Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate

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

Motivation for Project

Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed?

Motivation for ProjectWhy Do Certain Paradigms, Histories, Ideas Succeed? Most Ideas Do Not Persist ....

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

Motivation for ProjectWhy 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

Positive Legal Theory

Positive Legal TheoryLaw Professors are Important Actors

Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information

Positive Legal TheoryLaw Professors are Important Actors Repositories / Distributors of information Agents of Socialization

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

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

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

Social Network Analysis

Social Network AnalysisMethod for Tracking Social Connections, etc.

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

Social Network AnalysisMethod for Tracking Social Connections, etc.Method for Characterizing Diffusion / Info FlowMethod for Ranking Components basedupon Various Graph Based Measures

Basic Introduction toSocial Network Analysis

Terminology & ExamplesNODES

Terminology & ExamplesNODES Actors

Terminology & ExamplesNODES Institutions Actors

Terminology & ExamplesNODES Institutions Actors States/Countries

Terminology & ExamplesNODES Institutions Firms Actors States/Countries

Terminology & ExamplesNODES Institutions Firms Actors Other States/Countries

Terminology & Examples

Terminology & ExamplesExample: Nodes in an actor- based social Network

Terminology & Examples AliceExample: Nodes in an actor- based social Network

Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill

Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie

Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie David

Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill Carrie David Ellen

Terminology & Examples AliceExample: Nodes in an actor- based social Network Bill CarrieHow Can We Represent TheRelevant Social Relationships? David Ellen

Terminology & Examples AliceArcs Bill CarrieEdges David Ellen

Terminology & ExamplesArcs Alice Bill CarrieEdges David Ellen

Terminology & ExamplesArcs Carrie Alice BillEdges David Ellen

Terminology & Examples DavidCarrie Alice Bill A Full Representation of the Social Network Ellen

Terminology & Examples DavidCarrie Alice Bill A Full Representation Ellen of the Social Network (With Node Weighting)

Social Network Analysis of the American Law Professoriate

Cornell University Law School

Building A Graph Theoretic RepresentationHarvard Penn Cornell

Building the Full Dataset

Building the Full Dataset ....

Full Data Set ....

Full Data Set7,054 Law Professors ! p = {p1, p2, ... p7240} ....

Full Data Set7,054 Law Professors ! p = {p1, p2, ... p7240}184 ABA Accredited Institutions n = {n1 , n2, … n184} ....

Visualizing a Full Network

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

A Graph-Based Measure of Centrality

Hub Score

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

Hub ScoreSimilar 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 byNumber and Quality of Links Normalized Score (0, 1]

HubScore 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

Hub US News Peer Hub InstitutionScore 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

Distribution ofSocial Authority

Top 20 Institutions (By Raw Placements)1,000 800 600400200 BU IllinoisMinnesota Northwesternexas T Duke UCLA Cornell isconsin W 0 NYU Stanford Berkeley UVA GeorgetownPenn Harvard Yale Michigan Columbia Chicago

Highly Skewed Nature of Legal Systems!

Highly Skewed Nature of Legal Systems!Katz & Stafford 2010

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

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

Implications for Rankings

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

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

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

Computational Model of Information Diffusion

Why Computational Simulation?

Why Computational Simulation?History only Provides a Single Model Run

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

Computational Model of Information Diffusion

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

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

A Basic Description of the Model

A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given Institution

A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = p

A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = pInfect neighbors, neighbors-neighbors, etc.

A Basic Description of the ModelConsider a Hypothetical Idea Releasedat a Given InstitutionInfectiousness Probability = pInfect neighbors, neighbors-neighbors, etc.Two Forms Diffusion... Direct Socialization Signal Giving to Former Students

Channels of Diffusion

Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics

Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training

Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials

Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials Academic Conferences, Other Professional Orgs

Channels of DiffusionLots of Channels of Information DiffusionAmong Legal Academics Legal Socialization / Training Judicial Decisions, Law Reviews, Other Materials Academic Conferences, Other Professional Orgs SSRN, Legal Blogosphere, etc.

Channels of DiffusionLots of Channels of Information DiffusionAmong 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

A Sample Run of the Model

Run a Simulationon Your Desktop

Run a Simulationon Your Desktop (Requires Java 5.0 or Higher)

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/

From a Single Run toConsensus Diffusion Plot

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale Implementation

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python http://www.python.org/

From a Single Run toConsensus Diffusion PlotNetlogo is Good for Model Demonstration http://ccl.northwestern.edu/netlogo/Regular Programming Language TypicallyRequired for Full Scale ImplementationWe Used Python http://www.python.org/ Object Oriented Programming Language

From a Single Run toConsensus Diffusion Plot

From a Single Run to Consensus Diffusion PlotRepeated the Diffusion Simulation

From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per School

From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per SchoolYielded a Consensus Plot for Each School

From a Single Run to Consensus Diffusion PlotRepeated the Diffusion SimulationHundreds of Model Runs Per SchoolYielded a Consensus Plot for Each SchoolResults for Five Emblematic Schools Exponential, linear and sub-linear

Computational Simulation of Diffusion uponthe Structure of the American Legal Academy !

Some PotentialModel Improvements?

Some Potential Model Improvements?Differential Host Susceptibility

Some Potential Model Improvements?Differential Host SusceptibilityCountervailing Information / Paradigms

Some Potential Model Improvements?Differential Host SusceptibilityCountervailing Information / ParadigmsS I R Model Susceptible-Infected-Recovered

Directions forFuture Research

Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting Data

Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting DataDatabase Linkage to Articles/Citations Working with Content Providers

Directions for Future ResearchLongitudinal Data Hiring/Placement/Laterals Current Collecting DataDatabase Linkage to Articles/Citations Working with Content ProvidersEmpirical Evaluation of Simulation Computational Lingusitics Text Mining, Sentiment Coding

Thanks for Support

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Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate

by Daniel Katz on Apr 25, 2011

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Reproduction of Hierarchy? A Social Network Analysis of the American Law Professoriate — Presentation Transcript