philosophy discussion
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Phil 2: Puzzles and Paradoxes
Prof. Sven Bernecker
University of California, Irvine
Lecture 14.3
Solutions to the
Lottery Puzzle
Examples of Lottery Puzzle
1) S knows that S won’t have enough money to go on a safari this year.
2) If S knows that S won’t have enough money to go on a safari this
year, then S is in a position to know that S will not win a major prize in
a lottery this year.
C) Therefore, S is in a position to know that S will not win a major prize in
a lottery this year
Intuitively (1) and (2) are true, but in spite of the fact that (C) follows from
(1) and (2), intuitively (C) is not true.
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Solutions to the Lottery Puzzle
• Denial of knowledge of ordinary propositions (skepticism)
• Acceptance of the conclusion
• Denial of closure under known implications
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Skepticism
• Proposal: We don‘t know ordinary propositions.
• Problems:
i. Hard to believe.
ii. It seems to conflict with knowledge as the norm of
assertion: “Assert that p only if you know that p” (see
lecture 11.3, slide #4)
From the knowledge norm of assertion plus skepticism it
follows that we should hardly every assert anything.
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iii) Skepticism also conflicts with the principle about practical
reasoning: ”In deliberating about what to do, you should
only use known propositions as premises“
From the principle about rational reasoning plus skepticism it
follows that almost all of our practical reasoning is flawed.
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Acceptance of Conclusion
Proposal: We do know lottery propositions
Problems:
i. Inconsistent beliefs: If you know that ticket T1 will not win the lottery,
then it seems plausible, by parity of reasoning, to say the same thing
about ticket T2, ticket T3, etc. But then it seems that you can know of
an arbitrarily large percentage of tickets that they will not win. So you
are inconsistent if you also believe that the lottery is fair.
ii. Practical inconsistency: If you know that the ticket you are about to
purchase is a loser why do you purchase it?
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Denial of Closure Principle
• Counterexample: You're at the zoo, and in the pen in front of you is a striped
horse-like animal. You seem to know that the animal is a zebra. But what
about the possibility that it's a mule painted to look like a zebra? You may
have some reason to believe that it's not a cleverly-disguised mule. But your
evidence isn’t good enough to know that it's not a cleverly-disguised mule. So
you don't know it. However, the latter proposition denying the disguised-mule
hypothesis can be easily known by you to be entailed by the former
proposition asserting that the animal is a zebra. Hence, closure under known
implications fails.
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Closure under known implication: If S knows that p and if S knows
that p implies q, then S knows that q.
The Cost of Closure Denial
• 1. Given that knowledge is the norm of assertion, denial of closure under
known implications sanctions inconsistent assertions.
• Example:
I ask S whether she agrees that p. She asserts that she does. I then ask
S whether she realizes that q follows from p. “Yes,” she says. I then ask
her whether she agrees that q. “I’m not agreeing to that,” she says. I ask
her whether she now wishes to retract her earlier claims. “Oh no,” she
says. “I’m sticking by my claim that p and my claim that p entails q. I’m
just not willing to claim that q.” S is clearly inconsistent.
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• 2. Denying closure under known implications amounts to denying
either of these three principles:
Addition Closure: If S knows p and competently deduces (p or q)
from p, thereby coming to believe (p or q), while retaining
knowledge of p throughout, then S knows (p or q).
Equivalence Principle: If S knows a priori that p is equivalent to q
and knows p, then S is in a position to know q. (see lecture 13.1,
slide #13)
Distribution Principle: If S knows that p and q, S knows p and S
knows q.
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Suppose you know the ordinary proposition:
That is a zebra
Given Addition Closure you can deduce:
That is a zebra or that is a thing that is not a cleverly
disguised mule
And this statement is equivalent to the lottery proposition:
That is not a cleverly disguised mule
So denying closure under known implications implies denying either
addition closure or the equivalence principle.
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Suppose I know the ordinary proposition:
That is a zebra
By equivalence I can know:
That is a zebra and that is not a cleverly disguised mule
By distribution I can know the lottery proposition:
That is not a cleverly disguised mule
• So denying closure under known implications implies denying either
the equivalence principle or the distribution principle.
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Restricting Closure
Proposal:
Problems:
• Ad hoc solution
• Vague definition of lottery propositions
• Logical principles are supposed to hold for all propositions,
regardless of their content
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Restricted closure under known implications: If S knows that p and if S
knows that p implies q, then S knows that q, unless q is a lottery proposition.
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Relevant Alternative Closure
• The crucial question in assessing a knowledge attribution is whether S
would believe that p even if some not-p alternative, q, were in fact the
case.
• But not all of the not-p alternatives are relevant, for otherwise skepticism
would follow. If knowing that p would require the elimination of every
known alternative to p, as suggested by the closure principle, we could
never know anything about the world around us.
• A more plausible view might be that knowledge requires the elimination
of only relevant not-p alternatives and that skeptical alternatives are
normally not relevant. Which not-p alternatives are relevant depends on
the context.
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What is Relevance?
• In the objective reading, the relevance of an alternative has to do
with the objective probability of its realization.
• In the subjective reading, an alternative is relevant if the believer
takes it to be probable. Here relevance depends on the salience the
alternative.
• The subjective reading comes in two flavors. The relevance of an
alternative can be dependent on the conversational context of the
epistemic subject (knower) or on the conversational context of the
person describing the epistemic subject as a knower or a non-
knower (knowledge attributor).
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