Title: Truth-conduciveness Without Reliability: A Skeptical Derivation of Ockham
1Truth-conduciveness Without Reliability A
Skeptical Derivation of Ockhams Razor
- Kevin T. Kelly
- Department of Philosophy
- Carnegie Mellon University
- www.cmu.edu
2Naivete
Lo! An apple.
3Skeptical Hypothesis
Lo! An apple.
Maybe you are a brain in a vat. Everything would
look the same.
4Skeptical Hypothesis
poof
Maybe you are a brain in a vat. Everything would
look the same.
5Retrenchment
Thats not a serious possibility
You have the burden of proof. Its remote. Its
implausible. Its distant from the actual
world. Youre not in my community. Who cares
about the worst case?
6Retrenchment
Thats not a serious possibility
You have the burden of proof. Its remote. Its
implausible. Its distant from the actual
world. Youre not in my community. Who cares
about the worst case?
7Unsatisfying
- Possibilities delimited a priori circular
account. - Possibilities delimited a posteriori how do we
seek knowledge?
So there!
8Zen Approach
- Dont rush to defeat the demon.
Grrrr!
9Zen Approach
- Dont rush to defeat the demon.
- Get to know him extremely well.
- Justification may be located in the demons power
rather than in his weakness.
10The Zen of Computation
- Algorithms are justified by efficiency.
- Efficiency means you couldnt do better.
- You couldnt do better due to a demonic argument
(the halting problem, etc).
11Scientific Theory Choice
Which theory is true?
12Ockham Says
Choose the Simplest!
13Skeptical Hypothesis
Maybe a complex theory is true but the data are
simple
14Puzzle
- An indicator must be sensitive to what it
indicates.
simple
15Puzzle
- An indicator must be sensitive to what it
indicates.
complex
16Puzzle
- But Ockhams razor always points at simplicity.
simple
17Puzzle
- But Ockhams razor always points at simplicity.
complex
18Meno
- If we know that the truth is simple, we dont
need Ockhams razor.
simple
19Meno
- If we dont know that the truth is simple, what
good is Ockams razor?
complex
20Some Standard Responses
21Simple Theories are Virtuous
- Testable (Popper, Glymour)
- Unified (Friedman, Kitcher)
- Explanatory (Harman)
- Symmetrical (Malament)
- Compress data (Rissanen)
- Interesting (Vitanyi)
22But the Truth Might Not be Virtuous
- To conclude that a theory is true because it is
virtuous is wishful thinking (van Fraassen).
23 Overfitting (Akaike, Sober, Forster)
- Empirical estimates based on complex models have
greater mean squared distance from the truth
Truth
24 Overfitting (Akaike, Sober, Forster)
- Empirical estimates based on complex models have
greater mean squared distance from the truth.
Pop! Pop! Pop! Pop!
25 Overfitting (Akaike, Sober, Forster)
- Empirical estimates based on complex models have
greater mean squared distance from the truth.
Truth
clamp
26 Overfitting (Akaike, Sober, Forster)
- Empirical estimates based on complex models have
greater mean squared distance from the truth.
Pop! Pop! Pop! Pop!
Truth
clamp
27 Does Not Aim at True Theory
- ...even if the simple theory is known to be false
Four eyes!
clamp
28Miracle Argument (Putnam, Rosenkrantz)
- Simple data would be a miracle in a complex
world. - Simple data would be expected in a simple world.
29Miracle Argument
Planetary retrograde motion
Mars
Earth
Sun
30Miracle Argument
- Simple data would be a miracle in a complex
world. - Simple data would be expected in a simple world.
epicycle
q
lapping
Complex theory
Simple theory
31Miracle Argument
- Simple data would be a miracle in a complex
world. - Simple data would be expected in a simple world.
epicycle
lapping
q
Simple theory
Complex theory
32However
- Simple data would not be a miracle if the
complex theorys parameter were set near q
epicycle
q
lapping
Complex theory
Simple theory
33The Real Miracle
Ignorance about model p(S) ?
p(C) Ignorance about parameter settings
within theories p(C(q) C) ? p(C(q ) C).
Knowledge about parameter settings across
theories p(C(q)) ltlt p(S).
Is it knognorance or Ignoredge?
34The Ellsberg Paradox
3 ball colors with these frequencies
35The Ellsberg Paradox
p
q
r
Human betting preferences
p
q
gt
36The Ellsberg Paradox
p
q
r
Human betting preferences
!
p
q
gt
r
lt
p
q
r
37Diagnosis
p
q
r
ignorance
knowledge
38Robust Bayesianism (Levi, Kadane, Seidenfeld)
knowledge
ignorance
1/3
?
?
p
q
r
. . .
Credence is range of probs.
1/3
1/3
1/3
. . .
1/3
2/3
0
Choose the act with highest worst-case expected
value.
39Worst-case Expected Values
1/3
?
?
1/3
?
?
1/3
0
gt
gt
lt
1/3
0
2/3
40Whither Ockham?
Since you dont really know that complex worlds
wont produce simple data, shouldnt your
ignorance include distributions concentrated on
such possibilities?
I prefer ignoredge.
41In Any Event
The coherentist foundations of Bayesianism have
nothing to do with short-run truth-conduciveness.
42Temptation
If only the probabilities p(C(q ) C) were
chances rather than opinions. Then the alleged
miracle would be a proper miracle.
43Proof of God (R. Koons 1999)
- Natural chance is determined by the fundamental
theory of natural chance. - If Ockhams razor reliably infers the theory of
natural chance, the chance that a complex theory
of natural chance would have its parameters set
to produce simple data must be low. - But since natural chance is determined by the
free parameters of the fundamental theory of
natural chance, the parameter setting is not
governed by natural chance. - Hence, it must be governed by non-natural chance.
- Holy water is available at the exit.
44Moral
- The basic point is right.
- Solution
- Keep naturalism
- Keep fundamental scientific knowledge
- Dump short-run reliability as explication of
truth-conduciveness.
45 Externalist Magic
- Simplicity informs via hidden causes or tracking
mechanisms.
G
46 With Friends Like Those
- Practice and data are the same.
- Knowledge vs. non-knowledge depends on hidden
causes. - By Ockhams razor, better to explain Ockhams
razor without the hidden causes.
?
47The Last Gasp Convergence
Bayes (washing out of the prior) BIC
(Schwarz) Structural Risk Minimization (Vapnik,
Harman) TETRAD (Spirtes, Glymour, Scheines)
truth
Complexity
48The Last Gasp Convergence
truth
Plink!
Blam!
Complexity
49The Last Gasp Convergence
truth
Plink!
Blam!
Complexity
50The Last Gasp Convergence
truth
Plink!
Blam!
Complexity
51Logic is Backwards
- Ockham methods are sufficient for convergence.
- But every finite variant of a convergent method
converges (Salmon). - So Ockhams razor is not necessary for
convergence.
truth
Alternative ranking
52Truth Conduciveness
- Reliability
- Too strong
- Circles or magic required.
- Convergence
- Too weak
- Doesnt single out simplicity
Complex
Simple
Simple
Complex
53Truth Conduciveness
- Indication or tracking
- Too strong
- Circles or magic required.
- Convergence
- Too weak
- Doesnt single out simplicity
- Straightest convergence
- Just right?
Complex
Simple
Simple
Complex
Complex
Simple
54Truth-conduciveness as Straightest Convergence
Complex
Simple
55Ancient Roots
"Living in the midst of ignorance and considering
themselves intelligent and enlightened, the
senseless people go round and round, following
crooked courses, just like the blind led by the
blind." Katha Upanishad, I. ii. 5, c. 600 BCE.
56Retraction
- New output does not entail previous output.
Retracted Content
t
t 1
57Eliminate Needless Retractions
Truth
58Necessary Retractions are Virtuous
Truth
59Demons Role as Justifier
Truth
I can force every convergent method to retract
this often, so your retractions are justified by
my power.
60Eliminate Needless Delays to Retractions
theory
61Eliminate Needless Delays to Retractions
application
theory
application
application
application
corollary
application
application
application
corollary
application
corollary
62Easy Comparisons
at least as bad at least as many retractions
at least as late
retractions
time
63Worst-case Retraction Time Bounds
(1, 2, 8)
. . .
. . .
. . .
64Empirical Complexity
Hopeless ideas Syntactic length Computational
incompressibility
By what miracle do notational conventions
indicate truth?
65Empirical Complexity
Close but no cigar Free parameters Broken
symmetries
Meno, I want simplicity itself, not parts of
simplicity.
66Empirical Complexity
Empirical complexity of T in G the length of
the maximum path (T1, , Tn, T) of answers in G
the demon can force from an arbitrary convergent
method.
Keep up!
T
T3
T2
T1
67Polynomial Order
- Data open intervals around Y at rational values
of X.
68Polynomial Order
- Demon shows flat line until convergent method
takes bait.
Zero degree curve
69Polynomial Order
- Demon shows flat line until convergent method
takes bait.
Zero degree curve
70Polynomial Order
- Then switches to tilted line until convergent
method takes the bait.
First degree curve
71Polynomial Order
- Then switches to parabola until convergent method
takes the bait
Second degree curve
72Complexity can be Complex
Complexity given e
T2
3
T7
T4
2
T8
T5
1
0
T3
73Complexity Relative to Data
Complexity given e e
T2
3
T7
T4
2
T8
T5
1
0
T3
74Complexity Relative to Data
Complexity given e e
3
2
T2
1
0
T5
T4
T7
75Timed Retraction Bounds
- r(M, e, n) the least timed retraction bound for
worlds satisfying theories of complexity n and
producing finite input history e.
M
. . .
. . .
Empirical Complexity
0
1
2
3
76M is Efficient at e
- For each convergent M that agrees with M along
finite input history e, - for each complexity n
- r(M, e, n) ? r(M, e, n)
M
M
. . .
. . .
Empirical Complexity
0
1
2
3
77M is Strongly Beaten at e
- There exists convergent M that agrees with M up
to the end of e, such that - for each complexity n
- r(M, e, n) gt r(M, e, n).
M
M
. . .
. . .
Empirical Complexity
0
1
2
3
78M is Weakly Beaten at e
- There exists convergent M that agrees with M up
to the end of e, such that - For each n, r(M, e, n) ? r(M, e, n)
- Exists n, r(M, e, n) gt r(M, e, n).
M
M
. . .
. . .
Empirical Complexity
0
1
2
3
79Demons for Ockham
80Ockhams Razor
- Dont select a theory unless it is uniquely
simplest in light of experience.
3
2
?
T2
1
0
T5
T4
T7
81Ockhams Razor
- Dont select a theory unless it is uniquely
simplest in light of experience.
3
2
T7
T2
1
0
T7
82Stalwartness
- Dont retract your answer while it remains
uniquely simplest
3
2
T7
T7,
T2
1
0
T7
83Argument Sketch
- No matter what convergent M has done in the past,
nature can force M to produce each answer down an
arbitrary effect path, arbitrarily often. - Nature can also force violators of Ockhams razor
or stalwartness either into an extra retraction
or a late retraction in each complexity class.
84Ockham Efficiency Theorem
- Let M converge to the true theory in problem P.
The following are equivalent - M is always Ockham and stalwart in P
- M is always efficient in P
- M is never weakly beaten in P.
85Policy Retractions
- Many explanations have been offered to make sense
of the here-today-gone-tomorrow nature of medical
wisdom what we are advised with confidence one
year is reversed the next but the simplest one
is that it is the natural rhythm of science. - (Do We Really Know What Makes us Healthy, NY
Times Magazine, Sept. 16, 2007).
86Causal Inference
- Causal graph theory more correlations ? more
causes. - Idealized data list of conditional dependencies
discovered so far. - Anomaly the addition of a conditional
dependency to the list.
partial correlations
S
G(S)
87Causal Axioms (Pearl, Glymour)
- Screening off X is statistically independent of
its non-descendents given its parents. - No invisible causes The only true independence
relations are those entailed by condition 1.
N1
N1
P1
P2
P2
P1
N2
X
D
88Forcible Sequence of Causal Theories
Y1
X2
X3
W
X1
Y2
89Forcible Sequence of Causal Theories
Y1
Y3
X2
X3
W
X1
Y2
Y4
90Forcible Sequence of Causal Theories
Y1
Y3
X2
X3
W
X1
Y2
Y4
Y5
91Forcible Sequence of Causal Theories
Y1
Y3
X2
X3
W
X1
Y2
Y4
Y5
Y4
92Moral
- In counterfactual prediction, form of model
matters and retractions are unavoidable. - Ockham efficiency agrees very closely with best
contemporary practice. - Maybe thats all there is to it.
93Conclusions
- Ockhams razor is necessary for staying on the
straightest path to the truth - Does not reliably point at or indicate the truth.
- Demonstrably works without circles, evasions, or
magic. - Such a theory is motivated in counterfactual
inference and estimation.