The word theory is derived from Greek theoria. In ancient Greek philosophy, theoria referred to the act of observing or contemplating the natural world in order to gain knowledge and insight. During the middle ages, Latin scholars had a renewed interest in ancient Greek philosophy, and theoria was adapted to mean a system of ideas or principles used to explain a particular phenomenon. Then during the 17th century scientific revolution, when the scientific method was developed because of a new emphasis on empirical observation and experimentation, the use of the word theory became closely associated when referring to something that could explain and predict natural phenomena.
To theorize means to create or develop a theory or set of ideas that explain a phenomenon. Theorizing often involves making assumptions and hypotheses based on available evidence, and then testing these hypotheses through further observation, experimentation, and analysis. The goal of theorizing is to generate new knowledge and ways of understanding, so they can be used to predict future outcomes, solve problems, or improve existing processes and systems. Someone who theorizes is a theorist or theoretician.
The word theoretical refers to a proposed theory, that could be at any point of a wide spectrum of validity, starting from a conjecture and varying through all the levels of scientific testing. With the scientific method, theoretical work can develop conceptual frameworks that provide a starting point, and can guide on going empirical research. It can help synthesize and organize existing knowledge, and generate new hypotheses. Processes can include mathematical modeling and simulation. While the scientific process uses empirical observation and experiments, theoretical work is concerned with the speculation of theories, and quite often uses abstract reasoning and the manipulation of symbols and equations.
Theorem is a term most commonly used in mathematics. It is a statement that is believed to be true by using logical reasoning, based on axioms and assumptions. Hypotheses play a role in proving theorems, but they don’t follow the same procedure as the scientific method. In these cases the hypothesis of a theorem is an assumed truth that is followed by a statement called the conclusion. After the conclusion, the proof consists of a logical argument based on mathematical reasoning.
The significance of mathematical theorems come in the form of when they can solve problems and develop new theories. Some of the fields that theorems have had a successful impact on include, physics, engineering, computer science, and economics. Many of the most important and influential theorems in mathematics have been developed over hundreds of years, and they continue to be refined and extended. The concept of theorem is the fundamental basis for all mathematical research and the development of mathematical models.
Science has been so effective, that the modern usage of the word theory usually refers to a confident, well-supported and widely accepted explanation for what it is explaining. However, revision and refinement as new evidence and interpretations emerge, remind us to never consider a theory to be a final and absolute truth, but rather an evolving and dynamic explanation of nature.
The following is a standard check list for evaluating and refining scientific theories.
Empirical evidence: The theory must be supported by evidence that was gathered through observation, experimentation, and measurement. Mathematics can be applied in a variety of ways. The evidence should be consistent and reproducible, and should support the theory’s predictions.
Falsifiability: The theory must make specific predictions that can be tested empirically, and that the results of these tests must be able to potentially disprove the theory. This approach can help avoid conformation bias, and it is more realistic to believe something when you can’t disprove it.
Logical consistency: The theory must be logically consistent and not contradict other established theories or laws of nature.
Parsimony: The theory should be as simple and straightforward as possible, while still accounting for all available evidence.
Predictive power: The theory should be able to make accurate predictions about future observations or experiments. These predictions should be testable and supported by empirical evidence.
While this list provides some helpful guidelines, it’s not always clear on how to apply or interpret its results in every situation, and sometimes scientists will even disagree on the validity of certain theories. Besides scientist not always agreeing with each other, in society among the non-scientific community, there are even more grey areas and blurred lines. It’s not uncommon for the word theory to be used when the claim is actually wrong. Sometimes it’s used in referring to a suggested idea when people are trying to understand something, and other times it’s used for things that are still theoretical.
The expression “in theory” show us even more ways the word theory can be used.
“In theory, theory and practice are the same. In practice, they are not.” Albert Einstein
“In theory, if we could travel faster than the speed of light, we could go back in time.” Stephen Hawking
“In theory, all men are created equal. In reality, not everyone has the same opportunities.” Mahatma Gandhi
“In theory, quantum computing has the potential to solve complex problems that are impossible for classical computers.” David Deutsch
“In theory, everything is possible. In reality, some things are just highly improbable.” Pierre-Simon Laplace
The word theory is obviously used very broadly. To help with the endeavor of unifying theories, I designed the Theory Accuracy Description (TAD). This is an epistemological approach for understanding and defining philosophical and scientific theories, by rating theory with a 1-10 ranking system. The goal is to determine just how theoretical a theory is or isn’t, and what kind of role it can play in the grand scheme of things. A written description is required as well, because the 1-10 ranking system might lack the dimension required for fully explaining a theory. The following descriptions are for each of the 1-10 ratings.
- A theory that has been thrown out because of disconfirmation. Sometimes hypotheses have to many contradictions. This is revealed when a theory just couldn’t hold up to the scrutiny of the scientific method. Disconfirmation can occur through various means such as, empirical evidence, logical analysis, and new insights that challenge the validity of a particular idea. During the scientific process, disconfirmation and confirmation are critical steps that are achieved by subjecting the hypothesis or theory to rigorous testing. As testing continues, variables are usually slightly changed to determine if the theoretical model can hold up to it’s predictions. This process often consists of little bumps up or down in validity, through on going analysis of data and the refinement of the hypothesis when necessary. While disconfirmation can be exhausting for the scientist, the hardship of this painstaking process is always out weighed by the excitement of when a theory can move up the scale in validity.
- A theory that has elegance, but no mathematical or experimental support. Inviting, but without empirical evidence, only the potential for success. Such theories are often based on intuitive reasoning, analogies, and philosophical arguments. Sometimes there is enough aesthetic and conceptual appeal to make them attractive to researchers, even in the absence of empirical evidence. Elegant concepts have even inspired research, leading to new discoveries and insights, although it is important to note that such theories are often speculative and may not have a place in reality. Theories with conceptual elegance are desired at every level of validity, in all fields, because of the explanatory and coherent power they can have over more complex theories that attempt to explain the same things.
- A theory based on observation, but no mathematical or experimental support. Observational studies are typically used to collect data in natural settings without manipulating any variables. Such theories can inspire and inform the development of hypotheses, but without additional support from mathematical models or experimental data, they can often be subjective, open to bias, and can be influenced by the observer’s perspective, interpretation, and expectations. While observation is only one of numerous important parts of the scientific process, there are areas of research, for example, anthropology and history, where observation is the primary source of information. Observational studies are usually not considered experiments, but occasionally they can be.
- A theory that has no mathematical or experimental backing, but is successful at achieving what it is applied to. This is common in philosophy where ideas and concepts can be explored and debated without the need for empirical evidence. Theories of this nature are also used in engineering and technology fields, when they sometimes use heuristics or “rules of thumb” to design systems or devices that work effectively without a complete understanding of the underlying scientific principles.
- A theory that has mathematics or experiments supporting it, but only one or the other. It is not uncommon to have a theory that has mathematical backing but not experimental. In theoretical physics for example, many proposed theories have not yet been experimentally confirmed, but they are still considered important and worth exploring mathematically. This is because mathematical consistency and elegance often suggest that a theory is worth investigating further. Experiments without mathematic are not as common, but they do exist. One example is, sometimes experimental results may lead to new theories or models that have not yet been fully developed mathematically. Another example is, sometimes complex systems such as the brain or ecosystems are to challenging to develop mathematical models that accurately capture all the interactions and dynamics involved. In such cases, experimental observations may be used to refine or modify existing theories.
- A theory that has both mathematical and experimental conformation. When a hypothesis has been mathematically and experimentally tested and the out come is logically consistent with the predictions, this is when a scientific model is on its way to becoming a real theory.
- A theory that satisfies all of the criteria for empirical evidence. When a hypothesis has confirmation from observation, mathematics, experiments, and measurements, this is when a theoretical model becomes a scientific theory. When a theory has come this far it is now established and very hard to disprove. Although, testing should continue and all it takes is one little anomaly in the data to show that it is incomplete.
- A theory that is the unification of theories. This is when the convergence of evidence from multiple disciplines or approaches lead to a coherent and integrated understanding and theory of particular phenomena. As theories come together, philosophy should be going through it’s own convergence process as well. This would be a result of philosophers focusing more on the new unified facts and their implications, where as before, they had to concentrate on all of the proposed theories that existed before the new unification brought an understanding to the situation.
- A traditional Theory Of Everything. The unification of all fundamental forces. This will also be an era in which the convergence of other scientific theories now have a base to connect to. Philosophy will have a base as well, and instead of speculating on what the origin of the universe is, philosophers will be able to theorize on the implications of an understood origin for the universe, based on science.
- The Tree Of Everything. The unification of all phenomena, scientifically and philosophically. The scientific and philosophical methods require that on going testing, analyzing, and refinement continue in the pursuit of parsimony, how to understand the (Tree-OE), and how to use this knowledge.
The (TAD) system itself should also be worked on and improved over time. Some things that might need to be addressed are, how to rank all the different combinations and levels you can have with, observation, mathematics, experiments, and measurement. There’s also the issue of unifying theories without empirical evidence. Again, this is why writing a description along with the rank is very important.