A single starling adjusts its flight based on the few birds nearest to it, while a lone driver on a highway maintains their speed and lane. In isolation, these are simple actions. When thousands of starlings flock together, they form a murmuration, a fluid shape that moves as one. When thousands of cars occupy the same road, a traffic jam can materialize from seemingly nowhere. These large-scale patterns are emergent properties.
An emergent property is a characteristic that arises in a complex system from the collective interactions of its components. These properties are not present in the parts in isolation and cannot be predicted by studying them alone. The behavior of a starling flock or the standstill of a traffic jam belongs to the group, not the individual, as it is the connections between the parts that generate new behaviors.
The “More Is Different” Principle
In 1972, physicist P.W. Anderson published a paper titled “More is Different,” which argued against a purely reductionist view of science. Reductionism is the idea that a complex system can be understood by breaking it down and studying its most basic parts. For instance, a reductionist approach to an organism would study its organs, then tissues, then cells, and finally the molecules within them. Anderson contended that this approach is often insufficient.
His principle suggests that as you increase the quantity and complexity of a system, you can reach a point where entirely new properties and laws emerge. These new characteristics are not just more of the same, but qualitatively different. The ability to reduce everything to simple fundamental laws does not mean one can start from those laws and reconstruct the universe. The behavior of a large, complex group of particles is not understood by a simple extrapolation of the properties of a few.
This concept is summarized by the phrase “the whole is greater than the sum of its parts.” In this context, it means the interactions between the components create something fundamentally new. The hierarchy of science is not a simple linear chain, as new and surprising behaviors emerge at each level of complexity.
Examples Across the Sciences
The concept of emergence is visible across a range of scientific disciplines. In chemistry, water (H₂O) is a prime example. Its constituent parts, hydrogen and oxygen, are flammable gases at room temperature. When two hydrogen atoms bond with one oxygen atom, the resulting water molecule has a new arrangement of electrons. This configuration gives rise to properties like polarity, where the molecule has a slight positive charge on the hydrogen side and a slight negative charge on the oxygen side.
This polarity allows water molecules to form hydrogen bonds with each other. This leads to emergent properties such as surface tension, a high heat capacity, and “wetness.” None of these properties can be found in individual atoms of hydrogen or oxygen.
Biology provides some of the most striking examples. An individual ant operates on a limited set of simple behaviors, guided by chemical signals called pheromones, and has no commander guiding its actions. The interactions of thousands of ants, each following simple rules based on local information, give rise to sophisticated colony-level intelligence. They can build complex nests, efficiently forage for food by reinforcing the shortest paths with pheromone trails, and defend territory—abilities no single ant possesses. The colony functions as a “superorganism,” an entity with capabilities that emerge from the collective.
In physics, properties like temperature and pressure are emergent. A single atom or molecule does not have a temperature. Temperature is a measure of the average kinetic energy—the motion—of a vast number of molecules in a substance. Similarly, pressure is the result of countless molecules colliding with the walls of a container. These are macroscopic properties that only have meaning when dealing with a large collection of particles.
Neuroscience offers another example: consciousness. The brain is composed of billions of individual nerve cells called neurons. A single neuron is a relatively simple biological cell that fires electrical signals. The intricate network formed by these neurons, with their trillions of connections, gives rise to the emergent phenomena of thoughts, feelings, and self-awareness. Modern neurobiological theories propose that consciousness is the result of large-scale interactions between different neuronal networks across the brain.
The Rules of Emergence
Emergent behaviors do not arise randomly; they are governed by a set of underlying conditions. One of the most fundamental rules is that the system is composed of individual components that follow simple rules based only on local information. A bird in a flock adjusts its flight based on its immediate neighbors, not the entire flock. An ant follows a chemical trail it directly senses. This reliance on local interactions is a recurring theme.
A characteristic of these systems is the absence of central control. There is no leader, blueprint, or external director guiding the overall pattern. The global order, whether it’s the intricate structure of a termite mound or the synchronized flashing of fireflies, emerges spontaneously from the bottom up. This self-organization is a hallmark of emergence.
These systems also involve feedback loops, where the actions of agents modify the environment, which in turn influences the future behavior of other agents. When a few cars slow down on a highway, they cause the cars behind them to brake, reinforcing the initial slowdown into a traffic jam. When an ant finds a food source, it lays down a pheromone trail, which attracts more ants, who then reinforce the trail, creating a positive feedback loop that efficiently exploits the resource.
Implications for Science and Technology
The study of emergence has implications for how we approach scientific problems and develop new technologies. It encourages a shift from purely reductionist methods to “systems thinking,” which focuses on the relationships and interactions within a complex system. This approach is applied in fields like ecology, where the health of an ecosystem depends on the intricate web of interactions between countless species and environmental factors. It is also used in economics to understand market behavior that emerges from the interactions of millions of individual buyers and sellers.
In technology, the principles of emergence are directly applied in artificial intelligence, particularly in swarm intelligence and robotics. Instead of designing a single, highly complex robot, engineers can design a “swarm” of many simple robots. Each robot follows basic rules and communicates only with its neighbors, but together they can perform complex tasks like mapping an unknown area or cleaning up oil spills without a central commander. This decentralized approach is often more robust and adaptable.
The lens of emergence is also changing medicine. Viewing the body as a complex system helps researchers understand diseases that are not caused by a single agent but arise from the interplay of numerous factors, including genetics, environment, and lifestyle. This “systems biology” approach recognizes that health and illness can be emergent properties of the body’s vast network of interactions. Understanding these dynamics can lead to new ways of diagnosing and treating diseases that result from the system’s overall behavior.