*Originally published on 14-Nov-11; updated 20-Nov-11*

In part 1 of my series on the evidential Argument from Scale (AS), I concluded that Everitt’s formulation of AS is unsuccessful. At the same time, however, I said that there is something about the AS I find intuitive and so I wanted to try revising AS as a Bayesian argument to see if I could make a stronger version. The purpose of this post is to attempt to do just that.

Let us organize the relevant evidence into B, the relevant *background evidence,* and E, the *evidence to be explained.* E represents the evidence which functions as *puzzling facts *that need to be explained. B represents evidence which influences the explanatory power of rival theories — that is, the probability of rival theories given the evidence to be explained.[1]

*B: The Relevant Background Evidence*

1. A physical universe, which operates according to natural laws and which supports the possibility of intelligent life, exists.

2. Human beings are a type of intelligent life and exist only on Earth. Furthermore, human beings are moral agents.

*E: The Evidence to be Explained*

1. __Temporal Scale__: Humans appeared in the universe long after the beginning of the universe. For more than 99.999% of the history of the universe, humans have been absent from it. Even if we only consider earth history, for more than 99.99% of the history of the earth, humans have been absent from the earth.[2]

2. __Spatial Scale__: The total universe is many orders of magnitude greater than the size of the earth. The greater part of the universe is not accessible to human exploration.[3]

*H: Rival Explanatory Hypotheses*

Finally, let us consider two rival explanatory hypotheses which might be used to explain this data.

__theism (T)__: the hypothesis that there exists an omnipotent, omniscient, and morally perfect person (God) who created the universe.

__metaphysical naturalism (N)__: the hypothesis that the universe is a closed system, which means that nothing that is not part of the natural world affects it.

**Basic Strategy**

According to Bayes’ Theorem, the *final probability *of a hypothesis, Pr(H | B & E), is determined by multiplying its *prior probability,* Pr(H | B), by its *explanatory power, *Pr(E | H & B). If we have two rival hypotheses, the *relative odds form *of Bayes’ Theorem enables us to measure the *ratio *of these values in order to determine which hypothesis has the greatest overall balance of prior probability and explanatory power:

Pr(H1| B & E) Pr(H1 | B) Pr(E | H1 & B)

------------- = ---------- X --------------

Pr(H2| B & E) Pr(H2 | B) Pr(E | H2 & B)

If E is evidence favoring H1 over H2, then the ratio on the left hand side of the equation will be greater than one. If that ratio is less than one, E favors H2 over H1. And if the ratio equals one, then E is evidentially neutral: it favors neither H1 nor H2 over the other.

One strategy for showing that E favors H1 over H2 is to show that both of the ratios on the right-hand side of the equation are greater than one. Another method would be to show that one of the ratios on the right-hand side of the equation is greater than one, while the other ratio on the right-hand side of the equation is greater than or equal to one. It is the latter strategy which I shall pursue here.

**Bayesian Argument — Version 1**

Let’s begin by applying the relative odds form of Bayes’ Theorem to our rival explanatory hypotheses N and T:

Pr(N| B & E) Pr(N | B) Pr(E | N & B)

------------ = --------- X --------------

Pr(T| B & E) Pr(T | B) Pr(E | T & B)

Again, my strategy will be to determine if there is a way to show that one of the ratios on the right-hand side of the equation is greater than one, while the other ratio on the right-hand side of the equation is greater than or equal to one.

*The Ratio of Prior Probabilities*

Let’s consider the first ratio on the right-hand side of the equation. This asks us to compare the prior probability of N (i.e., the probability of N conditional upon B) to the prior probability of T (i.e., the probability of T conditional upon B). Here I am going to argue that Pr(N | B) > Pr(T | B). This statement is supported by a comparison of the two hypotheses’ *scope *and *simplicity.*

First, let’s consider scope, which may be defined as how much a theory purports to tell us about (the contingent features of) the world.[4] Roughly speaking, the greater scope of a theory, the more it says that might be false and the more likely it is to say something that *is* false.[5] N entails that natural entities all *lack* supernatural causes, whereas T says they all *possess* supernatural causes. Moreover, T also makes very specific claims about the attributes of this supernatural cause; T says the supernatural cause is omnipotent, omniscient, morally perfect, etc. Thus, N does not have greater scope than T.[6]

Second, let’s turn to simplicity, which may be defined as “a measure of the degree of (objective) uniformity that the theory attributes to the world.”[7] Following Paul Draper, I am inclined to believe that simplicity is intrinsically more probable than variety or change, since to believe otherwise would make it hard to have a non-circular justification for our reliance on inductive reasoning.[8] Turning to N and T, it seems obvious that N is simpler than T. T entails that reality includes at least one radically different kind of entity (i.e., a supernatural person) which N denies. Thus, N attributes greater uniformity to reality than T does. Therefore, N is ontologically simpler than T.[9]

I conclude, therefore, that Pr(N | B) > Pr(T | B). In other words, the prior probability of naturalism is higher than the prior probability of theism.

*The Ratio of Explanatory Powers*

Let us now turn to the second ratio on the right-hand side of the equation. This asks us to compare the explanatory power of N to the explanatory power of T. Everyone would agree, I think, that N provides us with no antecedent reason to expect E, i.e., N & B do not provide us with a reason to expect E.

But what about T? Based *solely* on the statements I have explicitly identified as our relevant background information, B1 & B2, I think it’s clear that T *also* provides us with no antecedent reason to expect E. Some skeptics have argued that T *does* provide us with an antecedent reason to expect E, namely, that the non-human scale of the universe suggests that humans were created by a very wasteful process.[10] In my opinion, however, this argument fails. It assumes that, if God exists, God’s sole purpose for creating the universe was for the use and benefit of human beings. That assumption strikes me as just that: an *assumption* with no evidence or reason offered in support. Furthermore, if we assume that God’s purpose(s) include the creation of embodied intelligent life, then it seems probable that He would have created intelligent life many times throughout the universe, not just on Earth. If that is the case, however, then it would be extremely unlikely that God created the universe solely for the use and benefit of human beings.

I conclude that, relative to the background information B, both N and T have *equal* explanatory power with respect to E. Therefore, this version of AS fails.

In my next post, I will consider another Bayesian version of AS.

**Series on the Argument from Scale**

**Notes**

[1] I owe this way of explaining the distinction between B and E to Robert Greg Cavin.

[2] Nicholas Everitt, *The Non-Existence of God* (New York: Routledge, 2004), 216, 218.

[3] Everitt 2004, 217.

[4] In this paragraph, I have borrowed heavily from Paul Draper, “Evil and Evolution,” unpublished paper.

[5] Ibid.

[6] Ibid.

[7] Ibid.

[8] Ibid.

[9] Ibid.

[10] As Dr. Ellie Arroway (played by Jodie Foster) put it in the movie *Contact, *“If we are alone in the universe, it sure seems like an awful waste of space.”