Class content > Introduction to the class

Humans have evolved the capacity to make sense of the world. By that we mean that humans do not just passively experience the world, they actively try to understand how it works. This is extremely useful in that it allows us to both anticipate and respond appropriately to our environmental surroundings (see Models of memory). Science is an extension of this basic human desire to make sense of the world. That is,

Science is not just about figuring out how the world works, science is about figuring out how we can think about how the world works.

This means that it is not just the results of science that matter -- it is how it helps us make sense of these results.  And we do this by building theories, principles, and models.

Theories in Science.

What makes science different from  everyday sense-making is that scientists have, with the help of a community of minds, developed strategies for systematically constructing, using, and revising ways of thinking about the world. Instead of just making a best guess about how something works, scientists articulate their ideas with concepts and principles, often using abstract representations, diagrams, and a precise technical vocabulary to create a powerful and coherent structure.  In many areas of scientific research, these ideas developed into broad powerful structures that help us think about and make sense of a wide variety of phenomena.

 A scientific theory is a formalized set of ideas about how some aspect of the world works that specifies the relevant objects, agents, or constructs (the stuff) as well as the ways in which they influence, interact with, and relate to one another (relationships).

In physics, an example that we will study this term is Newton's theory of motion. This theory sets out the concepts we need to understand how things move -- velocity, acceleration, force, energy, and so on.  The principles of this theory, Newton's Laws, allow us to make sense of a huge range of physical phenomena, ranging from the motion of planets to the meaning of temperature in a gas.

In biology, an example of a very general and powerful theory is Darwin's theory of evolution. Darwin fleshed out the details of his ideas in his book, On the Origin of Species, but it can really be boiled down to a few simple statements (see Darwinian model of natural selection).

Scientific Modeling

In any specific example, the broad general ideas of a scientific theory have to be selected, organized, and pared down. The real world is immensely complex and not everything is important in every case. What one has to consider depends on the questions one is asking. In thinking about whether a cat can knock an object off a table, we only need to think about the object's mass, the friction of the table, and the force the cat can exert. Whether the object is a rock that will survive the fall or a crystal bowl that will shatter doesn't matter in thinking about if it will fall or not.

In medicine, the functioning of how the heart moves blood through the body to first get oxygen from the lungs and then deliver oxygenated hemoglobin to the rest of the body at one level of detail may ignore leakage from blood vessels and the detailed structure of the cells in the blood. But in thinking about certain diseases, the properties of the particular cells in the blood may become critical.

The ideas in this model of the blood can easily be represented verbally, but other models might require quantification to get the specificity they require. Another example from biology is the Lotka-Volterra model of predator-prey interactions. This model is often represented as a series of differential equations, but the ideas in the model are actually quite simple: prey populations increase due to growth and decrease due to predation, and predator populations increase with amount of prey and decrease when prey die. This model works well to describe some aspects of the interaction of the populations and has produced some lovely fits to the oscillations of the numbers of foxes and rabbits, for example. But it ignores the fact that the prey has to live on something and may itself be a predator. (The rabbits eat plants.) In some situations this requires we modify our model and choose a more complex one. Once a model is out in the scientific community, it can be challenged, tested, revised, or reinforced according to a set of criteria agreed upon by the scientific community.

The value of modeling.

An important point is that models serve a variety of purposes in science. Sometimes models just help us think through ideas; other times, models allow us to make precise quantitative predictions about the world; other times, models serve as the starting point for designing empirical studies by pointing out what we don't yet know.

How models are constructed, used, and evaluated in science depends upon the particular question, aim, or purpose that is relevant to the situation.

To make the distinctions among different types of models more clear we might consider three broad classes of models:

1. Descriptive models are used to formally describe some aspect of the world, some pattern or observed regularity. Such models are empirically derived; that is, they arise from scientists’ attempts to observe and systematically record what they see. Watson and Crick's model of the structure of DNA is an example of a simple descriptive model. Galileo's description of the motion of objects by specifying the position, velocity, and acceleration of objects (whose size and structure are ignored in this model of motion) is a critical start in understanding how and why things move.

Descriptive models often help scientists articulate what it is they want to understand and often pave the way for deeper investigations. Once we understand and can describe some of the regularities that exist in the world, the next step is explaining them.

2. Explanatory models lie at the heart of science, in that they are constructed and used to explain how the world works. Explanatory models unpack the causal mechanisms that underlie what we observe in the world. They go deeper than descriptive models in their attempt to uncover the particular process or set of interactions that drive a phenomenon. Natural selection is an example of an explanatory model that allows us to explain patterns of change in populations over time in terms of the underlying causes. Explanatory models answer questions: What causes x? How does x happen? Why does x make sense? How can we explain x pattern?

For example, one could apply the model of natural selection to an observed frequency of a trait in a population in order to generate an explanation for how that frequency came about. Newton's development of the concept of force and relating the net force felt by an object to its acceleration provides the structures to create explanatory models of motion. Note that explanatory models can be qualitative or quantitative.

3. Phenomenological models are used for the purpose of making specific predictions about what might happen in the future given some range of starting conditions. Such models may or may not include a deep understanding of the underlying mechanisms. They often rely on observed consistent empirical regularities or correlations. Our description of the properties of deformable matter will have this character at first (until we begin to look at how the observed properties of matter depend on its molecular structure). In biology, many disease models are of this type. They may leave out many details of the biology of how diseases are transmitted, but because they use empirically derived parameters (e.g. transmission rates, migration rates) they can be fairly accurate in their predictions. Such models are  common in conservation biology, epidemiology, and medicine.

The main ideas about models in science.

1. Modeling is a general practice that scientists use to describe, explain and predict natural phenomena.

2. Constructing models requires making decisions how to bound the world into manageable chunks. It includes choosing and justifying assumptions, simplifications, and being upfront about what you are trying to describe, what you are including, and what your are ignoring -- and the resulting limitations of your model.

3. Models may be qualitative or quantitative. If they are quantitative they may permit prediction.

4. Ideally, models are tested against empirical data. Models that are able to explain empirical patterns and make robust predictions are more certain than those that are used early in investigations to pose hypothetical ideas.

5. Different fields and even different scientists within a field will use models in different ways depending on their interests and depending on the state of the science at the time.

6. All fields of scientific inquiry rely on model construction, use and evaluation to move theory forward, to suggest areas in need of further investigation and to interpret empirical data.

Julia Svoboda and Joe Redish 8/29/11