◉ A plain English walkthrough of the symbolic logic above.

The formula is a Bayes factor — a way of comparing how well two competing hypotheses explain the same body of evidence.

  1. The hypotheses being compared
    • H_{\mathrm{CC}}: Religion is primarily explained by cultural conditioning (environment and upbringing).
    • H_{\mathrm{FW}} or H_{\mathrm{Fair}}: Religion is explained instead by either free will independence or divine fairness (everyone supposedly has equal access to God).
  2. The evidence being evaluated
    The evidence set (E_1 \land E_2 \land E_3 \land E_4) includes four key empirical findings:
    • E_1: Belief distributions strongly track culture and geography.
    • E_2: Most people adopt their parents’ or community’s religion.
    • E_3: Large populations of sincere non-Christians never experience a clear revelation pointing uniquely to Christianity.
    • E_4: Exposure to any one religion is highly unequal across the globe.
  3. What the Bayes factor means
    • The numerator is the probability of seeing that whole package of evidence if cultural conditioning (H_{\mathrm{CC}}) is true.
    • The denominator is the probability of seeing that same evidence if either free will or divine fairness is true. Because those are two separate hypotheses, the denominator is written as a weighted average (with weights \alpha and 1-\alpha) to represent how much credence you give each.
  4. The inequality \gg 1
    The expression says the Bayes factor is much greater than 1. That means the observed evidence is far more likely under cultural conditioning than under free will or divine fairness.

Summary in plain words:
If people’s religions were truly the result of free choice or equal divine access, we wouldn’t expect beliefs to line up almost perfectly with geography and upbringing. But they do. That evidence makes much more sense if religion is mainly the product of cultural inheritance. The Bayes factor quantifies this: the data are vastly more probable given cultural conditioning than given free will or divine fairness.


◉ Narrative Analogy

Analogy: Languages vs. Free Choice

Imagine you’re trying to figure out why people speak the languages they do. Two explanations are on the table:

  1. Free Choice Hypothesis: People independently choose their language, so we should expect languages to be scattered around randomly — Japanese in Brazil, Spanish in rural China, Hindi in Canada.
  2. Cultural Conditioning Hypothesis: People mostly inherit the language of their parents and community, so we should expect strong clusters — Japanese in Japan, Spanish in Latin America, Hindi in India.

Now, look at the world: languages are clustered by geography, not randomly scattered. That’s the “evidence.”

The Bayes factor is like asking: Which explanation makes the evidence more probable? Clearly, the clustering of language is many, many times more likely under cultural inheritance than under pure free choice.


Applying this to religion:

  • If religious belief were driven by free will or equal divine access, we’d expect a scattered pattern — people all over the world converging on the same “true” religion.
  • Instead, we see the same thing we see with language: tight clusters determined by where and into what culture someone is born.

So the Bayes factor comes out heavily tilted: the “language-style” explanation (cultural conditioning) makes the actual evidence thousands of times more likely than the “free choice/divine fairness” explanation.


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