Right now, your brain is doing something specific. Billions of tiny cells are firing in a particular pattern. A split second from now, that pattern will change. And here is the surprising part: which pattern comes next may depend only on the pattern you are in right now — not on anything that happened before.

This idea comes from a branch of mathematics called Markov chains, named after the Russian mathematician Andrey Markov, who described them in 1906. They have become one of the most useful tools in brain science.

But the idea raises an uncomfortable question: if only the present moment matters, then what is the point of memory? Why do we remember anything at all?

The answer turns out to be both surprising and deeply practical. It connects brain science to some of the biggest questions in philosophy — and it offers a concrete, usable framework for changing your life through small, everyday actions.

What Is a Markov Chain? (As Simple as Possible)

Imagine you live in a small country with only three towns: Calm Town, Alert Town, and Restless Town. Every morning you wake up in one of these towns. Where you wake up tomorrow depends only on where you are today — not on where you were last week.

If you are in Calm Town today, there is a 70% chance you will still be in Calm Town tomorrow, a 20% chance you will be in Alert Town, and a 10% chance you will be in Restless Town. These chances can be written as a simple table:

This table is called a transition matrix. It is the engine of the whole system.

Notice something about the diagonal — those are the “staying put” numbers. Calm Town is very stable (70% chance of staying). Alert Town is fairly stable (65%). Restless Town is shaky (only 40% chance of staying — you are likely to move somewhere else soon).

The one rule that makes this a Markov chain is simple: where you go next depends only on where you are now. Your entire travel history — where you were last month, last year, ten years ago — does not matter. Only today matters.

This rule is called the Markov property. In technical language, the system is “memoryless.”

Where Do Markov Chains Show Up in the Brain?

Everywhere. From the smallest parts of a single brain cell to the behaviour of the whole brain.

The Tiniest Level: On-Off Switches in Your Neurons

Every neuron has thousands of tiny gates called ion channels. Think of them as microscopic doors. Each door can be Open or Closed. It flips between these two states randomly. The chance of flipping depends only on whether the door is currently open or closed — not on how long it has been in that position.

This is the simplest possible Markov chain: two states, two probabilities. Billions of these tiny doors, each flipping independently, produce the electrical signals that carry your every thought.

The Connection Level: How Neurons Learn

Neurons talk to each other across tiny gaps called synapses. These connections can get stronger (when you practise something) or weaker (when you stop). In extreme cases, connections get completely eliminated — the brain literally prunes wiring it does not need.

Scientists have shown that the way these connections change over time follows Markov rules. The future strength of a connection depends on its current strength and what the neurons are doing right now — not on the full history of that connection.

This is what learning looks like at the physical level. When you practise the guitar, you are strengthening certain synaptic connections. When you stop practising, those connections weaken. The brain is constantly rewriting its own wiring — and that rewriting follows the Markov property.

The Whole-Brain Level: Your Brain Has “Modes”

This is the most exciting part. Using brain scanners (fMRI and MEG), scientists have discovered that your entire brain switches between a small number of distinct “modes” or states. Each mode is a specific pattern of which brain regions are talking to each other. These modes switch every few seconds, and the switching follows Markov rules.

The mathematical tool used here is called a Hidden Markov Model (HMM). “Hidden” because you cannot see the brain’s mode directly — you can only figure it out from the signals the scanner picks up.

Here is what researchers have found:

Healthy brains switch smoothly between several modes. Think of a person who moves comfortably between being focused, being relaxed, daydreaming, and being alert — flowing naturally from one to the next.

Brains affected by PTSD get stuck. Scientists found that the PTSD brain develops what is called an absorbing state — a mode it cannot easily leave. The brain locks into one pattern and cannot switch out of it. This matches exactly what PTSD feels like from the inside: being trapped in a loop of anxiety and hypervigilance, unable to relax or shift your attention.

Brains of exceptionally gifted students show yet another pattern. During complex problem-solving, gifted teenagers can hold a productive brain mode for longer than average. Their “staying put” probability for the focused mode is higher. They also switch into the focused mode more easily from other modes.

These are not metaphors. These are actual numbers computed from real brain scans.

The Prediction Level: Your Brain Guesses What Comes Next

Your brain is always trying to predict what will happen next. When you hear a familiar song, your brain predicts the next note before it plays. When the prediction is wrong — say, a strange note plays instead — your brain produces a little jolt of surprise, detectable on an EEG machine.

A 2023 study showed that the brain’s prediction system works like a Markov transition matrix. The brain has learned: “After note A, note B usually follows with 80% probability.” When note C plays instead, the brain registers a prediction error. The brain, it turns out, has built its own internal Markov chain as a model of the world.

If Only the Present Matters, Why Do We Have Memory?

This is the question at the heart of the whole essay. If the Markov property says the future depends only on the present, does that mean our memories are useless?

No. It means the opposite. Here is why.

Your Memory Has Already Become Your Present

Think about two people sitting side by side on a park bench. Same bench, same weather, same moment. But one person spent twenty years as a soldier, and the other spent twenty years as a monk. Are they in the same “state”? Obviously not. Their brains have been physically shaped by completely different experiences. Their synaptic connections, their neural pathways, their transition probabilities — all different.

The River Analogy

A river’s flow at any point depends on the shape of the riverbed right here, right now. The river does not “remember” the rainstorms of ten years ago. But the riverbed is the result of those rainstorms. Every flood, every drought, every season of erosion carved the contours that now guide the water.

Your brain is the riverbed. Your experiences are the floods and droughts. The Markov property is the flow of water. The river does not need to remember because the memory is the riverbed itself.

What Memory Actually Does

It rewrites the probability table. Learning something new changes the chances of your future state transitions. A child who touches a hot stove rewires the probability of “reach toward stove” from high to very low.

It creates entirely new states. A person who learns to read has access to brain states that an illiterate person simply does not have. Experience does not just shuffle the probabilities — it expands the menu of possibilities.

It makes good states last longer. Practice increases the “staying put” probability of useful states. A trained meditator can sustain a calm, focused state much longer than a beginner.

It can break you out of traps. Therapy for PTSD works, in Markov terms, by reducing the “staying put” probability of the stuck state. The goal is not to erase the trauma but to make the brain flexible again.

Existentialism and the Markov Brain: You Are Not Your Past

The Markov idea — “only the present determines the future” — is not just a mathematical trick. It is the central message of existential philosophy, the tradition that includes Sartre, Camus, Heidegger, and Kierkegaard.

Sartre: You Are Free Right Now

Jean-Paul Sartre’s most famous idea is that existence precedes essence. In plain language: you are not defined by your history, your genes, your upbringing, or your resume. You are defined by what you do right now and what you choose next.

Look at the Markov chain. At every state, the system faces a spread of possible next states. It is not locked into one path. The transition matrix gives tendencies, not certainties. There is always some probability, even if small, of an unexpected jump — a sudden shift from Restless to Calm, from stuck to free.

Sartre called this radical freedom. The mathematics agrees: at every time step, multiple futures are possible.

Sartre’s “Bad Faith”: Pretending You Cannot Change

Sartre had a name for the habit of denying your own freedom: bad faith. Bad faith is telling yourself, “I am an angry person — that is just who I am.” Or: “I have always been disorganised — I cannot change.”

In Markov language, bad faith is the mistake of treating your current transition matrix as permanent. It is confusing a tendency with an identity. Neuroplasticity — the brain’s ability to rewire itself — means the matrix is always being rewritten by experience.

You are not your transition matrix. You are the process that can rewrite it.

Heidegger: Where You Start Is Not Where You End Up

Heidegger talked about thrownness — the fact that you find yourself in a situation you did not choose. You did not pick your family, your country, your body.

In a Markov chain, this is the starting state — X₀. You did not choose it. But here is the mathematical fact: as the chain runs, the starting state matters less and less. Over time, the system settles into its stationary distribution — the long-run pattern determined by the transition matrix, not by the starting point.

Translation: where you begin matters less than how you transition. The structure of your choices, over time, overwrites the accident of your origin.

Kierkegaard: The Leap That Changes Everything

Kierkegaard wrote about the leap of faith — a moment when someone jumps from one way of living to a fundamentally different one. Not through gradual steps, but through a single decisive act.

In Markov terms, this is a low-probability transition. Maybe there is only a 2% chance of jumping from “going through the motions” to “fully committed.” But 2% is not zero. The leap is always on the table.

And here is the crucial insight: once you make the leap, you face a different transition matrix entirely. New states become possible. Old traps lose their grip. The leap does not just move you to a new place — it changes the entire landscape of what comes next.

Camus: Finding Meaning in the Loop

Camus imagined Sisyphus, condemned to push a boulder up a hill forever, watching it roll back down every time. This looks like the ultimate absorbing state — an endless, inescapable loop.

But Camus said: “One must imagine Sisyphus happy.”

How? By changing the inner experience of the state without changing the state itself. The boulder still rolls down. But the person pushing it has found meaning in the act. The Markov chain sees the same loop. The human inside it has transformed.

This reveals something the mathematics alone cannot: the felt quality of a state is not captured by the transition matrix. Two people can occupy the same mathematical state and experience it completely differently. The numbers describe the dynamics. The philosophy describes what it is like to live them.

How Small Changes Reshape Your Future: A Practical Guide

Here is where the mathematics becomes genuinely useful for everyday life. The core insight is this: you do not need to make dramatic changes to dramatically change your life. Small shifts in probability, repeated consistently, change everything.

How Small Probabilities Compound

Imagine your daily life has three main states: Energised, Neutral, and Drained. Your current transition matrix looks like this:

Run this chain over a long time and you end up spending roughly 22% of days Energised, 36% Neutral, and 42% Drained. That is a life where you feel drained almost half the time.

Now suppose you make one small change — you start going to bed 30 minutes earlier. This just shifts a few probabilities by five percentage points:

You have gone from being drained 42% of the time to 33% of the time — gaining roughly one extra good day every two weeks — from a single habit change. Stack two or three small changes and the compound effect is striking.

This is the central practical lesson: you do not change your life by willpower on any single day. You change it by nudging probabilities, which changes where you spend your time over months and years.

Seven Principles for Rewriting Your Transition Matrix

Where the Traditions Meet

The Markov brain sits at a place where mathematics, brain science, philosophy, and ancient wisdom all point the same way.

The Bhagavad Gita teaches Nishkama Karma — act fully in the present, without clinging to past results or grasping at future outcomes. The Markov property says the same: the best action depends only on your current state.

Sartre insists that you are free at every moment, defined not by your past but by what you choose now. The transition matrix agrees: the past has been absorbed; only the current spread of possibilities matters.

Nietzsche’s amor fati — loving your fate, embracing the present moment including everything that led to it — mirrors the Markov chain’s relationship with its own history: not resisted, not replayed, but fully absorbed into the structure of now.

The Yoga Sutras describe pratyahara — turning inward, away from the noise of accumulated impressions — as the gateway to deep concentration. In Markov terms, this is the practice of raising the “staying put” probability of a focused state by reducing the distractions that trigger transitions away from it.

And Friston’s Free Energy Principle — perhaps the boldest theory in modern brain science — says the brain is a system that constantly updates its internal Markov models to better predict the world. Learning, in this view, is the lifelong rewriting of transition matrices through lived experience.

You are not your history. You are not your current state. You are the living process that rewrites the matrix — one small probability at a time.

Summary