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A Logic of Diversity

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Title: A Logic of Diversity


1
A Logic of Diversity
Scott E Page Complex Systems, Political Science,
Economics and Institute for Social
Research University of Michigan Santa Fe
Institute
2
The dim boy claps because the others
clap. - Richard Hugo
3
A Logic of Diversity
  • I am going to replace abstract concepts,
    metaphors, and mantras with formal frameworks to
    produce a logic of individual diversity and its
    aggregative implications.

4

Co-Authors
  • Lu Hong Mathematics of Diversity
  • Jenna Bednar Cultural Diversity and
    Institutional Path Dependence

5
The Diversity Mantra

Identity Diversity
Diverse Perspectives
6
Extending The Mantra

Identity Diversity
Diverse Perspectives
Better Outcomes
7
Enlarging The Mantra

Identity Diversity
Diverse Perspectives
Better Outcomes
8
Identity
9
Training
10
Experiential
11
Enlarging The Mantra

Identity, Training, Experiential Diversity
Diverse Perspectives
Better Outcomes
12
Todays Talk Unpacking The First Box


Diverse Perspectives
13
Wednesdays Talk Demonstrating Causality


Better Outcomes
Diverse Perspectives
14
Todays Talk
  • Describing the differences inside of our heads -
    cognitive differences.

15
Brief Intermission
  • Link to training (calculus, physics, etc..)
    obvious.
  • Link to experience (we reason based on past
    cases) also clear
  • But what of identity and culture?

16
A Most Important Question
17
A Most Important Question
  • Where do you keep your ketchup?
  • Fridge?
  • Cupboard?

18
The Follow-up Questions
  • Shoes on or off in your house?
  • Cross street when the red hand is flashing but no
    cars are present?
  • Read newspaper at breakfast table?
  • When you greet friends do you hug?

19
The Diversity Mantra

Identity Diversity
Diverse Perspectives
20
Diverse Perspectives?
  • Perspectives
  • Heuristics
  • Interpretations
  • Mental Models

21
Perspectives
  • A perspective is a representation of the set of
    possible solutions.

22
The Value of Perspectives
Most great breakthroughs in science result from
new perspectives. Newton Planetary
Motion Mendeleyev Periodic Table
23
Diverse Perspectives
(x,y)
(r,?)
  • Cartesian Polar

24
Ben and Jerry
  • chunk
  • size
  • number of chunks

y

x
z
25
Consultant 1
  • caloric rank

z
x
y

26
Consultant 2
  • masticity

z
x
y

27
Rugged Landscapes

28
Perspectives and Difficulty
  • A perspective creates a landscape where the
    elevation of each solution equals its value.
    The better the perspective, the less rugged the
    landscape.

29
Mt Fuji Landscape

30
Caloric Landscape

31
Chew Time Landscape

32
Value of Consultants

33
Perspectives in Strategic Contexts
  • A perspective can also simplify a strategic
    context. What was hard can become easy.

34
Sum to Fifteen Herb Simon
  • Setup Cards numbered 1-9 face up on table
  • Play Players alternate selecting cards
  • Object To hold exactly three cards that add up
    to fifteen

35
4/29/98 Page-De Marchi Match
  • D 7
  • P 6
  • D 5 (12)
  • P 3 (9)
  • D 1 (6,8,12)
  • P 9 (12,9,15)
  • De Marchi offers Draw!!

36
7th Grade Algebra
  • A Magic Square
  • 8 3 4
  • 1 5 9
  • 6 7 2

37
Page-De Marchi Again
  • _ _ _
  • _ _ _
  • _ X _

38
Page-De Marchi Again
  • _ _ _
  • _ _ _
  • 0 X _

39
Page-De Marchi Again
  • _ _ _
  • _ X _
  • 0 X _

40
Page-De Marchi Again
  • _ 0 _
  • _ X _
  • 0 X _

41
Page-De Marchi Again
  • _ 0 _
  • X X _
  • 0 X _

42
Page-De Marchi Again
  • _ 0 _
  • X X 0
  • 0 X _

43
An Equivalence
  • It can be shown that tic tac toe on the magic
    square is equivalent to sum to fifteen.
  • In one perspective the game is hard. In the
    other perspective, the game is easy.

44
What Is Hard Can Be Easy
  • Theorem For any problem there exists a
    representation such that the problem of finding
    an optimal solution is easy.

45
Water Flow Problem
  • Three valves (x,y,z) open 1, closed 0
  • Flow x y z - 2xy - 2yz - 2xz 4xyz

46
Walsh Functions
  • Walsh Function 3
  • W(x,y,z) 0 if 1s is even
  • W(x,y,z) 1 if 1s is odd
  • W(x,y,z) x y z - 2xy - 2yz - 2xz 4xyz

47
Caution
  • Diverse perspectives create more adjacencies, and
    therefore more solutions. Those additional
    solutions include better solutions only if the
    perspectives are appropriate to the problem.
  • More need not imply more better.

48
Heuristics
  • Heuristics are techniques that we use for finding
    solutions. They can take many forms
  • - simulated annealing algorithms
  • - rule of 72
  • - do the opposite

49
Heuristic Example
  • Fill in the blank
  • 1 2 3 5 _ 13

50
Answer
  • 1 2 3 5 8 13

51
Heuristic
  • 1 2 3 5 8 13
  • xi2- xi1 xi

52
Next Question
  • 1 4 _ 16 25 36

53
Answer
  • 1 4 9 16 25 36

54
Heuristic
  • 1 4 9 16 25 36
  • xi2

55
Last One!
  • 1 2 6 _ 1806

56
Answer
  • 1 2 6 42 1806

57
Heuristic
  • 1 2 6 42 1806
  • xi1 xi xi 2
  • 6 2 22
  • 42 6 62
  • 1806 42 422

58
Combining Heuristics
  • 1 2 6 42 1806
  • xi1 xi xi 2
  • This is a combination of the first two heuristics

59
One plus one equals THREE
  • By knowing two heuristics, you know three
    heuristics The two individual heuristic plus
    the combined heuristic.

60
Interpretations
Reality consists of many variables or attributes.
People cannot include them all. Therefore, we
either - consider only some attributes - lump
things together
61
Lump to Live
  • If we did not lump various experiences,
    situations, and events into categories, we could
    not draw inferences, make generalities, or
    construct mental models.

62
Real Life Examples
  • Kerry is a liberal
  • Soccer moms and NASCAR Dads
  • Price Earnings Ratios
  • Autism
  • Modern Art
  • SKA

63
An Example
  • Students and advisors can have one of four
    personality types
  • Obsessive
  • Curious
  • Ambitious
  • Rule Following
  • Outcome function F maps each pair into an outcome
    which is either good or bad.

64
The Outcome Function
student
O C A R
G
G
G
B
O
G
G
G
B
C
advisor
G
B
B
B
A
G
B
B
B
R
65
Advisor Type Interpretation
prediction
G
G
G
B
O
G
C
G
G
G
B
G
advisor
B
G
B
B
B
A
R
B
G
B
B
B
66
Student Type Interpretation
student
O C A R
G
G
G
B
G
G
B
G
G
B
B
B
G
B
B
B
prediction
G
G
B
B
67
Making Horse Races
This is why we differ on our predictions of what
will happen with stock prices, who will win
sporting events, and who is a likely terrorist --
we look at the world differently.
68
Miles Davis
Experts parse the world more finely than the rest
of.
69
Chicago El 1992
red line max 70K riders blue line max
80K riders

70
Chicago El 1992
red line max 70K riders blue line max
80K riders

40K
80K
70K
30K
71
Chicago El 1992
red line max 70K riders blue line max
80K riders

40K
80K
70K
30K
72
Chicago El 1992
yellow line max 40K riders purple line max
80K riders


40K
80K
70K
30K
73
Toolboxes vs Measuring Sticks
  • We can think of a persons ability as her
    collection of tools -- her perspectives, her
    heuristics, and her mental models -- and not as
    an IQ score.

74
More Toolbox Combinatorics
  • With one hundred tools, the number of collections
    of ten tools equals
  • 17,310,309,456,440
  • Compare this to the number of I.Q.s!

75
Toolboxes and IQs
  • Suppose 50 possible tools
  • Sarah knows 20
  • Frank knows 12
  • What are odds that Sarah knows all that Frank
    knows?

76
Toolboxes and IQs
  • Suppose 50 possible tools
  • Sarah knows 20
  • Frank knows 12
  • What are odds that Sarah knows all that Frank
    knows? About 4 in a billion

77
An Implicit Cheat
  • I assumed that any tool can be acquired. (I
    think Ill learn string theory.) That may not
    be true. It could be that tools have an ordering
    - to learn one tool you must first learn another.

78
Ladder Model
  • Suppose the tools are arranged in a ladder, so
    that to learn tool nine you must first learn
    tools 1-8.
  • Sarah now knows tools 1-20
  • Frank now knows tools 1-15
  • Sarah is smarter than Frank.

79
Many Ladders Model
  • Probability
  • of Ladders Sarah gt Frank
  • 1 100
  • 2 29
  • 3 9
  • 4 1
  • 5 0.005

80
A Puzzle
  • Why do people in the humanities and the arts
    believe in the value of diversity and why do
    people in the sciences not?

81
We Believe What We Know
  • Discipline of Ladders
  • Math Very Few
  • Physics Very Few
  • Economics Few
  • Political Science Several
  • Literature Many

82
Summary
  • We dont apply our IQ directly
  • We apply tools
  • Perspectives, heuristics, interpretations, mental
    models
  • Tools are superadditive (42)
  • Cannot universally compare intelligences
  • Can compare domain specific intelligence

83
Whats Next
  • Individual diversity influences collective
    performance.
  • ExplainWisdom of Crowds
  • See that diversity and ability merit equal
    standing
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