decision making

Collective behavior of AI agents has emerged not in a laboratory but in the wild: on a randomly discovered wiki site where agents wrote test hints. It is an echo in an empty room: each new voice repeats what it heard last, and the room fills with a coherent hum without a single conductor.

Two people agree to meet, but each has their own things to do. One hesitates: what if the other doesn't come? And decides not to go. The other thinks the same. The meeting doesn't happen. That's the simple mechanism the authors dissect: a joint endeavor requires everyone to invest time, but the payoff depends on whether the partner participates. In the model, each person keeps a to-do list ordered by importance.

Two people agree to meet, but each has their own things to do. One hesitates: what if the other doesn't come? And decides not to go. The other thinks the same. The meeting doesn't happen. That's the simple mechanism the authors dissect: a joint endeavor requires everyone to invest time, but the payoff depends on whether the partner participates. In the model, each person keeps a to-do list ordered by importance.

A person flips a coin to make a decision. They stand still — the coin lands heads. They take a few steps, and flip again. It would seem walking shouldn't affect the coin. But what if the walk itself changes how often they decide to simply repeat the previous toss? That's exactly what researchers tested.

A person flips a coin to make a decision. They stand still — the coin lands heads. They take a few steps, and flip again. It would seem walking shouldn't affect the coin. But what if the walk itself changes how often they decide to simply repeat the previous toss? That's exactly what researchers tested.

In a warehouse, there are boxes of apples and pears. There are many apples and few pears. But when the stock clerk says "a fruit with smooth skin," we are somehow more likely to recall the rare pear than the common apple. This is the inverse base-rate effect — a persistent glitch in how the brain resolves ambiguity between categories.

A cookbook without a shopping list: that's what a scientist faces when looking at a scheme of chemical reactions. The arrows and formulas on paper look neat, but once you start hunting in the lab for molecules that will actually behave that way, a real quest begins.

A neural network predicts the future of a system — say, how plasma will swirl in a magnetic field or how a face in a video will change. Errors are inevitable, and the further it looks ahead, the more they accumulate. Checking against the correct answer during operation is impossible: it simply doesn't exist.

The human mind is like a construction set that builds itself. New ideas don't die with their creators: they are passed on, mixed, and selected. This process is called cultural evolution: useful inventions stick, useless ones are forgotten. That's how a ratchet works — a mechanism that only turns forward. But what exactly happens in the head when one thought displaces another?

In combat, a pilot needs to instantly understand what an object is and what its intentions are. Conventional systems wait for complete radar data — like a goalkeeper who only reacts after the shot. The new algorithm works like an experienced goalkeeper: from the attacker's first moves, it predicts the target's maneuver.

The meditating brain is an orchestra, and attention is the conductor. When you're focused on your breath, all instruments play in harmony. But suddenly, a thought-cello plays a false note: that's mind wandering. An experienced conductor doesn't panic: they instantly catch the dissonance and gently guide the orchestra back to the main melody — the breath.

The robot juggler catches and throws balls in precise rhythm with a human, like two пульсара sending pulses in unison. Juggling three balls is a classic cascade, governed by a steady beat, like the uniform rotation of a cosmic lighthouse. Break the rhythm and the pattern crumbles.

Ordinary models assume that infection is like a steady flow of water that washes over everyone equally. In reality, the stream from a sick person fluctuates: right after infection it barely trickles, then turns into a raging downpour, and later subsides. The chance of catching the virus depends on the strength of this flow at the moment of contact.

In any city project, residents' behavior is a invisible force that determines success. One skeptical neighbor can slow down solar panel installation across an entire block: their caution triggers a rise in disorder.

Human hearing is wired so that the brain prioritizes familiar patterns — a friend's voice, the patter of rain, a car horn. But a new system works on a different principle: it registers any sound burst, even one never encountered before.

Traditionally, machine learning algorithms operate in a static environment, like water flowing along a pre-cut channel without changing its shape. The new method flips this paradigm: it lets an agent simultaneously master actions and deliberately modify the very environment it inhabits.

A snowflake grows, an oil slick spreads, a crowd moves — each case has its own equations. But the end result is often the same: shape, speed, and pattern follow a simple scheme that repeats across scales. This is universality — when a common law hides behind the details.

Imagine an old theater in the damp fog. On the stage, a scattering of actors, but only one is caught by the spotlight beam, made to freeze in the bright patch. The rest billow in the dim light, ready to dissolve at any moment.

Gravitational signals from dying massive stars are whispers reaching us from the most violent laboratories in the Universe. Einstein predicted these gravitational waves over a century ago, but only today, with detectors like LIGO, are we beginning to hear them.

Molecules float in a pot of broth. Some copy themselves, others help their neighbors, and still others just get in the way. This isn't soup: it's a model of early life: a replicator system. Usually, we assume the rules of the game are fixed once and for all: whoever reproduces fastest wins. But the authors of this study proposed something different — the rules can change too, just very slowly.

60 mice, 1,980 samples, 234 lung cancer patients. Behind each number lie thousands of genes. Their activity is measured to understand the disease. But the data aren’t uniform: for one patient, you assess a protein level (a continuous number), make a diagnosis (yes/no), and determine the tumor subtype (a category).

A closed loop: the machine proposes an idea, tests it on people, sees the failures, and spawns a new, more accurate guess. That's how AutoCog works — an automatic cognitive scientist. Previously, in the science of thinking (cognitive science), the hardest part, coming up with a theory, was done only by humans.

We're used to thinking that noticing only confirmations of our guesses is a mistake. But recent calculations, inspired by wave behavior, flip that view. It turns out that the habit of seeking the familiar is the best way to reduce information noise (scientists call it entropy).

A person reaches out to a stranger across town. That stranger will never return the favor: they'll never meet. So why help? Biologists call this indirect reciprocity: kindness comes back not from the person you helped, but from someone else, down the line. In a new study, two variants of this idea were pitted against each other.

Inside every cell, a complex network of genes determines what it will become — a nerve cell, a muscle cell, or part of the skin. This choice often resembles flipping a toggle switch: two stable positions, and the cell decides which way to tip. But cells have a feature rarely considered in such models: they divide.

Life is a constant dance between order and chaos. Back in the mid-20th century, Erwin Schrödinger noted that living things feed not so much on matter as on "negative entropy"—the ability to maintain their own order by increasing disorder around them.

A supermarket cashier at rush hour doesn't memorize every scanned item: they keep the running total in their head and watch the queue. The brain allocates its resources similarly: the expensive, precise cortex (responsible for deliberation) and the simpler, economical subcortical nuclei (where habits live) work in tandem.

Every morning you pick up the same mug, but the coffee tastes either bitter or just right. It's not about the coffee, and not about your character. Inside us, an invisible panel with dozens of sliders is constantly shifting: fatigue, hunger, anxiety, hormone levels.

A person looks at a door lock and sees a keyhole. They know that a key is inserted and turned. But if instead of a keyhole there's a touch panel, the familiar frame breaks. The person doesn't just make a mistake: they realize that the usual approach won't work and can say, "There's no place for a key here; a different approach is needed." Such an honest stop with an explanation is rare for machines.

In a grand courtroom, a meeting is in session. Hundreds of translator-neurons simultaneously hear fragments of phrases, and must piece them into a single coherent picture. This is exactly how large brain networks turn scattered signals from the eyes into a unified object image.

In a car, the steering, brakes, and accelerator gradually fail. At first, you can still swerve around a pothole, but each year the maneuver takes more effort. At some point, even a perfect road becomes impassable — the car no longer obeys the driver. That's exactly how the authors describe aging: not as the accumulation of rust or wear and tear, but as the loss of "controllability" of the body.

You're trying a dish at a restaurant, but you haven't seen the menu. You ask the waiter questions — too salty? what spices?—and from the answers you try to guess what kind of chef prepared it.

Every time you mull over a risky move, whether to invest in stocks or hold off, an inner dialogue runs through your head. Usually, scientists try to reconstruct that dialogue from the final choice alone, as if a detective were reconstructing a conversation between two people from a photo of the meeting spot.

A thermostat keeps a room at 20 degrees. Open a window in winter, and it kicks on the heat to restore equilibrium. The brain does something similar: it constantly predicts what will happen a moment ahead and corrects itself when wrong. That's how it learns.

An orchestra plays without a conductor. Each musician has a score: that's the genes. But the volume and tempo depend on the acoustics of the hall, the mood, and the neighbors on stage. That's roughly how heredity and daily life together shape a teenager's behavior.

Your hand rests on the table, and two short vibrations arrive at your wrist — one after the other. You need to say which is stronger. The task seems simple, but the answer depends on which vibration came first. If the first is weak and the second is strong, the difference feels larger than when they are swapped.

In a big city after rain, water collects in the same low spots no matter where the first drops fall. An hour later, the pattern of puddles hardly depends on where the downpour started. That is equilibrium — the outcome a system reaches regardless of its start, once the details are forgotten.

An amoeba turns left. For a long time, this was considered the result of a rigid chain of causes, like dominoes. But at the core of matter, there's a gap for freedom. The 'Agent Choice via Quantum Flux' model shows: each decision corresponds not to one, but to many quantum histories.

An epidemic is not just a virus: it's millions of personal decisions. One person keeps riding the subway; another locks themselves at home. Classical models describe the average behavior of a crowd but don't ask why each individual acts that way.

Two accomplices are interrogated separately: betray or stay silent? If neither knows what the other will do, their choice breaks the classical rule of probability. It’s like riding an old bicycle over bumps — the hard seat jolts you off. The textbook rule, the classical model, fails. The authors of a new study found a simple fix: the cyclist needs a soft saddle.