3.2 Empirical Laws Paradigm

Theories in the empirical laws paradigm approach communication from the perspective that there are universal laws that govern how we communicate. Other names for empirical laws include hard science, the positivist approach, the covering-laws approach, and the classical approach. Natural scientists look for universal laws to understand and explain our world.

Using our example of gravity, we know that objects fall to the earth 100% of the time when we drop them. This is a universal law. In the late 1950s, scholars began studying human communication using approaches developed in the natural sciences (a.k.a. the scientific method). Thus, early proponents of empirical laws theories studied communication to see if there were universal communication laws similar to those in the natural world.

Natural laws at work in our world influence every moment of our lives. Every time you fly in an airplane or cross a bridge, you trust that the people who designed and built the plane and bridge followed the physical laws that allow a plane to fly and a bridge to span a distance without collapsing. Every time you press the brakes on a car, you trust them to slow you down based on the laws explaining how long a mass, traveling at a certain speed, takes to stop. Even if you do not understand all of these laws, you live by them and believe the laws themselves hold true 100% of the time.

Are there laws you follow about communication with this kind of regularity? Are they applicable 100% of the time, in all situations, and with all people? What happens if someone breaks one of these laws? Are the consequences similar to when you break physical laws? For example, is the consequence of calling someone by the wrong name comparable to that of hitting your brakes and them not working? Those who approach communication from an empirical laws perspective believe there are laws that govern human communication. The premise of this approach can be stated as a simple equation of causation: If X, then Y. For example, if I greet a person with “Hi, how are you?” then I anticipate a response, “Fine, how are you?” It’s likely that you conduct much of your communication using this equation. However, does that mean that it works all of the time?

There are three characteristics that help us understand empirical laws theories: causation, prediction, and generalization (Infante, Rancer & Womack). Causation states that there is a “cause and effect” relationship for all actions. In the physical world, if someone drops a pen, it will fall. In human communication, if someone says “hello” to someone, that person responds. Prediction suggests that once someone determines a particular law is at work, they can use it to predict outcomes of future similar communication situations. Have you ever rehearsed how you will ask someone out on a date and tried to predict the outcome? What evidence did you use to make your prediction? In this example, you are using the “If X, then Y” equation to predict the outcome of the interaction.

Generalization suggests that if a prediction shows that behavior produces a certain outcome, we can generalize our predictions to include a wide variety of people, situations, and contexts. We make generalizations such as “If I’m friendly to others, they will be friendly to me” based on our past experiences with this type of behavior. However, this does not account for scenarios in which the person might not hear you, might be having a bad day, does not wish to respond, or assumes you are talking to another person, so they choose not to acknowledge you.

Three circle connected to one another. One says Causation - There is a "cause and effect" relationship for all actions, Another says Generalization - If a prediction shows that a behavior produces a certain outcome, we can generalize our predictions to include a wide variety of people, situations, and context, and the last named Prediction - once an individual determines a particular law is at work, they will use it to predict outcomes of communication situations.
Causation, Generalization, Prediction Image by Wikibooks is licensed under CC BY-SA 3.0

In the physical sciences, laws are absolute. This is comforting because it allows us to make informed decisions based on what we know about the laws that govern the world around us. In our example of gravity, we know that dropping an object will produce the same result every time. We could spend the rest of our lives testing this theory, but we don’t have to. We know what the result will be without having to continuously drop an object. Now imagine what it would be like to always know what the outcome would be of every communication situation! Would that be comforting to you or make your life boring?

Unlike the physical world, laws that govern human communication are not absolute and are most often bound by culture and context. Empirical laws theories are generally approached from the perspective of probability rather than absoluteness (Miller & Berger). Probability states that under certain conditions, it is highly likely that we can predict communication outcomes. For example, when you greet someone with “hello,” it is probable, not absolute, they will respond back with a greeting of their own. If they do not, you might run through a variety of reasons why the other person did not respond in accordance with the “laws” that govern greetings in our culture. Even though empirical laws theories do not produce absolutes about communication, we still use them in our everyday interactions with one another. Businesses, advertisers, schools, and other organizations use this approach to predict consumer, educational, and behavioral habits of particular demographic groups. While their approaches never produce a 100% cause-effect relationship, the information they gather helps them determine what actions to take to be successful in their communicative behaviors.

Empirical laws theories are a relatively new approach for understanding communication. We have only been developing empirical laws theories of communication for the past 100 or so years. To date, none of this research has concluded that given a certain circumstance, a particular communicative behavior will always produce a particular outcome. However, working under an empirical laws approach that accepts probability, we have many research examples that demonstrate probable laws that govern human communication.


A particular strength of empirical laws theories is that they help us determine cause-and-effect relationships in our communication with others. Understanding communication using these theories helps us predict the outcomes of our interactions with others. While we know that not all outcomes can be determined with 100% reliability, prediction and control allow us to navigate our encounters more easily. Think about the number of encounters you have each day in which you quickly predict and control your interaction with others. While not 100% conclusive, it’s comforting that a great number of our interactions have a certain level of probable outcomes.


A criticism of empirical laws theory is that while it is useful for understanding relatively simple interactions, it can oversimplify or fail to explain situations where a number of variables exist. Your classroom environment serves as a good example. While there are certain predictions you can make about how communication will occur in your classes, why is it that each classroom experience is unique? In your classrooms, it is impossible to predict, control, and generalize how a class will go with 100% accuracy because it is impossible to replicate classes in exactly the same ways. This approach does not account for the variety of human choices and behaviors that are brought into every communication context. It operates under the assumption that given the same context, people bring the same things to the context each time. Obviously, this is not the case. Human behaviors are complex and cannot be predicted at a 100% accuracy rate. However, empirical laws theories work well for showing us patterns of behavior that guide our communication.



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