1. Reproduction of Hierarchy?
A Social Network Analysis of the
American Law Professoriate
Daniel Martin Katz
Josh Gubler
Jon Zelner
Michael Bommarito
Eric Provins
Eitan Ingall

2. Motivation for Project

3. Motivation for Project
Why Do Certain Paradigms, Histories, Ideas Succeed?

4. Motivation for Project
Why Do Certain Paradigms, Histories, Ideas Succeed?
Most Ideas Do Not Persist ....

5. Motivation for Project
Why Do Certain Paradigms, Histories, Ideas Succeed?
Most Ideas Do Not Persist ....
Function of the ‘Quality’ of the Idea

6. Motivation for Project
Why Do Certain Paradigms, Histories, Ideas Succeed?
Most Ideas Do Not Persist ....
Function of the ‘Quality’ of the Idea
Social Factors also Influence the Spread of Ideas

7. Positive Legal Theory

8. Positive Legal Theory
Law Professors are Important Actors

9. Positive Legal Theory
Law Professors are Important Actors
Repositories / Distributors of information

10. Positive Legal Theory
Law Professors are Important Actors
Repositories / Distributors of information
Agents of Socialization

11. Positive Legal Theory
Law Professors are Important Actors
Repositories / Distributors of information
Agents of Socialization
Socialize Future lawyers, Judges & law Professors

12. Positive Legal Theory
Law Professors are Important Actors
Repositories / Distributors of information
Agents of Socialization
Socialize Future lawyers, Judges & law Professors
Responsible for Developing Particular Legal Ideas
(Brandwein (2007) ; Graber (1991), etc.)

13. Positive Legal Theory
Law Professors are Important Actors
Repositories / Distributors of information
Agents of Socialization
Socialize Future lawyers, Judges & law Professors
Responsible for Developing Particular Legal Ideas
(Brandwein (2007) ; Graber (1991), etc.)
Law Professor Behavior is a Important
Component of Positive Legal Theory

14. Social Network Analysis

15. Social Network Analysis
Method for Tracking Social Connections, etc.

16. Social Network Analysis
Method for Tracking Social Connections, etc.
Method for Characterizing Diffusion / Info Flow

17. Social Network Analysis
Method for Tracking Social Connections, etc.
Method for Characterizing Diffusion / Info Flow
Method for Ranking Components based
upon Various Graph Based Measures

18. Basic Introduction to
Social Network Analysis

19. Terminology & Examples
NODES

20. Terminology & Examples
NODES
Actors

21. Terminology & Examples
NODES
Institutions
Actors

22. Terminology & Examples
NODES
Institutions
Actors
States/Countries

23. Terminology & Examples
NODES
Institutions
Firms
Actors
States/Countries

24. Terminology & Examples
NODES
Institutions
Firms
Actors
Other
States/Countries

25. Terminology & Examples

26. Terminology & Examples
Example: Nodes in an actor-
based social Network

27. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network

28. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network
Bill

29. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network
Bill
Carrie

30. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network
Bill
Carrie
David

31. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network
Bill
Carrie
David
Ellen

32. Terminology & Examples
Alice
Example: Nodes in an actor-
based social Network
Bill
Carrie
How Can We Represent The
Relevant Social Relationships?
David
Ellen

33. Terminology & Examples
Alice
Arcs
Bill
Carrie
Edges
David
Ellen

34. Terminology & Examples
Arcs Alice Bill
Carrie
Edges
David
Ellen

35. Terminology & Examples
Arcs
Carrie
Alice Bill
Edges
David
Ellen

36. Terminology & Examples
David
Carrie
Alice Bill
A Full Representation
of the Social Network
Ellen

37. Terminology & Examples
David
Carrie
Alice Bill
A Full Representation
Ellen
of the Social Network
(With Node Weighting)

38. Social Network Analysis of the
American Law Professoriate

39. Cornell University
Law School

40. Cornell University
Law School

41. Cornell University
Law School

42. Cornell University
Law School

43. Cornell University
Law School

44. Cornell University
Law School

45. Building A Graph Theoretic
Representation
Harvard Penn
Cornell

46. Building A Graph Theoretic
Representation
Harvard Penn
Cornell

47. Building A Graph Theoretic
Representation
Harvard Penn
Cornell

48. Building A Graph Theoretic
Representation
Harvard Penn
Cornell

49. Building the Full Dataset

50. Building the Full Dataset

51. Building the Full Dataset

52. Building the Full Dataset

53. Building the Full Dataset
....

54. Full Data Set
....

55. Full Data Set
7,054 Law Professors
! p = {p1, p2, ... p7240}
....

56. Full Data Set
7,054 Law Professors
! p = {p1, p2, ... p7240}
184 ABA Accredited Institutions
n = {n1 , n2, … n184}
....

57. Visualizing a Full Network

58. Visualizing a Full Network

59. Visualizing a Full Network

60. Visualizing a Full Network

61. Visualizing a Full Network

62. Zoomable Visualization Available @
http://computationallegalstudies.com/

64. Zoomable Visualization Available @
http://computationallegalstudies.com/

65. A Graph-Based Measure
of Centrality

66. Hub Score

67. Hub Score
Similar to the Google PageRank™ Algorithm
Measure who is important?
Measure who is important to who is important?
Run Analysis Recursively...

68. Hub Score
Similar to the Google PageRank™ Algorithm
Measure who is important?
Measure who is important to who is important?
Run Analysis Recursively...
Score Each Institution’s Placements by
Number and Quality of Links
Normalized Score (0, 1]

69. Hub
Score
Rank
1
US News
Peer
Assessment
1
Hub
Score
1.0000000
Institution
Harvard
Hub Scores
2 1 0.9048631 Yale
3 5 0.8511497 Michigan
4 4 0.7952253 Columbia
5 5 0.7737389 Chicago
6 8 0.7026757 NYU
7 1 0.6668868 Stanford Hub US News
Hub
8 8 0.6607399 Berkeley Score Peer Institution
Score
Rank Assessment
9 10 0.6457157 Penn
10 10 0.6255498 Georgetown 26 24 0.1999686 UC Hastings
11 5 0.5854464 Virginia 27 34 0.1974877 Tulane
12 14 0.5014904 Northwestern 28 28 0.1749897 USC
13 10 0.4138745 Duke 29 35 0.1702638 Ohio State
14 10 0.4075353 Cornell 30 24 0.1586516 Boston College
15 15 0.3977734 Texas 31 72 0.1543831 Syracuse
16 28 0.3787268 Wisconsin 32 19 0.1537236 UNC
17 19 0.3273598 UCLA 33 56 0.1525355 Case Western
18 24 0.2959581 Illinois 34 82 0.1511569 Northeastern
19 28 0.2919847 Boston University 35 19 0.1428239 Notre Dame
20 28 0.2513371 Minnesota 36 56 0.1286375 Temple
21 24 0.2403289 Iowa
37 82 0.1232289 Rutgers Camden
22 28 0.2275534 Indiana
38 56 0.1227421 Kansas
23 19 0.2235015 George
39 64 0.1213358 Connecticut
24 16 0.2174677 Washington
Vanderbilt
40 47 0.1198901 American
25 41 0.2012442 Florida
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

70. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

71. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

72. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

73. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

74. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

75. Hub US News Peer Hub
Institution
Score Rank Assessment Score Score
26 24 0.1999686 UC Hastings
27 34 0.1974877 Tulane
28 28 0.1749897 USC
29 35 0.1702638 Ohio State
30 24 0.1586516 Boston College
31 72 0.1543831 Syracuse
32 19 0.1537236 UNC
33 56 0.1525355 Case Western
34 82 0.1511569 Northeastern
35 19 0.1428239 Notre Dame
36 56 0.1286375 Temple
37 82 0.1232289 Rutgers Camden
38 56 0.1227421 Kansas
39 64 0.1213358 Connecticut
40 47 0.1198901 American
41 34 0.1162101 Fordham
42 64 0.1150860 Kentucky
43 106 0.1148082 Howard
44 47 0.1125957 Maryland
45 28 0.1101975 William & Mary
46 56 0.1058079 Colorado
47 19 0.1041129 Emory
48 17 0.1031490 Washington & Lee
49 72 0.1027442 Miami
50 103 0.1006172 SUNY Buffalo

76. Distribution of
Social Authority

77. Top 20 Institutions
(By Raw Placements)
1,000
800
600
400
200
BU IllinoisMinnesota
Northwesternexas
T Duke UCLA Cornell isconsin
W
0 NYU Stanford Berkeley UVA
GeorgetownPenn
Harvard Yale Michigan Columbia Chicago

78. !
!

79. Highly Skewed Nature of
Legal Systems
!

80. Highly Skewed Nature of
Legal Systems
!
Katz & Stafford 2010

81. Highly Skewed Nature of
Legal Systems
!
Katz & Stafford 2010

82. Highly Skewed Nature of
Legal Systems
Smith 2007
!
Katz & Stafford 2010

83. Highly Skewed Nature of
Legal Systems
Smith 2007
!
Katz & Stafford 2010

84. Highly Skewed Nature of
Legal Systems
Smith 2007
!
Katz & Stafford 2010 Post & Eisen 2000

85. Implications for Rankings

86. Implications for Rankings
Rankings only Imply Ordering ( >, =, < )

87. Implications for Rankings
Rankings only Imply Ordering ( >, =, < )
End Users tend to Conflate Ranks with
Linearized Distances Between Units
(Tversky 1977)

88. Implications for Rankings
Rankings only Imply Ordering ( >, =, < )
End Users tend to Conflate Ranks with
Linearized Distances Between Units
(Tversky 1977)
Non-Stationary Distances Between Entities
Both Trivial and Large Distances
Linearity Heuristic Often Works
Assuming Linearity Can Prove Misleading

89. Computational Model of
Information Diffusion

90. Why Computational
Simulation?

91. Why Computational
Simulation?
History only Provides a Single Model Run

92. Why Computational
Simulation?
History only Provides a Single Model Run
Computational Simulation allows ...
Consideration of Alternative “States of the world”
Evaluation of Counterfactuals

93. Computational Model of
Information Diffusion

94. Computational Model of
Information Diffusion
We Apply a simple Disease Model to
Consider the Spread of Ideas, etc.

95. Computational Model of
Information Diffusion
We Apply a simple Disease Model to
Consider the Spread of Ideas, etc.
Clear Tradeoff Between Structural Position
in the Network and “Idea Infectiousness”

96. A Basic Description
of the Model

97. A Basic Description
of the Model
Consider a Hypothetical Idea Released
at a Given Institution

98. A Basic Description
of the Model
Consider a Hypothetical Idea Released
at a Given Institution
Infectiousness Probability = p

99. A Basic Description
of the Model
Consider a Hypothetical Idea Released
at a Given Institution
Infectiousness Probability = p
Infect neighbors, neighbors-neighbors, etc.

100. A Basic Description
of the Model
Consider a Hypothetical Idea Released
at a Given Institution
Infectiousness Probability = p
Infect neighbors, neighbors-neighbors, etc.
Two Forms Diffusion...
Direct Socialization
Signal Giving to Former Students

101. Channels of Diffusion

102. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics

103. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics
Legal Socialization / Training

104. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics
Legal Socialization / Training
Judicial Decisions, Law Reviews, Other Materials

105. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics
Legal Socialization / Training
Judicial Decisions, Law Reviews, Other Materials
Academic Conferences, Other Professional Orgs

106. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics
Legal Socialization / Training
Judicial Decisions, Law Reviews, Other Materials
Academic Conferences, Other Professional Orgs
SSRN, Legal Blogosphere, etc.

107. Channels of Diffusion
Lots of Channels of Information Diffusion
Among Legal Academics
Legal Socialization / Training
Judicial Decisions, Law Reviews, Other Materials
Academic Conferences, Other Professional Orgs
SSRN, Legal Blogosphere, etc.
Other Channels of Information Dissemination

108. A Sample Run of the Model

109. A Sample Run of the Model

110. A Sample Run of the Model

111. A Sample Run of the Model

112. A Sample Run of the Model

113. A Sample Run of the Model

114. A Sample Run of the Model

115. A Sample Run of the Model

116. A Sample Run of the Model

117. A Sample Run of the Model

118. A Sample Run of the Model

119. A Sample Run of the Model

120. A Sample Run of the Model

121. A Sample Run of the Model

122. A Sample Run of the Model

123. Run a Simulation
on Your Desktop

124. Run a Simulation
on Your Desktop

125. Run a Simulation
on Your Desktop

126. Run a Simulation
on Your Desktop
(Requires Java 5.0 or Higher)

127. Run a Simulation
on Your Desktop
(Requires Java 5.0 or Higher)

128. Run a Simulation
on Your Desktop
(Requires Java 5.0 or Higher)

129. Run a Simulation
on Your Desktop
(Requires Java 5.0 or Higher)
http://computationallegalstudies.com/2009/04/22/the-revolution-will-not-be-televised-but-will-it-
come-from-harvard-or-yale-a-network-analysis-of-the-american-law-professoriate-part-iii/

130. From a Single Run to
Consensus Diffusion Plot

131. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration

132. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration

133. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration

134. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/

135. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/
Regular Programming Language Typically
Required for Full Scale Implementation

136. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/
Regular Programming Language Typically
Required for Full Scale Implementation
We Used Python

137. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/
Regular Programming Language Typically
Required for Full Scale Implementation
We Used Python

138. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/
Regular Programming Language Typically
Required for Full Scale Implementation
We Used Python http://www.python.org/

139. From a Single Run to
Consensus Diffusion Plot
Netlogo is Good for Model Demonstration
http://ccl.northwestern.edu/netlogo/
Regular Programming Language Typically
Required for Full Scale Implementation
We Used Python http://www.python.org/
Object Oriented Programming Language

140. From a Single Run to
Consensus Diffusion Plot

141. From a Single Run to
Consensus Diffusion Plot
Repeated the Diffusion Simulation

142. From a Single Run to
Consensus Diffusion Plot
Repeated the Diffusion Simulation
Hundreds of Model Runs Per School

143. From a Single Run to
Consensus Diffusion Plot
Repeated the Diffusion Simulation
Hundreds of Model Runs Per School
Yielded a Consensus Plot for Each School

144. From a Single Run to
Consensus Diffusion Plot
Repeated the Diffusion Simulation
Hundreds of Model Runs Per School
Yielded a Consensus Plot for Each School
Results for Five Emblematic Schools
Exponential, linear and sub-linear

145. Computational Simulation of Diffusion upon
the Structure of the American Legal Academy
!

146. Some Potential
Model Improvements?

147. Some Potential
Model Improvements?
Differential Host Susceptibility

148. Some Potential
Model Improvements?
Differential Host Susceptibility
Countervailing Information / Paradigms

149. Some Potential
Model Improvements?
Differential Host Susceptibility
Countervailing Information / Paradigms
S I R Model Susceptible-Infected-Recovered

150. Directions for
Future Research

151. Directions for
Future Research
Longitudinal Data
Hiring/Placement/Laterals
Current Collecting Data

152. Directions for
Future Research
Longitudinal Data
Hiring/Placement/Laterals
Current Collecting Data
Database Linkage to Articles/Citations
Working with Content Providers

153. Directions for
Future Research
Longitudinal Data
Hiring/Placement/Laterals
Current Collecting Data
Database Linkage to Articles/Citations
Working with Content Providers
Empirical Evaluation of Simulation
Computational Lingusitics
Text Mining, Sentiment Coding

154. Thanks for Support