Expected value

Metadata

• Slug: 00133-expected-value
• Type: PARADOX
• Tags: probability
• Sources: 1

Axioms

• Assume the rules of the domain apply uniformly.
• Assume the observer’s criteria remain fixed.
• Assume classification boundaries stay consistent.
• Assume the model describes the real case.
• Assume repeated steps do not change the outcome.
• Assume no hidden variables are introduced midstream.

Contradictions

• Two reasonable lines of inference yield opposite conclusions
• A global rule conflicts with a local judgment
• A stable resolution appears to violate a starting premise
• Changing the framing reverses the outcome
• Intuition and formalism diverge at the same step

Prompts

• Which assumption is doing the most hidden work?
• What changes if you relax the smallest constraint?
• Does the paradox dissolve or relocate when reframed?
• What is conserved, and what is sacrificed?

Notes

Sources

• https://en.wikipedia.org/wiki/Expected_value

Overview

In probability theory, the expected value (also called expectation, expectancy, expectation operator, mathematical expectation, mean, expectation value, or first moment) is a generalization of the weighted average. The expected value of a random variable with a finite number of outcomes is a weighted average of all possible outcomes.

Tension

• Two reasonable lines of inference yield opposite conclusions.
• A global rule conflicts with a local judgment.
• A stable resolution appears to violate a starting premise.
• Changing the framing reverses the outcome.
• Intuition and formalism diverge at the same step.

Why It Matters

This entry tests how a stable rule-set can yield unstable conclusions under certain assumptions.

Prompts

• Which assumption is doing the most hidden work?
• What changes if you relax the smallest constraint?
• Does the paradox dissolve or relocate when reframed?
• What is conserved, and what is sacrificed?