The fallacy of composition is a logical error that occurs when someone concludes that something true for a part of a whole must also be true for the entire thing, or vice versa.
In simpler terms, it involves expanding narrow understanding, exaggerating small issues, and drawing overly broad conclusions.
For instance, in geometry, "a straight line is the shortest distance between two points" might lead to the fallacy of composition: "wherever there is the shortest distance, there must be a straight path." But the fact is that "the shortest distance between two mountain valleys is a curved path.
CROSS-SCALE ANTICIPATION - Caution
Cross-scale anticipation involves being cautious about contextual differences, breaking down larger entities into smaller modular compositions as modules of truth, so that generalizations can be seen within limits as they are.
Cross-scale is a concept that recognizes that natural and social phenomena can vary depending on the observed spatial and temporal scale.
For instance, in the case of laboratory testing where a drug is proven effective, the fallacy of composition might lead to the idea that consuming a large quantity of the drug will expedite the healing process. However, the reality is that "an overdose can worsen health." By observing organs such as the heart, lungs, liver, kidneys, brain, blood, hormones, etc., we can understand that an overdose does not always have a positive impact on overall health.
CROSS-SCALE PERSPECTIVE - Modular Perspective
One way to counter the fallacy of composition is by breaking down larger entities into small modular compositions, identifying and separating different parts of a system or object, and evaluating the truth of each part separately. This helps in avoiding inappropriate generalizations and understanding the relationship between parts and the whole more clearly.
MODELING REASONING
There are several models for cross-scale reasoning, including:
- 1. Analogy modeling involving relevant context-based comparisons.
- 2. Logical consequence modeling leading to conclusions.
- 3. Cause-effect modeling highlighting dependencies in the process.
Those who reject analogy modeling as small-scale modeling that cannot be extrapolated to large scales may not realize that science employs mathematical modeling, which is essentially a form of small-scale modeling.
MODULE OF TRUTH
Whatever the terminology, it is crucial to recognize that science heavily relies on mathematical modeling (modules of truth), which constitutes small-scale modeling.
So, the issue is not whether analogy modeling can be converted into mathematical modeling, but rather that small-scale modeling, whether analogical or mathematical, remains valid to the extent that its scale does not reduce in a way that the contextual thread is preserved and remains universal, thus enabling cross-scaling and explaining states at a larger scale.
