1. Complex Systems Models
in the Social Sciences
(Lecture 6)
daniel martin katz
illinois institute of technology
chicago kent college of law
@computationaldanielmartinkatz.com computationallegalstudies.com

2. Today I Would Like to
Sketch (In Part)
Where the World
is Heading

3. Simply
Put

4. A
Much
More
Data
Driven
World

5. Highlighting the Data Deluge
2008 2009 2010

6. Highlighting the Data Deluge
2011 2011

7. Highlighting the Data Deluge

8. Before Talking
About the
Specific
Applications

9. Some Broad Trends

10. What is Driving
the Big Data
Revolution?

11. This is the Era of “Big Data”
Decreasing Data Storage Costs
Increasing Computing Power
Fundamentally Altering the Scope of Scientific &
Technical Possibility

13. Moore’s law
!

15. And
How
Big is
‘Big’?

16. How Much Data Is a Petabyte?

17. How Much Data Is a Petabyte?

18. How Much Data Is a Petabyte?

19. How Much Data Is a Petabyte?

20. How Much Data Is a Petabyte?

21. How Much Data Is a Petabyte?

22. How Much Data Is a Petabyte?

23. How Much Data Is a Petabyte?

24. How Much Data Is a Petabyte?

25. Kryder’s law
!

26. How Much Data Is a Petabyte?

28. Implications for the Economy

29. Erik Brynjolfsson is the Schussel Family
Professor at the MIT Sloan School of
Management , Director of the MIT
Center for Digital Business, Chair of
the MIT Sloan Management Review ,
and the Editor of the Information
Systems Network
Andrew McAfee, a principal research
scientist at MIT’s Center for Digital
Business, studies the ways that
information technology (IT) affects
business.

30. . .. ....
..
......
....
............ 128
256 512 1024 2048 4096 327688192 16384
65536 131k 262k 524k 1M 2M 4M
................................................................
8M
................................
16M 33M 67M 134M 268M 536M 1B 2B
4B 8B 17B 34B 68B 137B 274B 549B
1T 2T 4T 8T 17T 35T 70T 140T
281T 562T 1Q 2Q 4Q 9Q 18Q 36Q
72Q 144Q 288Q 576Q 1QT 2QT 4QT 9QT

33. Implications for
Science + Engineering

34. How About This One ...

35. 2004 Darpa
Grand Challenge

36. Goal:
Build a Driverless
Car that Could
Travel 150 miles

37. Winning Vehicle
Traveled
only
Eight Miles

38. Fast Forward
to 2012 ...

41. Some Applicable Terms
That Will Drive
The Future

42. Natural Language Processing
Clustering
Knowledge Representation
Machine Learning
Feature Selection
Feature Extraction
Classification

43. http://www.drewconway.com/zia/?p=2378

45. A Simple Demo of
Machine Learning

46. Smarter Than You
Think ...

47. Smarter Than You
Think ...

48. A Brief Discussion of
the McKinsey Study

49. http://www-01.ibm.com/software/data/bigdata/industry.html

50. Financial Services
Help agents find patterns, understand context and
flag abuse by analyzing customer correspondence in
conjunction with structured information.
Predict consumer behavior by using cross-line of
business details to correlate consumer debt ratios
and transaction patterns.
Automate entity identification and risk profiling based
on SEC and other regulatory filings, with the ability
to assemble, model and stay current on large
volumes of information.
Consume, analyze and act on real-time market data
while maintaining sub-millisecond response times, even
under extreme data loads.

51. Retail
Analyze social media data in conjunction with
customer buying data to predict customer behavior,
and track sentiment and brand perception.
Monitor social media sources for rumors,
deliberate false information, and impersonation of
employees to more quickly understand and correct
misinformation.
Issue marketing promotions, analyze their success
in real time, and adapt promotions to optimize
outcome.
Analyze linkages between specific online
advertising and recommendations to buying
behavior so that you can strategically merchandise
and place effective advertising.
Run highly complex pattern detection algorithms
on years, rather than months of transaction data,
allowing the organization to rapidly detect and
respond to new fraud scenarios and exposures.
(Also True of Financial services)

52. Healthcare
Perform complex real-time analytics on
physiological streams of data in ICU
environments to detect life-threatening
conditions in time to proactively intervene.
Manage and analyze real-time sensor data to
assist chronic disease patients.
Capture and analyze clinical information from
electronic health records to speed the creation
and diffusion of medical knowledge.
Track a wide variety of data streams and
leverage prior benchmarks to potentially help
to expose the early signs of an epidemic

53. Telecom
Perform real-time mediation with the ability to
handle billions of call detail records per day.
Process real-time call data to predict customer
churn and remediate customer satisfaction issues
(ex: dropped calls) as soon as they happen.
Enable real-time geo-mapping and marketing
promotions.
Analyze social media data with customer buying
data to predict customer behavior and track
sentiment and brand perception.
Unlock the insights embedded in call center voice
recordings by doing voice-to-text conversions,
then performing advanced text analytics on the
converted recordings.

54. The Artificial Intelligence
Revolution is On

55. The Artificial Intelligence
Revolution is On

56. http://www.youtube.com/watch?v=lI-M7O_bRNg&feature=relmfu
Watch This On Your Own

57. http://www.youtube.com/watch?v=DywO4zksfXw&feature=related
Watch This On Your Own

59. A Brief Word About Data
Driven Theory Building

60. Hypothesis Testing
is the Core of
Mainstream Science

61. Deduction

62. Popperian
Falsification

64. Partial or
Complete Induction

65. Is the Alternative

68. In Case You
Did not Know

69. This is an
Inductive
Age

70. This is the Age of
Aspirational Spelling
(Spelling is 1.0 Thinking)

71. The Inverse Problem

72. Kepler v. Newton

73. Net Flix Prize -- From the AT&T
Labs http://www.youtube.com/watch?v=ImpV70uLxyw

74. The Music Genome Project

75. A Brief Word About Data
Driven Theory Building
One Billion Clicks Can Be the
Basis for a Theory
Generation
The Hypothesis Testing Frame
is not the only way to do
Science

76. Validation Can Be Achieved
Through Either:
(1) Out of Sample Testing
(2) Forward Prediction
A Brief Word About Data
Driven Theory Building
http://en.wikipedia.org/wiki/Netflix_Prize
http://www.wired.com/epicenter/2009/09/
how-the-netflix-prize-was-won/
Example:

78. This is the Age of
Aspirational Spelling
(Spelling is 1.0 Thinking)
Additional Examples

79. Additional Examples
People Who Bought X
Also Bought Y
Collaborative Filtering

80. The Science of Similarity
What Makes a Set of High Dimensional
Objects ‘Similar”?
Movies People Words Music Books
Crowd Sourced Human Reasoning is Helping
Develop a ‘New’ Science of Similarity

81. Computer
Forensics
Drawn from Jesse Kornblum
1. Feature Extraction
2. Feature Selection
3. Comparison
4. Clustering
5. Classification
6. ???
The ‘???’ means:
– Which features to extract
– Which similarity measure to use
– Which classification algorithm
The Science of Similarity

83. Machine Learning

84. http://www.youtube.com/watch?
v=yDLKJtOVx5c&feature=results_video&playnext=1&list=PLD0F06
AA0D2E8FFBA

85. http://www.youtube.com/watch?v=EFrgVDniDqU&feature=related

86. If you Want a Full Course in the Topic It is
Free From Stanford University

87. http://www.forbes.com/sites/oreillymedia/
2012/01/05/goodbye-information-economy-
hello-feedback-economy/

88. © daniel martin katz michael j bommarito
Machine Learning
is the heart of
predictive analytics

89. Legal Analytics
Professor Daniel Martin Katz
Professor Michael J Bommarito II
© daniel martin katz michael j bommarito

91. Supervised
Statistical models
Bayesian, e.g., Naïve Bayes Classification
Frequentist, e.g., Ordinary Least Squares
Neural Networks (NN)
Support Vector Machines (SVM)
Random Forests (RF)
Genetic Algorithms (GA)
Semi/Unsupervised
Neural Networks (NN)
Clustering
K-means
Hierarchical
Radial Basis (RBF)
Graph
Some Machine Learning Methods
© daniel martin katz michael j bommarito

92. http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html
© daniel martin katz michael j bommarito

93. classification
clustering
regression
dimension reduction
the family of machine learning methods © daniel martin katz michael j bommarito

94. Quick Example of
the Methods
© daniel martin katz michael j bommarito

95. © daniel martin katz michael j bommarito
Adapted from Slides By
Victor Lavrenko and Nigel Goddard
@ University of Edinburgh
Take A LookThese 12

96. © daniel martin katz michael j bommarito
72
Female
Human
3
Female
Horse
36
Male
Human
21
Male
Human
67
Male
Human
29
Female
Human
54
Male
Human
44
Male
Human
50
Male
Human
42
Female
Human
6
Male
Dog
7
Female
Human

97. © daniel martin katz michael j bommarito
Classification
(Supervised Learning)
decision
boundary
female
male
f( )
Gender?

98. © daniel martin katz michael j bommarito
Classification
(Supervised Learning)
decision
boundary
female
male
f( )
Gender?
Regression
(Supervised Learning)
#f( )
Age?
723
2
3
67
54
29
42
44 50
7
6
27 44 53 3
68
2
48
10
6
743
4
4

99. © daniel martin katz michael j bommarito
Classification
(Supervised Learning)
decision
boundary
female
male
f( )
Gender?
f( )
Loan
Application?
Yes
Multi Class Classification
(Supervised Learning)
No
Maybe
Yes
Perhaps
No
Multiclass =
Boundary
Hyperplane
Regression
(Supervised Learning)
#f( )
Age?
723
2
3
67
54
29
42
44 50
7
6
27 44 53 3
68
2
48
10
6
743
4
4

100. © daniel martin katz michael j bommarito
Classification
(Supervised Learning)
decision
boundary
female
male
f( )
Gender?
f( )
Loan
Application?
Yes
Multi Class Classification
(Supervised Learning)
No
Maybe
Yes
Perhaps
No
Multiclass =
Boundary
Hyperplane
Regression
(Supervised Learning)
#f( )
Age?
723
2
3
67
54
29
42
44 50
7
6
27 44 53 3
68
2
48
10
6
743
4
4
Clustering
(Unsupervised
Learning)
Clusterf( )
Group?

101. classification
clustering
regression
dimension reduction
the family of machine learning methods © daniel martin katz michael j bommarito

103. Why I Am Interested in
(Obsessed with) Analogy

104. Analogy

105. Deep End of Human Reasoning?

106. What Makes
an Analogy
Convincing?

107. Analogical Reasoning

108. Law

109. Thinking
Like a
Lawyer

111. Psychology

112. The Availability
Heuristic
(Related but not exactly on point)

114. Science

115. Interdisciplinary
Scholarship

117. Markets

118. Emerging
Technology
Venture
Capital
Pricing
Model
Arbitrage
Detection

119. Emerging
Technology
Venture
Capital
Pricing
Model
Arbitrage
Detection
Analogy

120. How Much is
Facebook Worth?
Do I have the
right analogy?
1 billion
10 billion
100 billion
500 billion

121. The Science of Similarity

122. and of course

123. The Curse of Dimensionality

124. The
Revolution
in
Soft
Artificial
Intelligence

125. Computer
Forensics
Drawn from Jesse Kornblum
1. Feature Extraction
2. Feature Selection
3. Comparison
4. Clustering
5. Classification
6. ???
The ‘???’ means:
– Which features to extract
– Which similarity measure to use
– Which classification algorithm

126. My hope is that the AI Revolution
will allow us to better understand
analogical reasoning as it is a critical
for a large number questions we
care about .....

127. http://blog.ted.com/2010/07/14/when_ideas_have/
When Ideas
Have Sex
By Matt Ridley