But that’s not why we build models.

We build them to predict the future.

When the researchers extended the models beyond the ten years of observed data, something remarkable happened.

Figure 2. Comparing one-, two-, and nine-factor models.

The elaborate nine-factor model predicted that marital satisfaction would eventually collapse, plunging toward zero after about ten years.

The much simpler two-factor model predicted something quite different. Happiness still rose and fell — as life inevitably does — but gradually returned toward a person’s normal baseline. That prediction fit much better with what psychologists and economists already understood about how people adapt to life’s major events.

The simpler model made the better forecast.

The extra seven variables hadn’t uncovered a deeper truth.

They had simply mistaken noise for signal.

That idea stopped me in my tracks.

Like many people, I had always assumed that more information naturally produced better predictions.

Sometimes it does.

Sometimes it simply makes us more confident.

Forecasts are often misunderstood for the same reason.

Before the 2016 U.S. presidential election, many forecasting models gave Donald Trump roughly a 30 percent chance of winning. When he won, many people concluded that the forecasts had failed.

But a 30 percent chance isn’t zero.

If tomorrow’s weather forecast says there’s a 30 percent chance of rain and it rains, we don’t accuse the meteorologist of making a bad forecast.

We simply happened to live in the 30 percent world.

Good forecasters don’t promise certainty.

They estimate probabilities.

The more I read about forecasting, the more I realize it isn’t really about mathematics.

It’s about humility.

It’s about recognizing that the world is more complicated than we’d like it to be.

It’s about understanding that every additional variable doesn’t necessarily bring us closer to the truth.

Sometimes it carries us farther away.

Learning the ukulele has quietly taught me the same lesson.

When I spent all my time trying to perfect G7, I wasn’t really making progress.

I was polishing one small piece of a much larger puzzle.

The goal wasn’t to master one chord.

The goal was to learn how chords connect together into songs.

Forecasting isn’t much different.

The goal isn’t to explain every wiggle in the past.

The goal is to understand the pattern that is most likely to continue into the future.

There will always be another variable to add.

Another statistic to collect.

Another chord to practice.

The difficult part isn’t deciding whether more information exists.

It always does.

The difficult part is recognizing the moment when more information stops adding understanding and starts adding noise.

I’m still learning the ukulele.

My transitions still aren’t smooth.

My fingers still miss the occasional string.

But these days, I think less about perfecting the next chord.

I think more about learning the next song.

Inside the Box: Finding Certainty in a World of Constraints

Oscar Wilde wrote that “the true mystery of the world is the visible, not the invisible.” For much of my life, I assumed that certainty must lie somewhere beyond what we can see — hidden in secret laws, deeper causes, or final explanations. But the more I read, travel, and reflect, the more I am beginning to believe that the deepest truths are present all around us in ordinary patterns, if only we learn how to notice them.

This realization has emerged from several books that, at first glance, seem unrelated. In Sync, Steven Strogatz describes how fireflies flash together, heart cells beat in rhythm, and large systems spontaneously organize themselves. In A Brief History of the Universe and Our Place in It, Sarah Alam Malik tells the story of how a handful of physical laws shaped everything from the first atoms to galaxies, stars, and planets. And in Inside the Box, Drew Boyd and Jacob Goldenberg argue that creativity often flourishes not when constraints are removed, but when they are embraced.

That last idea particularly struck me.

We tend to think of freedom as the absence of limitations. We imagine that creativity requires unlimited choices and open-ended possibilities. But some of the most enduring works of art and science emerged under strict boundaries. Dr. Seuss wrote Green Eggs and Ham after accepting a challenge to use only fifty distinct words. The restriction did not stifle his imagination; it sharpened it.

Constraints, it turns out, do not eliminate possibility. They give possibility its form.

One of my favorite demonstrations of this principle took place on the Moon. During Apollo 15 in 1971, astronaut David Scott dropped a hammer and a falcon feather onto the lunar surface. In the absence of air resistance, both fell at exactly the same rate and struck the ground together. Two objects that seemed entirely different obeyed the same underlying law. Gravity treated them equally.

The lesson is both simple and profound. Beneath the world’s endless variety, there is order. The same law that guided a feather and a hammer on the Moon governs falling apples, orbiting planets, and the formation of galaxies. The universe is not arbitrary. It is creative within constraints.

A river without banks is not really a river. It is simply water spreading in every direction. The banks do not imprison the water. They create its path and give it power and direction.

The same may be true of our lives.

As an economic forecaster, I have spent years trying to make sense of uncertain outcomes using incomplete information. Models are constrained by assumptions, and data are always imperfect. Yet those limits do not render forecasting meaningless. They provide a framework within which patterns can be recognized and probabilities estimated.

As a writer, I have discovered that a blank page can be more intimidating than a structured theme. Boundaries — whether a word limit, a central metaphor, or a guiding question — focus thought and invite creativity.

And in relationships, constraints can be equally important.

I have watched several dear friends navigate the difficult questions that often arise later in life: where to live, how to spend their time, and what shape the years ahead should take. They have many options, and in theory that freedom should feel liberating. But an abundance of possibilities can sometimes create anxiety rather than peace. Too much freedom can be disorienting when there is no clear structure to guide the next step.

Perhaps this is one of the great paradoxes of human life. We do not always need fewer limits. Sometimes we need the right boundaries — the commitments, routines, and relationships that help us move forward.

This insight has changed the way I think about uncertainty.

Uncertainty is not the absence of order. It is the space in which order emerges. Small causes can still lead to large outcomes. Chance continues to shape our lives in unpredictable ways. But these events unfold within structures that channel possibility into meaningful forms.

The search for certainty, then, may not be a search for perfect knowledge. It may be a search for the patterns that become visible when uncertainty meets form.

The universe is governed by laws. Great stories are written within constraints. Rivers need banks. Lives need structure.

Oscar Wilde was right. The mystery is not hidden somewhere beyond our reach. It is visible all around us — in synchronized fireflies, in the birth of stars, in a feather and a hammer falling together on the Moon, and in the choices that give shape to our days.

We spend much of our lives trying to escape limitations. Yet perhaps our deepest freedom is found not outside the box, but within it.

I used to think of uncertainty as something distant — weather forecasts, markets, things too large or complex to fully understand.

But the more I pay attention, the more it feels closer than that.

Not distant at all.

Something that shows up in small moments — the ones we almost don’t notice.

There’s a story about Edward Lorenz, a meteorologist who was running a weather simulation.

At one point, he decided to rerun part of it.
Instead of entering the full number the computer had used — something like 0.506127 — he rounded it to 0.506.

A small difference. Barely noticeable.

He expected the same result.

Instead, the outcome was completely different.

That discovery became one of the foundations of chaos theory.

Not chaos as randomness — but chaos as sensitivity.

Small differences don’t stay small.
They expand.

It’s easy to keep that idea in the world of science — models, equations, weather systems.

But it doesn’t really stay there.

It shows up in ordinary life.

I think back to my first backpacking trip.

It was described as a beginner’s backpack across the Grand Canyon.

That one word — beginner — made me think I might be able to do it.

Without it, I’m not sure I would have signed up — or taken that first step at all.

That trip became my first real step into the outdoors.

Not a short hike, not a day trip — but something extended, something that asked more of me.

And it led to other trips, and other experiences that followed.

All from a single word that made something feel just within reach.

I still remember moments from that canyon.

A pink Grand Canyon rattlesnake, blending almost perfectly into the rock around it. A species found only there, protected in part because predators like peregrine falcons couldn’t easily see it from above.

And the squirrels.

On one side of the Colorado River, darker bodies with white tails.
On the other, lighter bodies with dark tails — what I later learned were Kaibab squirrels.

Separated by the river.
Shaped over time by different conditions.

Even the rims of the canyon tell that story.

The North Rim, about a thousand feet higher, holds more snow.
Different climate. Different pressures. Different outcomes.

It’s the same pattern, over and over.

Small differences in starting points.
Then divergence.

There’s a tendency to think in terms of control.

If I plan well enough, decide carefully enough, manage the risks — then I can shape the outcome.

But systems don’t always work that way.

Not weather systems.
Not landscapes.
Not life.

In complex systems, outcomes aren’t just the result of big decisions.

They’re shaped by initial conditions — tiny inputs, small variations, things that feel insignificant at the time.

And once things begin to unfold, those differences don’t disappear.

They grow.

That doesn’t make our choices meaningless.

If anything, it makes them more interesting.

Because even the smallest action — something we barely think about — can carry more influence than we realize.

I’m starting to see uncertainty a little differently.

Not as something to eliminate or control.

But as something that’s always in motion, always responding, always evolving from the smallest of starting points.

Maybe the shape of uncertainty isn’t something vast and distant.

Maybe it begins right here —

in a single word,
a small decision,
a step taken when it almost wasn’t,

quietly expanding into everything that follows.

Among thousands of penguins, patterns emerge — but not always in ways we expect.

Lately, I’ve been reflecting on how much travel and new experiences shape perspective.

Every new place seems to teach something different — not just about the world, but about people and ourselves.

But some of the most lasting insights haven’t come from movement at all.

They’ve come from watching.

On a recent trip to the Falkland Islands, standing among thousands of penguins, I found myself less focused on the spectacle and more on the pattern.

They moved with purpose, but not urgency.
They simply belonged, without question.
There was no sense of trying to predict what came next — only an ease in responding to what was.

In other moments — walking through a rainforest in southern Chile, or noticing the stillness of an owl before flight — I’ve felt something similar.

There’s a kind of quiet intelligence in nature.
Not analytical. Not forced. Just… present.

For the past 9 years, I’ve taught economic forecasting.

At its core, forecasting is about trying to understand what comes next — using data, patterns, and models to reduce uncertainty.

But one of the quiet lessons that emerges over time is this:

The more complex the system, the more it resists being neatly predicted.

There’s a kind of paradox in that.

The more we study and model systems — markets, economies, even human behavior — the more we begin to see the limits of prediction.

We don’t eliminate uncertainty.
We learn to live with it more thoughtfully.

One of the challenges in forecasting — especially in complex systems — is not just predicting outcomes, but recognizing how many of them we won’t see coming at all.

A good example comes from 1971, when the Twenty-Sixth Amendment to the United States Constitution lowered the voting age from 21 to 18.

The motivation at the time was largely centered around the Vietnam War. If 18-year-olds were old enough to be drafted and sent to fight, the argument went, they should also have the right to vote.

“Old enough to fight, old enough to vote.”

But that wasn’t the end of the story.

At the same time, access to the birth control pill was still restricted for many young women under 21. In many cases, it required parental consent or marriage. As the age of majority shifted to 18, access became easier for college-age women.

What followed wasn’t part of the original policy discussion.

Over time, women began entering professional schools — law and medicine — in increasing numbers. Greater control over reproductive decisions made it more feasible to stay in school longer, to plan, and to pursue demanding career paths.

The shift didn’t happen all at once, and it wasn’t caused by a single change. But it’s hard not to notice how the trajectory evolved in the decades that followed.

The shift becomes visible over time.

Source: U.S. Department of Education, National Center for Education Statistics (NCES); American Bar Association; Association of American Medical Colleges. Data simplified for illustrative purposes.

The pattern is clear.

What began as a policy rooted in voting rights and military service had ripple effects that reached into education, careers, and the structure of opportunity itself.

Not predicted, but deeply consequential and not easily forecast

In a way, it’s not so different from what we see in nature — small changes in conditions leading to entirely new patterns over time.

Animals don’t forecast in the way we do.
They adapt. They respond. They move with conditions as they unfold.

Watching them, you get the sense that survival isn’t about predicting the future perfectly —

but about being aligned with the present.

And then there are moments that expand the frame even further.

Standing outside on a clear spring night, looking up at the sky, it’s hard not to feel the same thing on a different scale.

The stars move in patterns we can map and predict with remarkable precision.

And yet, the universe itself remains vast, evolving, and only partially understood.

Predictable… and still not.

Across all of these — markets, ecosystems, the night sky — there’s a common thread:

Patterns exist.
But so do limits.

And maybe the goal isn’t to eliminate uncertainty, but to understand how to move within it.

Travel shows us different places.

Nature shows us different ways of being.

And forecasting — at its best — teaches us humility.

Not everything can be predicted.

But a lot can be learned by paying attention.

There’s something about the beginning of spring that feels like a quiet reset.

The air shifts. The light lingers a little longer. What had been still begins to move again.

This year, that sense of renewal has been especially present — coming just after Passover and Easter, both reminders, in their own way, of transition, reflection, and the possibility of starting again.

And yet, as much as spring feels like forward motion, it’s not something we move through alone.

Every step, every decision, exists within a larger system — shaped not just by what we choose, but by how others respond.

It’s something I’ve come to appreciate more through the lens of game theory.

Game theory, at its core, is the study of how decisions interact — how outcomes depend not just on our own choices, but on the choices of others.

In many situations, the best path forward isn’t determined in isolation. It emerges through interaction — through a kind of quiet coordination, whether we recognize it or not.

One of the central ideas in game theory is something called a Nash equilibrium — a situation where no one benefits from changing their strategy on their own, given what others are doing.

It’s not necessarily perfect. It’s not always optimal.

But it’s stable.

And you begin to notice versions of it in places you might not expect.

You see it in small ways.

In a conversation, where listening changes what comes next.
In a shared space, where small adjustments keep things in balance.

Even in the natural world.

Birds feeding in the same area don’t constantly compete for every advantage. Over time, they adjust — spacing out, taking turns, settling into a rhythm that allows all of them to continue.

It’s not perfect cooperation, but it’s not pure competition either.

It’s something in between — a kind of balance that emerges through repeated interaction.

In game theory, this is often described through repeated games, where behavior evolves over time. Cooperation can emerge — not because it’s enforced, but because it works.

Not in every moment, but across many of them.

Spring itself feels like that — less a single event, and more a system coming back into alignment.

And maybe that’s part of what renewal really is.

Not just starting over, but recognizing how we’re connected to what’s unfolding around us.

That our decisions don’t stand alone.

They interact, adapt, and take shape over time — much like the patterns we begin to notice if we pay attention long enough.

It’s a reminder that moving forward isn’t just about choosing a direction.

It’s about understanding the space we’re moving within — and the others moving alongside us.

Lately, I’ve been noticing the same idea in yoga — how balance isn’t something you find by standing still, but something you discover while moving.

Stability, it turns out, isn’t always planned. Sometimes it’s discovered.

Not long ago, I found myself walking along a quiet path in a park near Ushuaia, Argentina — close to the southern tip of the world.

A river moving steadily beside it. Trees shifting slightly in the breeze. Mountains in the distance, unchanged and yet slowly shaped over time.

It wasn’t dramatic. There was no single moment that stood out.

But the longer I walked, the more something familiar began to take shape.

The same patterns I think about in other areas — economics, forecasting, decision-making — were there too.

Movement. Adjustment. Balance.

The river didn’t force its way forward. It adapted to what was in front of it. The path didn’t rush — it simply continued. Even the mountains, which appear permanent, are the result of slow and constant change.

And it reminded me of something I’ve seen in statistics.

In a recent NCAA tournament game, UConn defeated Duke on a last-second shot — a 35-foot three-pointer with less than a second remaining. In that moment, it felt almost inevitable, as if everything had been building toward that outcome.

But if you step back, it was just one shot. One moment.

The kind that, more often than we think, goes the other way.

In basketball, a player might make several shots in a row and appear to be “hot.” It feels like something has changed — that the next shot is more likely to go in.

But often, each shot is largely independent. What looks like a pattern in the moment can simply be a small sample — too little to tell us very much.

One data point can look like noise. Even a short streak can be misleading.

It’s only over time — across many observations — that a clearer pattern begins to emerge.

We often react to what’s right in front of us, as if it defines the whole story.

But most of the time, it’s just one point along a much longer path.

It made me think about how often we look for clarity in big moments, when most of it is already present in quieter ones.

We don’t always need new information. Sometimes we just need to see what’s already there.

And once you start noticing those patterns, it becomes a little easier to trust the process —

not because everything is predictable,

but because it isn’t unfamiliar.

Lately, I’ve been noticing how often the same patterns show up in different places.

In economics, we talk about systems searching for balance — markets adjusting, correcting, moving toward equilibrium, even if only for a moment.

In nature, the rhythm is just as clear. Seasons turn. Tides shift. Growth gives way to rest, and then begins again.

Even in the broader universe, there’s a quiet consistency — motion, energy, cycles unfolding over time in ways both predictable and mysterious.

And somewhere within all of that, we’re doing the same.

Adjusting. Learning. Finding our footing.

Not always in control of the outcome, but still part of the process.

The more I pay attention, the more I realize that growth isn’t really about predicting what comes next.

It’s about learning how to respond when it does.

And maybe there’s something grounding in that —

the idea that uncertainty isn’t something to eliminate,
but something to move with.

— Steven Baru