What are Scientific Laws—3 Views:
1. Regularity View—Laws describe regularities among natural phenomena. (e.g., Galileo’s law: Heavy objects released near the earth’s surface fall to the ground with constant acceleration.)
2. Regularity View (Conditional Form)—Laws describe regularities among natural phenomena under idealized conditions. (e.g., Galileo’s law [amended]: Heavy objects released near the earth’s surface in a vacuum fall to the ground with constant acceleration.)
3. Laws as Characterizations of Powers or Dispositions—Laws describe the potentialities of natural entities to act in certain ways. (e.g., Galileo’s law [amended]: Heavy objects released near the earth’s surface have a propensity to fall to the ground with constant acceleration.)
Objections to the Regularity View:
1. The regularity view fails to account for the difference between accidental and lawlike regularities.
· example of accidental regularity: Everyone in this class is under seven feet tall.
· example of lawlike regularity: Everyone in this class respires.
2. The regularity view fails to distinguish causes from effects.
· For example, to say that there is a regularity between lightning and thunder does not say which is the cause and which is the effect.
3. Because of disturbing influences, few if any scientific laws describe exceptionless regularities among natural phenomena.
· example—Law: Objects of greater density than water sink when placed in water. (exception: needles)
Objection to the Regularity View (Conditional Form):
According to this view, scientific laws do not describe, and do not apply to, the vast majority of real-world situations in which the idealized conditions identified by the law do not exist.
Objections to Laws as Characterizations of Powers or Dispositions:
1. Causal qualities, powers, dispositions, tendencies, etc. are not observable and thus are empirically suspect.
2. Some scientific laws—e.g., the first and second laws of thermodynamics and the law of conservation of energy—“have consequences for the behaviour of physical systems, and can be used to predict their behaviour, independently of the details of the causal processes at work.” (Chalmers, p. 221)