Complex system

A complex system is something made of many parts. These parts are called elements or agents. They work together in ways that make the whole system act differently from each part by itself. This is called an emergent property. For example, the human brain, nature, the weather, traffic, and the internet are all complex systems. In a complex system, small changes can cause big effects. Big changes can sometimes cause only small effects. This makes it hard to know what will happen. Complex systems are often unpredictable.

Many complex systems have feedback loops. In a feedback loop, the result of something affects what happens next. Positive feedback makes changes grow stronger, like rumors spreading quickly. Negative feedback makes changes smaller, like predators and prey keeping each other’s numbers in balance. Complex systems can adapt, which means they can change to fit new situations. In nature, this can happen through evolution. In human systems, people can change their plans when they get new information.

Complex systems can also self-organize. This means patterns or structures can form without a leader. Examples include birds flying in a flock, ants working together, or traffic jams forming by themselves. Scientists study complex systems with tools from math, science, and computers. They use networks to show how parts are connected. They also use simulations to test ideas. Studying complex systems helps people make better choices about the climate, health, money, and many other things.

History
The study of complex systems has a long history in many subjects. It started with early ideas about how everything is connected and has grown into a modern science that uses advanced math and computers. In the 1700s and 1800s, thinkers like Adam Smith in economics and Charles Darwin in biology saw that big patterns could appear from the actions of many individuals, without anyone controlling them. Adam Smith’s idea of the “invisible hand” showed how markets could organize themselves through people acting in their own interest. Charles Darwin’s theory of evolution explained how many different species and complex ecosystems could form over time without a designer.

In the early 1900s, new areas of science such as thermodynamics and statistical mechanics gave formal ways to study systems with many interacting parts, like gases. Scientists like Ludwig Boltzmann and Josiah Willard Gibbs created methods to explain how large-scale properties such as temperature and pressure come from tiny particle movements. Around the same time, ecology grew as a science. Researchers like Charles Elton used network-like diagrams to study predator–prey relationships and the movement of nutrients. In the 1940s, Norbert Wiener developed cybernetics, which studied feedback, control, and communication in both machines and living things.

In the mid-1900s, Ludwig von Bertalanffy created systems theory, which said that complex patterns appear in many different fields, from biology to engineering. Early computers allowed scientists to model large systems by simulating many parts at once. In the 1960s, meteorologist Edward Lorenz found deterministic chaos while studying the weather. He showed that very small changes at the start could lead to very different results. This meant that even simple systems could be unpredictable.

By the late 1900s, complexity science had become its own field. The Santa Fe Institute, founded in 1984, brought together experts from physics, biology, economics, and computer science to study self-organization, adaptation, and emergence. New tools such as agent-based models, cellular automata (first made by John von Neumann and later popularized by John Conway’s Game of Life), and network analysis helped study problems like traffic flow, disease spread, and social behavior. Better computers and more data made it possible to study complex systems in greater detail.

In the 2000s and beyond, complex systems research has expanded into climatology, genetics, artificial intelligence, and global economics. Modern network science, led by researchers like Albert-László Barabási and Duncan Watts, has found that many real-world networks have scale-free or small-world structures, no matter the subject. Over time, the study of complex systems has shown two main lessons: we can understand and model these systems better than before, but complete prediction is often impossible.

Characteristics
A complex system has special features that make it different from simpler systems. These features work together to create unique and often hard-to-predict behavior.

 One important feature is that a complex system is made of many connected parts, called agents, nodes, or elements. These parts can be physical objects, living things, or even ideas like choices or pieces of data. The parts can interact directly, like molecules in a gas hitting each other, or indirectly, like changes in market prices affecting how traders act. Because there are so many connections and ways for parts to interact, the whole system cannot be understood by looking at only one part.
 Another feature is emergence. This means the system shows patterns or abilities that the parts alone do not have. For example, single brain cells (neurons) do not have consciousness, but the network of many neurons working together can create it. In the same way, no single ant knows the whole layout of its environment, but an ant colony can find the shortest path to food. Emergence means the whole system can act in new ways that are different from just adding up the actions of each part. This often makes prediction difficult.
 Complex systems are also nonlinear. In a nonlinear system, changes in one part do not always cause effects of the same size. A small change might cause a very big effect, as in the “butterfly effect,” where tiny changes in the weather can create very different long-term outcomes. A big change might have almost no effect if other parts of the system balance it out. Because of nonlinearity, simple cause-and-effect thinking often does not work.
 Feedback loops are another important part of complex systems. In a positive feedback loop, a change becomes stronger over time. For example, melting ice makes the Earth less reflective, which makes it warmer, which melts more ice. In a negative feedback loop, change is reduced, making the system more stable. For example, the human body cools down by sweating or warms up by shivering. Complex systems often have many feedback loops at the same time, and these loops can interact in ways that cause stability, cycles, or rapid growth.
 Many complex systems are adaptive, which means they can change their behavior when the environment changes. This can include learning, like in artificial intelligence programs that improve with training, or evolution, like species in nature developing new traits to survive. Some adaptive systems also self-organize, meaning they create order without a leader or central control. Examples include fireflies flashing together or traffic jams forming without a traffic light.
 Complex systems often have network structures that show how parts are connected. Networks can be centralized, decentralized, or spread out. The structure affects how strong or weak the system is. For example, scale-free networks have many nodes with few connections and a few nodes with many connections. This makes them strong against random problems but weak if the highly connected nodes are damaged.

Types
Complex systems can be grouped into different types, based on what they are made of, how their parts interact, and where they are found. All types share common features such as emergence, nonlinearity, and adaptation, but they are different in size, makeup, and how they work. The main types are natural complex systems, social complex systems, engineered complex systems, and hybrid complex systems.

Natural complex systems are found in nature and are not designed by humans. Examples are ecosystems, climate systems, and the human brain. Ecosystems are made of living things and their environment, where changes in one species can affect many others. Climate systems include the atmosphere, oceans, land, and ice, with feedback effects like the greenhouse effect changing the Earth’s temperature over time. The human brain has about 86 billion neurons linked by trillions of connections, creating patterns of signals that give rise to thinking and consciousness. These systems often work on many scales of time and space, which makes them hard to predict or control.

Social complex systems are made of people, groups, and organizations. They are affected by culture, politics, economics, and psychology. Examples include cities, economies, and social networks. Cities are centers of human activity, where housing, transport, and resources interact to affect things like population density and energy use. Economies are made of countless transactions between buyers and sellers, with prices and production changing according to supply, demand, and laws. Social networks are webs of relationships where ideas and behaviors can spread in unpredictable ways. These systems are often adaptive because people and groups can change their actions when situations change.

Engineered complex systems are human-made and built for specific purposes. They can still show complex behavior because of many parts, many types of interactions, and uncertain environments. Examples are transportation networks, power grids, and the internet. Transportation networks include roads, trains, air travel, and shipping routes, all of which must handle changing demand and unexpected problems. Power grids send electricity from many sources to customers, balancing supply and demand while avoiding large-scale blackouts. The internet is a global network of connected devices that share data through common rules. Engineered systems must be watched and updated to work well and avoid failures.

Hybrid complex systems mix natural, social, and engineered parts. They are often especially complex because different kinds of systems interact. Examples include global supply chains, environmental management systems, and public health systems. Global supply chains connect natural resources, factories, transport, and markets, and can be affected by natural disasters, political changes, or pandemics. Environmental management systems use laws, technology, and community action to protect nature, combining science with politics. Public health systems combine hospitals, social behavior, and biology, as seen when controlling disease outbreaks.

Each type of complex system is studied in different ways. Natural systems often need long-term data and models based on physical laws. Social systems use ideas from sociology and economics to understand human choices. Engineered systems use engineering and control theory to improve reliability. Hybrid systems often need methods from many fields. Studying these types helps scientists, governments, and engineers understand where complexity comes from, predict problems, and make systems stronger.

Structure
The structure of a complex system is the way its parts are arranged, connected, and interact. Every complex system is made of elements, also called nodes, agents, or units, that are linked by relationships or connections. The parts can be physical objects, like computers in a network, or abstract things, like people in a social network or variables in a mathematical model. The arrangement of these parts forms a topology (layout) that controls how information, energy, or resources move through the system. A system’s structure can change over time as new connections appear, old ones disappear, or the parts themselves change.

One important part of structure is hierarchy, where parts are arranged in different levels. In a living thing, for example, molecules form cells, cells form tissues, tissues form organs, and organs work together in organ systems. Each level has its own rules and behaviors, but higher and lower levels can influence each other. Hierarchies can make a system stronger by keeping problems at one level, but they can also cause trouble if a key high-level part fails. Not all systems are hierarchical, some are decentralized, with no single point of control, like ant colonies or blockchain networks.

Another key feature is network topology, which describes how parts are connected. In random networks, connections are made without a clear pattern. Small-world networks have short paths between nodes and high clustering, making information spread quickly. Scale-free networks have a few highly connected hubs and many nodes with few links. The network type affects how strong or weak a system is. Scale-free networks resist random failures but can be damaged if hubs are attacked, while small-world networks balance fast communication with stability.

Many complex systems are modular. This means their parts form groups (modules) with many connections inside the group but fewer connections to other groups. For example, different parts of the brain handle special tasks but still share information through certain pathways. Modularity can make systems more efficient and protect them from problems spreading, but too much modularity can make it harder for parts to work together.

Some systems have multi-layer or multi-scale structures. This means they have different kinds of networks working at the same time. A city’s transportation system has roads for cars, railways for trains, and air routes for planes, all linked by transfer points like airports and train stations. Each layer has its own rules, but they must work together. This allows many functions at once but also means that a failure in one layer can cause problems in others.

Many complex systems have dynamic connectivity, where connections change over time. In social networks, friendships form and end. In ecosystems, predator–prey links change as species move or adapt. Dynamic structures make systems flexible and able to adapt, but also harder to predict because the interaction rules can change.

Mathematical and Theoretical Approaches
Mathematical and theoretical approaches to complex systems are ways to use math and logic to describe, study, and predict systems that have many parts interacting with each other. These methods try to show the main ideas of complexity, such as emergence (new patterns appearing), nonlinearity (small changes making big effects), feedback (effects that loop back to influence causes), and adaptation (changing in response to the environment). Complex systems exist in many areas, like physics, biology, sociology, and engineering. The math used comes from many fields, such as graph theory, probability, statistics, dynamical systems, information theory, and computer modeling. The method chosen depends on the type of parts in the system, how they interact, and what questions researchers want to answer.

Network theory is a common method. It shows a system as a graph with nodes (parts of the system) and edges (connections between parts). Network theory studies things like degree distribution (how many connections each node has), centrality (how important each node is), and clustering coefficient (how likely nodes are to form tight groups). This helps researchers find key nodes, understand how things like diseases or information spread, and see how strong a system is against failures or attacks. Networks can have different shapes, such as random graphs, small-world networks, or scale-free networks. Each shape has special mathematical properties that affect how the system behaves.

Nonlinear dynamics and chaos theory study how a system changes over time when the rules are not simple or proportional. Nonlinear equations can describe these changes. Depending on the situation, the system might settle to a stable point, move in cycles, or behave chaotically. Chaos theory shows that even systems with fixed rules can have unpredictable results, because small differences at the start can grow very fast over time. This idea is used in weather forecasts, biology, and economics, where small uncertainties can make big differences later.

Statistical mechanics is another method, especially for systems with many parts. It started in physics to connect the small-scale behavior of particles with large-scale properties like temperature. It uses probability to describe the likely states of a system without tracking every single part. This method is now used outside physics too, such as in studying social trends or patterns in biology. It helps find patterns that come from the combined actions of many parts.

Information theory, created by Claude Shannon, measures how information is stored, sent, and changed in a system. It uses ideas like entropy (a measure of uncertainty) and mutual information (how much information two parts share). These help researchers measure order, disorder, and relationships in systems such as the brain or communication networks. It is useful for understanding how systems handle signals, adapt to change, and stay strong against random noise.

Agent-based modeling (ABM) is a computer method where each part, called an agent, is given rules for how to act and interact. The system is then run in a simulation to see what patterns appear. Agents can have different features, learning abilities, and decision-making styles. ABM has been used to study crowds, markets, disease spread, and ecosystems. It is useful when math formulas are too simple to capture all the details.

Cellular automata are grids where each cell changes based on simple rules and the states of nearby cells. Even with simple rules, running them many times can create complex patterns. A famous example is John Conway’s Game of Life, which can create moving shapes and self-copying patterns. Cellular automata are used to model things like fluid dynamics, forest fires, and biological growth.

Often, researchers combine several methods to study a system. For example, a disease model might use network theory to show contact patterns, nonlinear dynamics to show infection rates, and agent-based modeling to show how people might change their behavior. Using more than one method helps confirm results, find the most important factors, and design solutions that work even when there is uncertainty. No single method can show everything about a complex system, so researchers choose tools based on the balance between detail, general rules, and how much computer power is needed.

Methods of study
300px|thumb|A 48-hour computer simulation of Typhoon Mawar using the Weather Research and Forecasting model 
Complex systems are hard to study because they do not work in simple, predictable ways. They often have feedback loops, where one change can cause more changes, nonlinear cause-and-effect, where small changes can have big results, and the ability to change and adapt over time. Because of this, no single tool can explain everything about them. One important tool is mathematical modeling. This is when scientists use equations to show how parts of a system affect each other. Some models always give the same result if you start with the same conditions (deterministic), while others include randomness to show uncertainty (stochastic). For example, nonlinear equations can help describe how animal populations change, how fluids move, or how electrical circuits work. Some models use step-by-step changes, like cellular automata, which track how things change in a grid of cells. These models can predict outcomes and test ideas, but they often need to be simplified so they can be solved.

Another useful method is computer simulation. This is especially important when systems are too complicated for exact math solutions. Simulations make virtual versions of systems so scientists can see how they react to different situations. In agent-based modeling, many “agents” (such as people, cars, animals, or molecules) each follow simple rules, but together they create complex patterns. This helps in areas like traffic planning, tracking disease outbreaks, or studying ecosystems. Another type, network modeling, shows systems as webs of connected points, making it easier to study how things are linked and how they flow.

Observation and measurement are also key to understanding complex systems. In nature, scientists might watch ecosystems for years, use satellites to track weather, or record brain activity. In social or technological systems, they might gather data from surveys, financial reports, or city sensors. One challenge is that these systems often work on many different scales of space and time, so scientists need data from multiple sources. New tools like remote sensing and real-time monitoring make collecting this data much easier.

In recent years, data analysis has become an important way to study complexity. Large datasets can reveal patterns and links that were hard to spot before. Machine learning tools, like clustering and neural networks, can find hidden structures in data from genetics, social media, or transportation. Statistics help measure uncertainty and test ideas about how systems work. Data mining can even find early warning signs before a system fails. However, scientists have to be careful, just because two things appear linked does not mean one caused the other.

Sometimes researchers also use experiments to study complex systems. In labs, they might create small versions of systems, like testing predator–prey relationships or simulating how markets work. Field experiments test real-world changes, like adjusting traffic lights to see if congestion improves. Experiments can reveal cause-and-effect, but they are often smaller and simpler than the real system.

Often, the best results come from combining different methods. For example, in studying an epidemic, scientists might use network theory to map how people interact, equations to model infection rates, simulations to test control strategies, and real data to check predictions. Climate scientists might mix atmospheric models with satellite images and statistical tools to make better forecasts. Combining theory, simulation, and real-world data gives a fuller picture of the system. Because complex systems can change over time, scientists must keep updating their methods. Models and simulations are improved with new data and better theories. This means that understanding complex systems is always a work in progress, no single method can explain everything, and each approach is just one piece of the puzzle.

Challenges and Criticisms
Studying complex systems is hard because they have many parts that interact in complicated ways. They often change over time, have feedback loops, and do not follow simple cause-and-effect rules. One big problem is that scientists can rarely measure everything in a system. For example, in an ecosystem, it is impossible to track every single plant and animal in detail. In a global economy, no one can record every purchase, job, or business decision. Missing information can make models oversimplify the system, which sometimes leads to wrong or misleading results.

Another challenge is something called sensitivity to initial conditions. This means that very small differences at the start can lead to huge differences later on. It is a key idea in chaos theory and is one reason why weather forecasts become less accurate the further they look ahead. A tiny error in today’s measurements can cause big differences in predictions for days or weeks later. This also affects things like climate predictions or stock market forecasts. Even with the best models, predictions are often better for showing possible outcomes than giving exact answers, which can be frustrating for people expecting certainty.

Model complexity is another problem. If a model tries to include every detail, it can become so complicated that it is hard to understand or too slow for a computer to run. These detailed models might also only work for the exact situation they were built for. On the other hand, if a model is too simple, it may leave out important details that affect how the system works. Finding the right balance between realism and simplicity is one of the hardest parts of studying complex systems.

It is also difficult to test and check these models. In a lab, experiments can be repeated under the same conditions to see if the results are correct. But with big systems like the climate or the global economy, you cannot restart them to see what happens again. Often, different models can explain the same past events but give completely different predictions for the future. This problem, called equifinality, makes it harder to know which model is actually the most reliable.

Complex systems research also gets criticized for sometimes using too many metaphors and analogies. Terms like “emergence” or “self-organization” can be useful for describing patterns, but if they are not clearly defined, they can become vague and hard to test. For example, comparing a social network to a living organism can help people imagine how it works, but it can also hide important differences. Critics worry that if complexity science relies too much on descriptions without solid math or clear measurements, it will be less effective in solving real-world problems.

There are also ethical concerns. Models used for decisions about public health, climate change, or the economy can affect millions of people. If a model is biased, poorly made, or misunderstood, it can lead to harmful choices. For example, a bad disease outbreak model might cause a country to either underprepare or overreact, risking lives or damaging the economy. That is why it is important for scientists to be open about the assumptions, uncertainties, and limits of their models.

Finally, studying complex systems usually involves experts from many different fields. This can make communication difficult because people from different disciplines may use the same words but mean different things, or disagree on the best way to collect and analyze data. Building shared methods and understanding takes time, patience, and a willingness to learn from each other, but it is necessary to make progress in studying these complicated systems.

Related pages
 Biological organisation
 Chaos theory
 Cognitive science
 Complex adaptive system
 Complex networks
 Complexity
 Complexity economics
 Cybernetics
 Decision engineering
 Dissipative system Dual-phase evolution
 Dynamical system
 Dynamical systems theory
 Emergence
 Enterprise systems engineering
 Fractal
 Fractal physiology
 Generative sciences
 Hierarchy theory
 Homeokinetics
 Interdependent networks Invisible hand
 Mixed reality
 Multi-agent system
 Network science
 Nonlinearity
 Pattern-oriented modeling
 Percolation
 Percolation theory
 Process architecture
 Self-organization System accident
 System dynamics
 System equivalence
 Systems engineering 
 Systems theory
 Tektology
 Ultra-large-scale systems

References

Category:Systems