With the separation of natural philosophy from theology, humans freed themselves a bit more from the idea that nature must conform to some preconceived. Starting with Descartes, we tried studying existence “ab initio” (smarty-pants Latin for “from first principles”). Basically, can we start from nothing and create an understanding of reality from the ground up? Descartes started by assuming that he knew nothing, and from that starting point, he tried to determine what could be known with certainty about the real world and his own existence. He realized that he was having thoughts, therefore, he himself must exist. (“Cognito ergo sum”—“I think, therefore I am.”) In a sense, this was the foundation of modern epistemology, the study of knowledge and how we know things.
Building on this, modern science, like any worldview or field of study, is completely dependent on several assumptions (not an exhaustive list):
1) humans are fundamentally rational creatures
2) we live in an ordered universe that follows rational laws that can be studied and understood
3) our senses are fundamentally reliable (not infallible), so we have a basic trust in our perception of reality external to ourselves
4) both our perceptions and natural phenomena can be tested and probed to establish their veracity (agreement between perception and actuality).
In order to create a rational framework for scientific knowledge, the universe is viewed purely as a complex machine. Conceptual models were created to describe it, and they are modified to account for variations and complexities. For example, a human walking, running, or even on horseback will occasionally need to worry about inhaling a bug or having one fly into one’s hair or eyes. Early autos were similar, but as the speed of the cars increased, folks had to now worry about bugs going splat upon contact with their faces. Consequently, the idea of a windshield gained rapid popularity. The basic requirements of a car are an engine, frame and wheels. Things like seats, doors, and windshields were added to make the basic car conform to the reality of human usage. Similarly, scientific models were created to explain the whats and hows of the universe and then modified as failures of the models to conform to the realities of the universe are discovered.
In short, by definition, modern science is a methodology for studying the natural universe. Since it is limited to natural phenomena, it offers little insight into the possibility of supernatural phenomena or nonmechanistic explorations. Two major mistakes that our culture has made are (1) replacing the universe with our models of it and (2) expecting science to explain all of existence. These errors have contributed to the cultural shift from modernity to postmodernity. This is because, as a rule, humans resist and reject the idea that existence is purely mechanistic and recognize that there is more to existence and life than the machinery. One very human problem is the tendency to throw out the baby with the bath water. We did it by assuming that modern science was the only way to look at the whole story, and now as we realize it doesn’t, we are in danger of rejecting it as being irrelevant or strictly an outpouring of some cultural paradigm. It is a tool and needs to be viewed as such, with all of the advantages and limitations intrinsic to it.
The mistakes I mention deserve a little more attention. The second error, expecting science to explain all of existence, is a little less prevalent than it used to be as we have intuitively realized its insufficiency. Justification for the intuition was offered in the “6 Questions” post two days ago, so I won’t spend any more time on it here. The first error, replacing the universe with our models of it, has become more and more prevalent as our models have gotten better and better. Usually, your first attempt at explaining a phenomenon is easily shown to be incomplete as new information is obtained. Thus, it is obvious that it is a model trying to explain reality. So when you obtain data that doesn’t agree with the model, you know that either there was an error in designing/performing the experiment or it is revealing a problem with the model.
As our models improve and we have more confidence in them, we tend to stop saying, “The universe is like…” and start saying, “The universe is…” The difference between these statements is critical. When we stop saying ‘is like,’ we are in danger of putting the validity of the model above the actuality of the phenomenon. When divergent data is obtained, we assume a problem with the experiment, because it certainly can’t be a problem with the model. Maintaining a high level of skepticism throughout one’s career is critical to maintaining a high level of competency in science. We should never be afraid to go back to first principles and spot check the integrity of our models.
SDG
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