Scientific modelling

Scientists make models to learn.

A model is a simple version of something real. It makes big things easier to see.

Sometimes real things are too hard to study. A model helps when we cannot measure things directly. It is a way to help us think.

Some models use computers. These models can show how things change over time. They can show storms or heat.

Other models use living things. Some models use glass tools in a lab.

Scientists check if their models are right. They look at what happens in the real world. This helps them learn more.

Scientists use models to understand our world. A model is a simple version of a real thing.

It might represent an object or a set of steps. Models help when real things are too hard to study. It can be hard to measure things directly in person.

Scientists make many kinds of models. Some use math to find exact numbers. Others use computers to run a simulation. A simulation is a way to show how things act.

Computer models can show how weather changes over time. They can show heatwaves or big storms.

Some models use living things, like lab rats. We call these in vivo models. Other models use glassware in a lab. These are called in vitro models.

To make a model, scientists use abstraction. This means they leave out parts that do not matter for their task. They also use assumptions. An assumption is something they believe is true for the model to work. Scientists check their models against real facts. If a model does not match what we see, they must change it.

Scientific modelling is a very important tool for researchers. It helps people understand objects, events, or physical processes in our world.

Sometimes the real world is too big or too small to study easily. A model acts as a simplified reflection of reality. It is not a perfect copy, but it is a useful one. Scientists use these tools to define, measure, or visualize things. They can even use models to simulate how things might act.

Building a model follows a specific way of working. First, a scientist identifies the important parts of a real situation. They use a method called abstraction to pick only what matters. This means they leave out details that do not help their task. They also use simplification to make the subject easier to handle.

Next, they must make certain assumptions to help the model function. These assumptions define the area where the model is meant to work. Finally, they might turn a conceptual model into a computer simulation. This allows them to see how a system behaves over time.

Many people have studied how these models work throughout history. Philosophers like Ian Hacking and Nancy Cartwright have written about them. In 1961, Leo Apostel studied the formal side of models. John von Neumann also shared important ideas about methods in physical sciences.

Today, philosophers and scientists work together to understand their roles. They look at how models help us gain new intuition. This work helps improve how we use science to learn about nature.

There are many different kinds of models used today. Some are called in vivo, which means they use living things like rats. Others are in vitro, which means they use glassware like tissue cultures. Scientists also use in silico models, which are models run on computers.

Climate models are a great example of complex work. They use data to show things like heatwaves or storms. These models can help us prepare for changes in our weather. They use math and computers to process huge sets of information.

Models are very similar to the maps you might use for travel. A map does not show every single tree or blade of grass. It only shows the paths and places you need to see. Scientific models work in a very similar way for researchers. They help us see patterns that might be hard to find alone. We can use them to predict what might happen in the future. This makes the huge world feel a little easier to understand.

Scientific modelling is a fundamental activity used to represent empirical objects, phenomena, and physical processes. These models act as simulacra, which are simplified reflections of reality. While they are only approximations, they are extremely useful for making the world easier to understand, define, quantify, or visualize.

Scientists use models to simulate how systems might behave under different conditions. This process is considered one of the three pillars of scientific method, alongside theory building and experimentation. By creating these representations, researchers can gain intuition about complex entities that might otherwise be impossible to study directly.

The process of generating a model begins with a specific task or question. To build it, scientists must use two main techniques: simplification and abstraction. Simplification involves leaving out observed entities and relations that are not important for the specific task at hand. Abstraction involves aggregating information that is important but does not need to be shown in extreme detail.

These choices are shaped by physical, legal, and cognitive constraints. A scientist's perception of reality is itself a model, which is often expressed as a conceptual model. To execute this, the model may be implemented as a computer simulation, which requires making further choices like using numerical approximations.

Different types of models serve different scientific purposes. Conceptual models help researchers better understand a subject through ideas and relations. Mathematical models are used to quantify specific features of a system. Computational models allow for the simulation of complex processes, often referred to as being "in silico" when run on computers.

Other models are "in vivo," meaning they use living models like laboratory rats. There are also "in vitro" models, which are conducted in glassware, such as tissue cultures. Finally, graphical models are used to visualize a subject through images or diagrams.

Systems within these models can be categorized by how they change over time. Discrete models feature variables that change instantaneously at separate points in time. In contrast, continuous models involve state variables that change continuously with respect to time.

A simulation is the method used to implement a model, often when the subject is too complex for a direct analytical solution. A steady-state simulation provides data about a system at a specific instant, usually at equilibrium. A dynamic simulation provides information about how a system behaves and changes over a period of time.

History shows that the study of modelling has long been central to science and philosophy. In 1961, Leo Apostel published work on the formal study of models. John von Neumann also contributed significant ideas regarding methods in the physical sciences. Philosophers such as Nancy Cartwright and Ian Hacking have explored how models represent and intervene in the world.

More recently, scholars like Roman Frigg and Stephan Hartmann have noted that philosophers are paying increasing attention to the various roles models play in scientific practice. This growing interest has led to a larger collection of specialized methods and meta-theories.

Evaluating whether a model is successful is a critical part of the scientific enterprise. A model is evaluated primarily by its consistency with empirical data. Any model that is inconsistent with reproducible observations must be modified or rejected.

Beyond data, scientists look at a model's ability to explain past observations and predict future ones. They also consider the cost of use, the degree of refutability, and the simplicity or aesthetic appeal of the model. Even if a model is not perfectly true, scientists often debate which model is better for a specific task, such as seasonal weather forecasting.

One significant application of these methods is in climate science. Climate models apply knowledge from various sciences to process massive sets of input data. They execute differential equations among grid elements within a three-dimensional model of Earth's climate system.

These models can produce simulated climates that include elements like heatwaves and storms. By describing the intensity and frequency of these events, models support adaptation to projected climate change. They also enable extreme event attribution, which helps explain specific weather events through scientific simulation.