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# Uncertainty by William Briggs

Author:William Briggs [Briggs, William]
Language: eng
Format: epub
ISBN: 9783319397559
Publisher: Springer International Publishing, Cham
Published: 0101-01-01T00:00:00+00:00

7.3 Paths

There is a difference, as there was for truth (necessary and conditional or local), between universal and partial or limited deterministic models. The model of the projectile was, in absence of any other information, partial; so was the red-blue-object model. Both models say propositions will be true or false given the stated conditions, but the partial model contains premises which are not (known to be) necessarily true. Deterministic models may also be over-loaded, which is when two partial deterministic models have different, not logically equivalent premises, but which make identical predictions about a set of propositions. More than one model can explain the same set of facts. But there can only be one true understanding of cause.

The goal is to discover universal deterministic models, which contain necessarily true premises and which lead to certainty and where the nature and essence of the events are understood. Given the results of quantum mechanics, it appears this goal cannot be met for efficient causes for some events. No full, universal efficient deterministic model of nature exists: if one did, it would be the prized Theory of Everything. Even though we are barred from complete knowledge, rich and useful conditional models abound.

Einstein, Podolsky, and Rosen [67] famously said that “If, without in any way disturbing a system, we can predict with certainty (i.e., with probability equal to unity) the value of the physical quantity, then there exists an element of reality corresponding to that quantity.” What might that mean to deterministic versus causal models?

Before you is a machine that has a dial marked 1 through 3 and a light. Moving the dial through its states and the light turns yellow, blue, white. From this you form the premise (with obvious shorthand) “If D1, yellow; if D2, blue; if D3, white.” This is a deterministic model. It says that, given certain conditions, certain other things happen with certainty. Extreme probabilities (0 and 1) are easily derived from deterministic models with the addition of a minor premise and some proposition of interest. For instance, add the minor premise “D2 (the dial is in position 2)” and propositions “The light is white” or “The light is chartreuse.” Given this model, these propositions are false. We could have also deduced, from these two premises, the proposition “The light is blue.”

Why did the model turn its various colors? I have no idea. How can the model be causal if we don’t know all the causes of some event? Because it turns out we don’t know all (as in all) the causes in any contingent event, yet we can sometimes understand essences. I don’t need to know, or even need to care about every cause of the light, either, not if all I am interested in is its color.

The model relates to propositions of the light’s colors, even though there are lots of facts about the machine and it milieu which exist but which we ignore. It is I hope obvious that some thing or things were the efficient cause of the light turning color.