“Uniformity of Nature” principle
 Inductive inferences make a kind of assumption:
unobserved cases will resemble previously observed cases.
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Hume’s Puzzle
 Can we say what would count as evidence that UN is in
general a reliable principle to follow?
 Same question: Can we state any good reason for thinking
inductively?
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Is UN true?
Notice that UN doesn’t always work:
1. I’ve interviewed 100,000 people, and none won the
lottery.
2. Unobserved cases will be like observed cases.
3. Therefore, no one has won the lottery.
Inference isn’t always successful… but somewhat reliable.
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Can we give a non-deductive
argument for UN?
 Why should you rely on the past to predict the future??
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A “solutions” to Hume’s puzzle
 Peter Frederick Strawson: The idea: “Is it rational to do
induction? By rational we mean, in part, using induction!”
 Asking whether UN is justified is like asking if the law is
legal. Being legal means being in accordance with what
the law says. Asking whether the law is in accordance with
what the law says doesn’t make any sense.
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Strawson’s Idea
 Induction doesn’t need a justification. It is one of the rules
that, if followed, gives beliefs justification.
 This logical move has actually been made before.
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Understanding Strawson
Let’s talk about deduction.
 “What the Tortoise Said to Achilles”
from Lewis Carroll.
Achilles: “(1) All A are B
(2) All B are C
(3) So, All A are C”
 Tortoise: “By what reason does 3 follow
from 1 and 2?”
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Tortoise/Achilles
Achilles: “The reason that the conclusion follows is “IF ‘All A are B,’ AND ‘All B
are C,’ THEN “All A are C.”
Tortoise: You are quite right! That is the reason the conclusion follows. And
the premises are reasons to accept the conclusion, you should add this
missing premise into the set of premises where it belongs.”
Achilles: (1) All A are B.
(2) All B are C
(3) If All A are B and All B are C, then All A are C.
(4) So, All A are C.
Tortoise: Wait, what’s the reason (4) follows from (1)-(3)? It’s missing!
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 Lewis Carroll is pointing out that there are premises, and
on the other hand there are explicitly defined inference
rules. Inference rules are not premises, they connect
premises to conclusions.
 Carroll’s point: “successful deductive reasoning” is
following a certain set of rules; can we say the same for
successful inductive reasoning?
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Induction is not formally defined
like deductive validity
 “We’ve observed 1,000 cases of rats being injected with 1
mg of substance X, and in all cases none lived, therefore
no rats injected with 1 mg of substance X will live.”
 “We’ve observed 1,000 swans, and in all cases, none of
them were black, therefore no swans are black.”
 These inferences have the same form! But one is successful
while the other isn’t.
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Summary so far
 Doesn’t seem to be a non-question begging way to answer
Hume’s riddle here.
 We might wonder: if we can’t make any progress on this
question/puzzle, then maybe there is something wrong with
the question…
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Re-understanding the problem
 “Why will unobserved cases be like observed cases (in
general)?” “Why will the future be like the past (in general)
?”
 Strange questions … because they seems wrong.
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The Right Question:
 We shouldn’t be asking why are unobserved cases like
observed cases generally, or why is the future like the past
generally, but rather:
 In which ways/respects will unobserved cases be like
observed cases? (or, in which respects ways will the future be
like the past?).
This new question is called: “the new riddle of induction.”
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The New Riddle of Induction
 “How will unobserved cases be like observed cases? In
what respect?” Notice: in any particular case, this is a
scientific question! Ex: curve-fitting problem:
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``Simplest” curve is the best?
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…or could be a relationship like
this:
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…or any of these:
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The Point of all the Funny Curves:
 For any set of data points, there is an infinite number of
possible curves which fit the data (are consistent with the
data).
 Solution: just get more data, right?
 Point: inductive projection depends on more than just
data (for example, background assumptions).
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Not just about curves of course
 Very real world problem that any being that learns from
experience somehow solves every day:
 Ex: Last Friday my dog poops in the house, so I scold him.
 What does my dog take the lesson here to be?
 Don’t poop inside?
 Don’t poop inside on Fridays?
 Pee but don’t poop inside?
 Don’t poop in the living room?
 Use the roll of Charmin and the toilet like a human!?
The point: the data in hand does not determine what “theory” is the
right one to project given the data.
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Correlation / Causation
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How projection happens?
 What is the relevant property or pattern for projecting
from observed cases into unobserved ?
 Answer is, unequivocally: it depends.
 Ex 1 : “Typed papers get better grades.”
 Exceptions exist of course. But seems true.
 Ex: “Smokers more often get lung cancer.”
 Exceptions of course. Butt true.
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Example 1: Typing vs. Grades
 How should I project into the future based on this pattern?
 “Will I get a worse grade if I hand-write?”
 Depends, right? What explains the correlation/pattern?
 (1) Maybe: Typing causes better grade. (changes grader’s mind
about the goodness of the paper directly, i.e. easier to read).
 (2) Or: good students take the time to type and revise, thus
having the most presentable paper.
 So , if writing long-hand causes worse grades, seems like I
should type. But if not, shouldn’t expect a real significant
effect.
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Might be a “hidden variable” at
work…
A
A
B
X
B
Causation
vs.
“Correlation via common cause”
“Correlation does not imply causation.”
Taken another example: smoking and getting lung cancer…
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Smoking and Lung Cancer
 Tobacco companies: “we do not deny that smoking and lung
cancer are correlated.”
 Could be a genetic predisposition
 Of course, we now have the data that rules this out this
particular possibility. Experiments with rats: you put them a
cage and they either get smoke or not, it’s not like they get to
chose their cage. Also, introducing smoking into a population
increases cancer.
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Coffee Drinking and Heart Attacks
 Correlation: coffee drinkers get more heart attacks.
 Question: “should I stop drinking coffee if I want to reduce
risk of a hear attack?”
Correlated but
not causing
SMOKE
HEART ATTACK
COFFEE
Kind of causal pattern: effect of a non-causal correlate.
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Another effect of an uncaused
correlate:
 Both polio and ice cream consumption spiked during
summers (only kids get polio).
HEAT
POLIO
ICE CREAM
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Another kind of hidden variable
case:
 Heroin users commit more property crime.
 Should you think that if you use heroin, there is an
increased chance of you breaking into someone’s home
and taking their stuff? Maybe… it depends.
 Effect of an intervening cause:
H-user
Need-$
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Crime
Country music and suicide
 There are higher rates of suicide in parts of the country
where there is more country music on the radio!
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Uninteresting Moral: correlation
does not imply causation
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More Significantly…
 If you want to make a good inductive projection of a
pattern (to all cases, or the next case, or your case), seems
like you need to know what explains the correlation.
 Idea: maybe explanation is “more basic” than induction,
because you need good explanations to make good
inductions.
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