game theory

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.

In 1970, mathematician John Conway invented the Game of Life — a cellular field where just three rules generate surprisingly complex patterns. Forty years later, SmoothLife appeared: a version where space is no longer cellular but smooth, like a blurred patch. The authors of the new study took an even simpler variant of this continuous game: now everything changes smoothly in time as well.

A web where lions eat zebras and hyenas finish the leftovers is not just a list of dinners. It's a survival map for an entire region. When the climate shifts, animals move, and the map tears. To understand where it will tear and where it will hold, you need to find its unchanging 'skeleton'—the connections that repeat across different places.

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?

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.

Two gardeners haggle over water from a magical well. One proposes how many buckets to take, the other can agree or refuse. If the deal goes through, the water decreases; if not, the well gradually refills. This haggling repeats hundreds of times, and the gardeners' habits sometimes change randomly, as if they're trying new tactics.

For a long time, biologists argued: if a herd of animals stably maintains a mix of aggressive and peaceful individuals, does each one alternately change behavior, or do some always fight while others always yield? In classical equations, these options are indistinguishable.

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.

The modern standard approach to AI training is like a child locked in a library: he remembers millions of books but doesn't know that fire burns and falling hurts.

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.

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 cloud service dishes out commands to a drone swarm without any knowledge of their movements. The signals are encrypted, but each maneuver leaves a barely visible trace in the cipher, like ink bleeding through paper from the pressure of a pen. Over time, these ink stains coalesce into a pattern, and the hidden data is no longer a secret.

Ecosystems are not chaos but a complex dance where every species plays its part. Scientists modeled such a dance: two prey compete for carbon and water (the building blocks of cells), while a predator eats both. Sixteen scenarios emerged — from predator extinction to a steady rhythm for all three species.

A wolf pack switches from chasing prey to defending its den in an instant. Engineers have instilled this same versatility into an algorithm for a robot swarm. The key is a method proposed by John von Neumann: instead of chasing the target, the robots calculate its future moves and block escape routes, like beaters cutting off trails.

An experienced detective doesn't calculate a criminal's path using the laws of physics: he checks a file of similar cases. The new prediction method works the same way: instead of complex equations, it has a vast archive of trajectories that have already happened — planetary motions, currency exchange rates, signals from space.

A hundred agents, two thousand tasks, less than a hundred iterations, and the market for unloved chores settled into equilibrium all on its own. No one was directing things from the center; each participant only responded to the nearest signal. That’s how multiplicative tatonnement works — a mechanism like the synchronized dance of a flock without a leader.

The ballroom fills with guests. Every new arrival is a request, and the dance steward's job is to find a suitable pair. Two people can be matched as soon as they're close, yielding an accurate pair, but what if a perfect partner arrives a minute later? Hesitate, and the waiters grow weary: some get bored, others lose patience.

Eight hundred participants, firms and workers, enter the market. Each holds a strict preference list in mind. The challenge: pair up so that no one wants a do-over. In mathematics, this is called a game theory with a ban on repeat alliances, and its roots trace back to the work of John von Neumann and Oskar Morgenstern.

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.

In 1953, mathematician Alexander Grothendieck proved a theorem that became a cornerstone in the theory of tensor products. It states that for ordinary function spaces, two natural norms, the projective and the injective, are equivalent up to a universal constant. In other words, two ways of measuring the 'size' of combinations of vectors give similar results.

A crowd at a metro station during rush hour. Some hurry toward the escalator, others slow down at the departure board, still others weave around a column. Each step is not merely a reaction to what's right in front of your nose. A person estimates in advance where a neighbor will be a second from now and adjusts their route accordingly. Physicists call this forward-looking behavior anticipatory.

Understanding the photoelectric effect — where light knocks electrons out of a material — is like learning to cook without a recipe. That’s where digital tools help: games provide intuition, and simulations provide precision.

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.

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.

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.

After a game of Scrabble, the board looks like a mosaic: tiles are scattered but form a connected pattern — islands of words in a sea of empty cells. Such a pattern can be described mathematically, but usually that requires complex rules.

A lab assistant carefully follows every step of an experiment, but at the end, writes in the report what they wanted to write before they even started. That's how modern language models behave when given scientific research tasks.

Engineers improving neural networks act like breeders: they try a variant, check the result, and keep the best. They collected 935 such trials from 161 papers and measured how often a code tweak turns out beneficial, harmful, or neutral. The picture is familiar to any biologist: 68% of changes degrade performance, 19% do nothing, and only 13% yield a gain.

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.

A Network That Decides When to Stop A crowd grows: each new person joins the group with a probability that depends on how large the group already is. If the group is small, joining is easy; if it is huge, people start to hesitate, and growth slows down. This mechanism, called feedback, changes the entire behavior of the network.

In ping-pong, two paddles connected by an invisible thread feel each other's movements, guessing the ball's trajectory. Researchers gave a computer a similar thread — quantum entanglement. Into an ordinary trial-and-error learning program, they embedded a quantum circuit of eight qubits — particles that can spin like a coin on its edge.

Imagine two particles that behave as if they share a telepathic link: measuring one instantly determines the state of the other — at any distance.

Neurons in the brain and nodes in artificial neural networks don't just fire with simple yes/no signals; they also handle smoothly varying quantities, like where an object is and how big it is. But neural responses are finicky: the same stimulus triggers slightly different bursts of activity each time.

At night, the brain doesn't switch off. Instead, it sorts through the day's events like a librarian arranging books on shelves. But where do those strange, sometimes nonsensical dream images come from? Many have assumed they're just random noise with no meaning. A new computer model shows that randomness doesn't hinder: it helps.

When moving to a new apartment, a person doesn't relearn how to open doors or turn on lights. Experience from the old home transfers automatically: a kitchen is a place with a stove and a sink, a bedroom is a place with a bed. The brain doesn't store an exact map but a flexible schema that it overlays onto unfamiliar space. Scientists have built a computer model of this transfer. It has two levels.