Basic HTML Version

[insert Figure 1]
Figure 6.1
. The Rosen modeling relation
The two systems are related via the encoding and decoding arrows. Encoding is the
process of measurement: it is the assignment of a formal label (such as a number) to a natural
phenomenon. Decoding is prediction: it is taking what we generate via the inferential
machinery of the formal system into representations of expected phenomena. Additionally,
the arrows for inference and causality represent the entailment structures of their respective
The modeling relation provides us with a way of ascertaining congruence between the
natural system, N, and the formal system or model, F. What determines successful
congruence is that the diagram, as a whole, commutes. That is, the numbered arrows meet the
condition: (1) = (2) + (3) + (4). This means that our measurements (2), when run through the
inferential machinery (3) of our model, will generate predictions (4), which will agree (when
verified) with the actual phenomena (1) occurring in N. It bears mentioning that any encoding
from N to F is an abstraction and if the modeling relation holds, then F is a model of N. If all
four conditions of the modeling relation do not hold, then F is merely a description of N
under a specific condition.
The importance of this distinction between model and description is that it goes to the
very heart of Zack Kopplin’s argument. “Science” is about models and their use. Description
has its place, but teaching schoolchildren representations and descriptions is
them science and the scientific method. Traditional definitions of the scientific method tend
define it as “a method of procedure that has characterized natural science since the 17th
century, consisting in systematic observation, measurement, and experiment, and the
formulation, testing, and modification of hypotheses” (As retrieved from Google in May
2014). Or take the Union of Concerned Scientists’ (2007) definition:
“A scientific hypothesis must be testable and falsifiable. That is to say, a hypothesis
must make predictions that can be compared to the real world and determined to be
either true or false, and there must be some imaginable evidence that could disprove
it. If an idea makes no predictions, makes predictions that cannot be unambiguously
interpreted as either success or failure, or makes predictions that cannot be checked
out even in principle, then it is not science.”
The scientific method and related definitions of “science” are science as a model, where, as in
the Rosen definition, the model can be used to make predictions, and there exists a mapping
between the model and the “observed natural system.”
Faye (2006) conceptualizes realism as follows: