Upgrade to Pro — share decks privately, control downloads, hide ads and more …

Some Thoughts on Modeling Legal Systems

Some Thoughts on Modeling Legal Systems

“Modeling the Law & Justice System” - Virtual Talk at the Indian Institute of Technology - Delhi

This presentation highlights the Complexity Challenge in Law, Six Forms of Modeling Legal Systems and concludes with some thoughts about Law + STEM.

Thanks to the following organizations for hosting me -
DAKSH
Indian Institute of Technology, Delhi
National Law University, Delhi
DCoE IIT, Delhi

0f2a473c07602f3dd53c5ed0de0c56b5?s=128

Daniel Martin Katz
PRO

May 27, 2021
Tweet

Transcript

  1. S O M E T H O U G H

    T S O N M O D E L I N G L E G A L SY ST E M S @computational professor daniel martin katz danielmartinkatz.com Illinois tech - chicago kent law computationallegalstudies.com https://bit.ly/3yxhY2e
  2. None
  3. None
  4. TO DAY L E T M E B E G

    I N W I T H A N O B V I O U S STAT E M E N T …
  5. W H I C H ( F O R S

    O M E ) M I G H T A L S O B E A P R OVO CAT I V E STAT E M E N T …
  6. L AW I S E N G I N E

    E R I N G
  7. H O W E V E R , TO DAY

    I T I S A PA RT I C U L A R TY P E O F E N G I N E E R I N G
  8. I M P L E M E N T E

    D I N A PA RT I C U L A R M A N N E R
  9. T H E Q U E ST I O N

    I S TO W H AT E X T E N T T H E F I E L D O F L AW M I G H T B E N E F I T F R O M
  10. I N S I G H T S O F

    F E R E D B Y T H E T R A D I T I O N A L ST E M ACA D E M I C F I E L D S
  11. None
  12. F O R T H E B E N E

    F I T O F O U R M U LT I D I S C I P L I N A R Y AU D I E N C E
  13. L E T U S STA RT W I T

    H T H E B R OA D E ST P O S S I B L E Q U E ST I O N
  14. W H AT I S T H E P U

    R P O S E O F L AW A N D L E G A L I N ST I T U T I O N S ?
  15. S O C I E T I E S C

    R E AT E R U L E S TO T R Y TO G OV E R N VA R I O U S F O R M S O F S O C I A L , E CO N O M I C A N D P O L I T I CA L B E H AV I O R
  16. S O C I E T I E S C

    R E AT E G OV E R N A N C E I N ST I T U T I O N S D E VOT E D TO VA R I O U S L E G A L TA S K S
  17. R U L E M A K I N G

    R U L E I N T E R P R E TAT I O N R U L E E N F O R C E M E N T T H E S E TA S K S I N C LU D E
  18. O F CO U R S E A S T

    H I S I N VO LV E S H U M A N S T H E S E P R O C E S S E S A R E N OT N E C E S S A R I LY N E U T R A L
  19. A N D TO P D O W N G

    OV E R N M E N TA L R U L E S A R E FA R F R O M T H E O N LY D R I V E R S O F B E H AV I O R
  20. B U T T H E G U I D

    I N G P U R P O S E I S TO H E L P E N G I N E E R C E RTA I N C L A S S E S O F B E H AV I O R
  21. A N D P R E V E N T

    / M I N I M I Z E OT H E R F O R M S O F B E H AV I O R
  22. I H O P E YO U CA N S

    E E A L R E A DY T H AT T H I S I S AC T UA L LY A G R A N D E N G I N E E R I N G C H A L L E N G E
  23. T H I S C H A L L E

    N G E I S O N E TO W H I C H T H O S E B E YO N D T H E T R A D I T I O N A L F I E L D O F L AW
  24. A R E W E L L P O I

    S E D TO CO L L A B O R AT I V E LY CO N T R I B U T E
  25. None
  26. BY WAY OF INTRODUCTION

  27. I AM AN ACADEMIC

  28. I AM A SCIENTIST & TECHNOLOGIST

  29. I AM A SCIENTIST & TECHNOLOGIST

  30. I AM A SCIENTIST & TECHNOLOGIST

  31. I AM A SCIENTIST & TECHNOLOGIST https://legaltechcenter.de/en/index.html

  32. I AM A TEACHER

  33. I AM A TEACHER

  34. I AM A TEACHER

  35. I AM A THOUGHT LEADER

  36. I AM A THOUGHT LEADER

  37. I AM IN THE LEGAL INNOVATION AND VENTURE SPACE AS

    AN ADVISOR
  38. LexPredict.com CONSULTANT & ADVISOR TO THE FORTUNE 1000, AM LAW

    200, LEGAL TECH STARTUPS, ETC.
  39. I AM A SUCCESSFUL ENTREPRENEUR

  40. I MENTION ALL OF THIS ONLY SO THAT YOU MIGHT

    BE ABLE TO ORIENT SOME OF MY COMMENTARY
  41. AND UNDERSTAND THAT I AM *NOT* A MERE SPECTATOR OR

    A COMMENTATOR BUT RATHER AN ACTIVE PARTICIPANT … #SKININTHEGAME
  42. SO TODAY ALLOW ME TO SHARE SOME OF MY NOTES

    FROM THE FIELD
  43. None
  44. PA RT I CO N F R O N T

    I N G T H E CO M P L E X I TY C H A L L E N G E
  45. W H E T H E R YO U A

    R E A L A R G E M U LT I N AT I O N A L CO R P O R AT I O N , A S M A L L B U S I N E S S O R A N I N D I V I D UA L C I T I Z E N …
  46. L AW H A S A CO M P L

    E X I TY C H A L L E N G E …
  47. IF YOU TAKE A STEP BACK

  48. THE REAL ANIMATING DRIVER IN THE DEMAND FOR UNITS OF

    LEGAL PRODUCTION
  49. IS COMPLEXITY

  50. SOCIAL, ECONOMIC AND POLITICAL COMPLEXITY

  51. MANIFESTS ITSELF IN OUR DOMAIN AS LEGAL COMPLEXITY

  52. WHILE IT IS SOMEWHAT HARD TO PRECISELY DEFINE …

  53. None
  54. VIRTUALLY EVERY WAY YOU MIGHT CONSIDER IT LEGAL COMPLEXITY HAS

    GROWN
  55. DANIEL MARTIN KATZ, CORINNA COUPETTE, JANIS BECKEDORF & DIRK HARTUNG,

    COMPLEX SOCIETIES AND THE GROWTH OF THE LAW, 10 SCIENTIFIC REPORTS 18737 (2020) CORINNA COUPETTE, JANIS BECKEDORF, DIRK HARTUNG, MICHAEL BOMMARITO, & DANIEL MARTIN KATZ, MEASURING LAW OVER TIME: A NETWORK ANALYTICAL FRAMEWORK WITH AN APPLICATION TO STATUTES AND REGULATIONS IN THE UNITED STATES AND GERMANY, FRONT. PHYS. (2021 FORTHCOMING)
  56. SIGNIFICANT GROWTH IN LAWS AND REGULATIONS

  57. SO THE QUESTION IS HOW TO MATCH THAT COMPLEXITY WITH

    THE APPROPRIATE
  58. COMPLEXITY MITIGATION TACTICS …

  59. MIXTURES OF PEOPLE, PROCESS AND TECHNOLOGY

  60. DESIGNING MORE SCALABLE SYSTEMS

  61. BECAUSE THE LEGACY MODEL HAS BEEN

  62. TO USE LOTS OF LABOR TO SOLVE HIGH COMPLEXITY PROBLEMS

  63. BUT THAT APPEARS TO BE REACHING ITS PRACTICAL AND ECONOMIC

    LIMITS
  64. THE APPROACH NEEDS TO BE MORE FUNDAMENTAL

  65. AND LEVERAGE THE BEST OF SCIENCE AND TECH

  66. http://prawfsblawg.blogs.com/prawfsblawg/2017/03/-complexity-mitigation-strategies- for-law-law-land-and-beyond-and-some-other-thoughts-on-hadfield-su.html#more SOME THOUGHTS ON LEGAL COMPLEXITY MITIGATION STRATEGIES

  67. None
  68. PA RT I I S I X F L AVO

    R S O F M O D E L I N G
  69. M O D E L S O F VA R

    I O U S F L AVO R S CA N B E S E E N AC R O S S S C I E N C E A N D E N G I N E E R I N G
  70. T H U S , I WA N T TO

    D I S C U S S A S E R I E S O F I D E A S A S S O C I AT E D W I T H
  71. T H E M O D E L I N

    G O F L E G A L SY ST E M S
  72. None
  73. T H E DA K S H C E N

    T R E O F E XC E L L E N C E F O R L AW A N D T E C H N O LO GY AT I I T- D E L H I ( D CO E ) WA S E STA B L I S H E D I N O C TO B E R 2 0 2 0 A S A CO L L A B O R AT I O N B E T W E E N I N D I A N I N ST I T U T E O F T E C H N O LO GY ( I I T ) D E L H I A N D DA K S H S O C I E TY, B E N G A LU R U H I G H L I G H T S F R O M A R E C E N T CA L L F O R P R OJ E C T S A . M O D E L I N G T H E J U ST I C E SY ST E M B . TO O L TO M A K E L AW S M O R E ACC E S S I B L E TO C I T I Z E N S , A N D CA S E D U R AT I O N & O U TCO M E P R E D I C TO R C . CA S E F LO W M A N AG E M E N T D. S C I E N T I F I C S C H E D U L I N G O F CA S E S & WO R K A L LO CAT I O N F O R J U D G E S
  74. A . M O D E L I N G

    T H E J U ST I C E SY ST E M B . TO O L TO M A K E L AW S M O R E ACC E S S I B L E TO C I T I Z E N S , A N D CA S E D U R AT I O N & O U TCO M E P R E D I C TO R C . CA S E F LO W M A N AG E M E N T D. S C I E N T I F I C S C H E D U L I N G O F CA S E S & WO R K A L LO CAT I O N F O R J U D G E S A L L F O U R O F T H E S E AC T UA L LY I N VO LV E M O D E L S
  75. None
  76. S O W H AT I S M O D

    E L I N G ?
  77. T H E T E R M TA K E

    S O N D I F F E R E N T M E A N I N G S AC R O S S T H E S C I E N C E S
  78. H E R E A R E S O M

    E E L E M E N T S
  79. A M O D E L I S A R

    E P R E S E N TAT I O N O F A SY ST E M L I K E LY I N VO LV E S A L E V E L O F A B ST R AC T I O N CA N B E U S E D TO U N D E R STA N D B OT H SY ST E M CO M P O N E N T S A N D T H E I R CO N N E C T I O N TO O N E A N OT H E R T H E R E A R E VA R I O U S A P P R OAC H E S O N E CA N U S E
  80. None
  81. None
  82. AT L E A ST S I X D I

    F F E R E N T F O R M S O F “ M O D E L I N G ” A R E R E L E VA N T F O R L E G A L SY ST E M S
  83. R E M E M B E R E AC

    H O F T H E S E H A S A N ACCO M PA N Y I N G M E N TA L M O D E L . .
  84. B U T L E T U S STA RT

    AT T H E M AC R O S CA L E M O D E L S A N D WO R K TO WA R D T H E M I C R O S CA L E M I C R O M E S O M AC R O
  85. None
  86. M O D E L I N G TY P

    E 1 M O D E L I N G T H E L AW I T S E L F ( AG G R E G AT E L E V E L )
  87. I H AV E A L R E A DY

    G I V E N A B I T O F A P R E V I E W O F T H I S A P P R OAC H
  88. TA K E T H E L E G A

    L R U L E S T H E M S E LV E S A N D M O D E L T H E M
  89. B OT H STAT I CA L LY A N

    D A S A F U N C T I O N O F T I M E
  90. L AW S CA N O F T E N

    B E M O D E L E D A S CO M B I N AT I O N A H I E R A R C H Y, R E F E R E N C E N E T WO R K , S E Q U E N C E G R A P H
  91. None
  92. H E R E A R E S O M

    E E X A M P L E S …
  93. Boulet, Romain, Pierre Mazzega, and Daniele Bourcier. "A network approach

    to the French system of legal codes—part I: analysis of a dense network." Artificial Intelligence and Law 19.4 (2011): 333-355. Boulet, Romain, Pierre Mazzega, and Danièle Bourcier. "Network approach to the French system of legal codes part II: the role of the weights in a network." Artificial Intelligence and Law 26.1 (2018): 23-47.
  94. None
  95. Koniaris, Marios, Ioannis Anagnostopoulos, and Yannis Vassiliou. "Network Analysis in

    the Legal Domain: A complex model for European Union legal sources." Journal of Complex Networks 6.2 (2017): 243-268.
  96. M. Bommarito. & D. Katz. A Mathematical Approach to the

    Study of the United States Code. Physica A: Statistical Mechanics and its Applications, 389(19), 4195-4200 (2010). D. Katz & M. Bommarito. Measuring the complexity of the law: the United States Code. Artificial intelligence and law, 22(4), 337-374. (2014)
  97. H E R E W E I N V E

    ST I G AT E D A S I N G L E S N A P S H OT O F T H E 2 0 1 0 U N I T E D STAT E S CO D E I N A N E F F O RT TO E X P LO R E I T S CO M P L E X I TY
  98. THEN WE STUDIED THE STATUTORY LAW OF TWO LARGE COUNTRIES

    OVER 25 YEARS DANIEL MARTIN KATZ, CORINNA COUPETTE, JANIS BECKEDORF & DIRK HARTUNG, COMPLEX SOCIETIES AND THE GROWTH OF THE LAW, 10 SCIENTIFIC REPORTS 18737 (2020)
  99. LEVERAGED INFO MAP (MAP EQUATION) TO ALGORITHMICALLY REORGANIZE THE STATUTORY

    LAW INTO MORE COHERENT CATEGORIES (TOPICS)
  100. MAP EQUATION MAP EQUATION SOURCE ROSVALL, M., AXELSSON, D. &

    BERGSTROM, C. THE MAP EQUATION. EUR. PHYS. J. SPEC. TOP. 178, 13–23 (2009).
  101. RESULTING CLUSTER QUOTIENT GRAPHS

  102. TRACE THE CLUSTER QUOTIENT GRAPHS OVER TIME

  103. THEN WE ADDED ALL OF THE FEDERAL LEVEL REGULATIONS TO

    THE STATUTES CORINNA COUPETTE, JANIS BECKEDORF, DIRK HARTUNG, MICHAEL BOMMARITO, & DANIEL MARTIN KATZ, MEASURING LAW OVER TIME: A NETWORK ANALYTICAL FRAMEWORK WITH AN APPLICATION TO STATUTES AND REGULATIONS IN THE UNITED STATES AND GERMANY, FRONT. PHYS. (2021 FORTHCOMING)
  104. None
  105. None
  106. OF COURSE THERE ARE EVEN LARGER SCALES (WE ARE ONLY

    COVERING THE RED TILES)
  107. T H I S F L AVO R O F

    M O D E L I N G I N VO LV E S CO M B I N AT I O N S O F I N F O R M AT I O N T H E O R Y, N E T WO R K S C I E N C E A N D N AT U R A L L A N G UAG E P R O C E S S I N G
  108. None
  109. M O D E L I N G TY P

    E 2 M E A S U R E SY ST E M L E V E L O U T P U T S
  110. H O W WO U L D O N E

    E VA LUAT E T H E Q UA L I TY O F A L E G A L SY ST E M I N A G I V E N CO U N T RY ?
  111. CA N W E CO M PA R E L

    E G A L SY ST E M S B A S E D U P O N C E RTA I N M E A S U R E S O F Q UA L I TY ?
  112. H O W M I G H T W E

    CO M B I N E A N I N D I V I D UA L M E A S U R E O F Q UA L I TY I N TO A CO M P O S I T E M E A S U R E ?
  113. I T I S T H E S E A

    N D OT H E R R E L AT E D Q U E ST I O N S
  114. W H I C H N G O ’ S

    , R E S E A R C H E R S A N D OT H E R S S E E K TO R E G U L A R LY A N S W E R
  115. I T T U R N S O U T

    T H AT T H E S E Q U E ST I O N S A R E N OT A LWAY S E A SY TO A N S W E R
  116. None
  117. T H E R E A R E LOT S

    O F M E A S U R E S W H I C H AT T E M P T TO B R OA D LY C H A R AC T E R I Z E F I D E L I TY TO CO N C E P T S S U C H A S ‘ R U L E O F L AW ’
  118. B U T T H E G OA L I

    S TO M OV E F R O M “ I K N O W I T W H E N I S E E I T ”
  119. TO M O R E O B J E C

    T I V E A N D G E N E R A L I Z E D M E A S U R E S
  120. AG A I N M U C H E A

    S I E R S A I D T H A N D O N E
  121. None
  122. H E R E A R E A F E

    W E X A M P L E S O F PA P E R S A N D P R OJ E C T S O N T H E S U B J E C T
  123. 1) SUPPORT OF JUSTICE INSTITUTIONS THROUGH TARGETED INTERVENTIONS THAT IMPROVE

    THE SPECIALIST FUNCTIONS OF THE JUSTICE SYSTEM 2) LEGAL EMPOWERMENT, THROUGH THE PROTECTION AND PROACTIVE OUTREACH TO WOMEN, THE POOR AND MARGINALIZED GROUPS TO UNDERSTAND AND NAVIGATE THEIR LEGAL PROBLEMS 3) JUSTICE IN SECTORS THROUGH THE STRENGTHENING OF THE REGULATORY FRAMEWORKS AND INSTITUTIONS OF ALL THOSE SECTORS CRITICAL FOR THE ACHIEVEMENT OF BROADER DEVELOPMENT OBJECTIVES 4) DEVELOPMENT OF ANALYTICS AND DIAGNOSTICS TO INFORM POLICY, PROMOTE DIALOGUE AMONG STAKEHOLDERS AND BETTER TARGET REFORMS. WORLD BANK EXPERTISE IN JUSTICE AND DEVELOPMENT THE WORLD BANK HAS WORKED ON JUSTICE AND DEVELOPMENT AROUND THE GLOBE FOR MORE THAN 25 YEARS THROUGH MORE THAN 800 PROJECTS.
  124. http://info.worldbank.org/governance/wgi/#doc-methodology

  125. https://worldjusticeproject.org/

  126. None
  127. None
  128. M O D E L I N G TY P

    E 3 E M P I R I CA L E VA LUAT I O N O F L E G A L R U L E S / P O L I CY M A K I N G
  129. A S A N I N T E L L

    E C T UA L F I E L D
  130. L AW FAC E D A C R E D

    I B I L I TY R E VO LU T I O N
  131. W H E R E B Y L AW Y

    E R S J U D G E S P O L I CY M A K E R S
  132. W E R E C R A F T I

    N G L E G A L R U L E S A N D D O C T R I N E S
  133. A N D C R I T I Q U

    I N G L E G A L R U L E S A N D D O C T R I N E S
  134. W I T H O U T A N Y

    E M P I R I CA L F O C U S O N T H E R E A L WO R L D P E R F O R M A N C E O F T H E S E R U L E S A N D D O C T R I N E S
  135. R O U G H LY ~ 2 5 Y

    E A R S AG O T H E R E B E G A N TO B E ‘ E M P I R I CA L T U R N ’ I N L E G A L S C H O L A R S H I P
  136. Legal Scholarship Has become far more ‘empirical’ in nature

  137. None
  138. None
  139. None
  140. D E T E R M I N E (

    A S B E ST P O S S I B L E ) W H E T H E R A PA RT I C U L A R P O L I CY I N T E RV E N T I O N AC H I E V E S T H E D E S I R E D E N D S TASK
  141. MODELING APPROACH CO U L D E M P LOY

    T R A D I T I O N A L Q UA N T I TAT I V E S O C I A L S C I E N C E M E T H O D S ( S U C H A S R E G R E S S I O N )
  142. MODELING APPROACH O R M I G H T I

    N VO K E M O R E S O P H I ST I CAT E D E CO N O M E T R I C M E T H O D S
  143. EXAMPLE METHODS I N ST R U M E N

    TA L VA R I A B L E S , P R O P E N S I TY S CO R E M ATC H I N G , R U B I N CAU S A L M O D E L , R E G R E S S I O N D I S CO N T I N U I TY, D I F F E R E N C E I N D I F F E R E N C E S , E TC .
  144. S I N C E M O ST P O

    L I CY E VA LUAT I O N CA N N OT R E A L LY B E U N D E RTA K E N U N D E R R A N D O M I Z E D CO N T R O L T R I A L ( R C T ) TY P E CO N D I T I O N S
  145. F O L K S O F T E N

    L E V E R AG E M E T H O D S TO T R Y TO G E T SY N T H E T I C V E R S I O N S O F R C T ’ S
  146. CAU S A L I N F E R E

    N C E A P P R OAC H E S A R E B E ST F O R A P P R O P R I AT E P R O B L E M S / Q U E ST I O N S W H E R E I D E N T I F Y I N G / L I N K I N G CAU S E A N D E F F E C T A R E K E Y
  147. None
  148. O N S S R N A LO N E

    , T H E R E A R E T H O U S A N D S O F E M P I R I CA L L E G A L ST U D I E S TY P E PA P E R S
  149. https://papers.ssrn.com/sol3/JELJOUR_Results.cfm? form_name=journalBrowse&journal_id=2123644

  150. H E R E A R E J U ST

    A F E W E X A M P L E O F PA P E R S L E V E R AG I N G VA R I O U S E M P I R I CA L M E T H O D S
  151. E VA LUAT I N G T H E I

    M PAC T S O F C H A N G E S I N C I V I L P L E A D I N G R U L E S
  152. E X P LO R I N G T H

    E P E R F O R M A N C E O F B A N K R U P TCY R E F O R M F O R CO N S U M E R D E B TO R S
  153. E VA LUAT I N G T H E R

    O L E O F E X P U N G E M E N T O N T H E O U TCO M E S O F CO N V I C T E D C R I M I N A L S
  154. R E V I E W I N G W

    H E T H E R C H A N G E S I N PAT E N T L AW D O C T R I N E H AV E WO R K E D TO U N D E R M I N E PAT E N T T R O L L S
  155. Epstein, L., & Martin, A. D. (2014). An introduction to

    empirical legal research. Oxford University Press. Lawless, Robert M., Jennifer K. Robbennolt, and Thomas Ulen. Empirical methods in law. New York: Aspen Publishers, 2010.
  156. None
  157. M O D E L I N G TY P

    E 4 S I M U L AT I O N B A S E D A N A LY S I S O F L E G A L R U L E S / P O L I CY M A K I N G
  158. E M P I R I CA L M O

    D E L S / M E T H O D S A R E TY P I CA L LY U S E D TO A N A LY Z E P E R F O R M A N C E O F P O L I C I E S W H I C H A R E A L R E A DY D E P LOY E D
  159. S O M E T I M E S A

    P O L I CY M A K E R WO U L D L I K E TO U N D E R STA N D I N A DVA N C E T H E L I K E LY I M PAC T S
  160. T H E A LT E R N AT I

    V E TO P O ST H O C E M P I R I CA L E VA LUAT I O N I S TO CO N ST R U C T U P F R O N T G A M E T H E O R E T I C O R S I M U L AT I O N B A S E D M O D E L S
  161. T H E R E A R E A VA

    R I E TY O F A P P R OAC H E S TO B U I L D F O R M A L M O D E L S
  162. G A M E T H E O R E

    T I C M O D E L S AG E N T B A S E D M O D E L S SY ST E M DY N A M I C S M O D E L S
  163. R E M E M B E R T H

    E R E I S A N U N CA N N Y VA L L E Y I N T H E O R E T I CA L M O D E L S
  164. Hyper Stylized Fairly Realistic T H E U N CA

    N N Y VA L L E Y I N T H E O R E T I C M O D E L S Conway’s Game of Life The Sims Video Game
  165. B E WA R E F O L K S

    C LO B B E R YO U I N T H E VA L L E Y B E CAU S E O F S O M E D E TA I L T H AT I S ‘ M I S S I N G ’ F R O M T H E M O D E L
  166. None
  167. H E R E A R E A F E

    W E X A M P L E S O F PA P E R S / P R OJ E C T S L E V E R AG I N G D I F F E R E N T M E T H O D S
  168. T WO O L D E R PA P E

    R S W H I C H H AV E S PAW N E D M A N Y F O L LO W O N A N A LY S E S
  169. T H E A RT I C L E U

    T I L I Z E S A N E W G A M E - T H E O R E T I C F R A M E WO R K , F O C U S I N G O N T WO C E N T R A L D I M E N S I O N S O F P O L I CY- M A K I N G I N ST R U M E N T S I N PA RT I C U L A R : T I M I N G A N D B R E A DT H .
  170. T H E M O D E L U T

    I L I Z E S R U L E S F O R TA X PAY E R B E H AV I O R A N D A P P R E H E N S I O N O F TA X E VA D E R S I N O R D E R TO T E ST T H E E F F E C T S O F N E T WO R K TO P O LO G I E S I N T H E P R O PAG AT I O N O F E VA S I V E B E H AV I O R
  171. S I M U L AT I O N O

    F G OV E R N A N C E A N D E N F O R C E M E N T O F F O R E ST S E X T R AC T I O N L I M I T S W I T H L E S S O N S F O R OT H E R CO M M O N P O O L R E S O U R C E G OV E R N A N C E P R O B L E M S
  172. U S E S B E L I E F

    L E A R N I N G TO M O D E L T H E DY N A M I C S O F R AT ’ S O F F E N D E R , TA R G E T, A N D G UA R D I A N B E H AV I O R S W I T H I N A N AG E N T- B A S E D M O D E L I S T H AT T H E AG E N T S L E A R N A N D A DA P T G I V E N O B S E RVAT I O N O F OT H E R AG E N T S ’ AC T I O N S W I T H O U T K N O W L E D G E O F T H E PAYO F F S T H AT D R OV E T H E OT H E R AG E N T S ’ C H O I C E S .
  173. None
  174. None
  175. M O D E L I N G TY P

    E 5 M O D E L U S E R E X P E R I E N C E W I T H A L E G A L P R O C E S S
  176. L AW H A S M A N Y P

    R O C E S S E S A N D P R O C E D U R E S
  177. I N D E E D W E T E

    AC H E N T I R E C L A S S E S O N L E G A L P R O C E S S E S A N D P R O C E D U R E S
  178. T H E R E A R E OT H

    E R WAY S TO C H A R AC T E R I Z E P R O C E S S E S A N D P R O C E D U R E S
  179. None
  180. R E L E VA N T I N T

    E L L E C T UA L D I S C I P L I N E S
  181. P R O C E S S E N G

    I N E E R I N G O P E R AT I O N S R E S E A R C H D E S I G N T H I N K I N G H U M A N CO M P U T E R I N T E R AC T I O N ( H C I )
  182. None
  183. T H E D E V E LO P M

    E N T A N D / O R D E P LOY M E N T O F S O F T WA R E
  184. I T I S B A S E D U

    P O N M O D E L S
  185. CA S E M G M T M AT T

    E R M G M T R E Q U I R E S A ST E P B Y ST E P U N D E R STA N D I N G O F P R O C E S S
  186. None
  187. I T CA N A L S O S U

    P P O RT T H E ‘ R E E N G I N E E R I N G ’ O F T H AT P R O C E S S
  188. I T I S CA N A L S O

    P R OV I D E T H E F O U N DAT I O N A L DATA TO S U P P O RT OT H E R TY P E O F M O D E L I N G E F F O RT S
  189. None
  190. THERE ARE KNOWN METHODOLOGIES FOR MANAGING PROCESS CENTRIC WORK PROJECT

    MGMT PROCESS IMPROVEMENT
  191. None
  192. INDEED, ACROSS THE ECONOMY THERE ARE MANY EFFORTS TO CONVERT

    AN ARTISANAL PROCESS INTO AN INDUSTRIAL PROCESS
  193. THE INDUSTRIALIZATION OF THE ARTISAN

  194. THE INTELLECTUAL ORIGINS CAN BE TRACED TO A MUCH EARLIER

    TIME
  195. THE INTELLECTUAL ORIGINS CAN BE TRACED TO A MUCH EARLIER

    TIME
  196. the toyota production system lean ideas lean for enterprises (white

    collar, etc.)
  197. WE WANT TO ENSURE THAT WE CAN MAINTAIN THE ARTISAN

    ELEMENTS THAT DO ADD VALUE
  198. WITH RESPECT TO A GIVEN PROCESS THERE IS OFTEN A

    SIGNIFICANT SPREAD
  199. LEAN, SIX SIGMA AND OTHER PROCESS IMPROVEMENT METHODOLOGIES

  200. CAN HELP IMPROVE ALMOST EVERY SUBSECTOR IN LAW

  201. CONVERT HIGH VOLATILITY PROCESS

  202. CONVERT HIGH VOLATILITY PROCESS INTO A LOWER VOLATILITY PROCESS

  203. WHAT YOU ARE LEFT WITH IS A VERY USEFUL ARTIFACT

  204. Responsible Role Task Description Estimated Time Billing Code Precedential Document

    Tool/ Technology Used Participant INDIVIDUAL PROCESS STEP
  205. THE PROCESS MAP

  206. IT CAN AID IN Increasing Response Times Predicting Resource Loads

    Coordination Across Stakeholders Increasing Margins on Work
  207. I TEACH A FULL LENGTH CLASS AT ILLINOIS TECH LED

    BY OUR COLLEAGUES FROM THE LAW FIRM SEYFARTH SHAW
  208. None
  209. None
  210. SO THINK OF THE PROCESS MAP AS A FIRST ORDER

    ESTIMATION OF YOUR ACTUAL PROCESSES
  211. RICH / GRANULAR DATA CAN HELP ILLUMINATE THE ACTUAL PROCESSES

    PRESENT IN VARIOUS (LEGAL) ORGANIZATIONS
  212. FOR EACH NODE IN A PROCESS WE WANT TO BE

    ABLE TO RENDER A PREDICTION ABOUT THINGS SUCH AS DURATION, COST, ETC.
  213. COURTS CAN PROVIDE SCIENTIFIC SCHEDULE OF CASES, ETC.

  214. EACH UNIT OF TIME LINKED + LOGGED TO A NODE

    ON THE PROCESS MAP
  215. LEAN PROCESS MAPPING

  216. IF THERE IS NOT A NODE THEN IT CAN BE

    ADDED AND THUS THE MAP BECOMES MORE REFLECTIVE OF REALITY
  217. JUST BE CAREFUL NOT TO CREATE A #RIDICULOGRAM

  218. WITH PREDICTIONS ABOUT INDIVIDUAL NODES

  219. WE CAN THEN SUM TO GENERATE PREDICTIONS ABOUT THE DISTRIBUTIONAL

    MOMENTS OF AN OVERALL MATTER (OR PHASE) (i.e. mean, variance, skewness, kurtosis)
  220. THIS MATTER SHOULD TAKE … BETWEEN 9-15 MONTHS IN 85%

    OF THE SIMILAR MATTERS (WHAT ABOUT THE LONG TAIL?)
  221. THIS MATTER WILL COST… MOST COMMON RANGE 275K - 345K

    BUT THE SECOND MODE IS 555K - 625K (AND THAT SECOND MODE TYPICALLY IS REALIZED WHEN THE FOLLOWING FACTORS ARE PRESENT … )
  222. SPEAKING OF #PREDICTION

  223. None
  224. M O D E L I N G TY P

    E 6 P R E D I C T I V E M O D E L I N G O F L E G A L SY ST E M S
  225. H O W LO N G W I L L

    T H I S M AT T E R L A ST ?
  226. H O W M U C H W I L

    L T H I S M AT T E R CO ST ?
  227. W I L L W E W I N ?

  228. WHAT ARE THE RIGHT RESOURCES TO USE AT THE MATTER

    AND TASK LEVEL ?
  229. T H E R E A R E A W

    I D E VA R I E TY O F P R E D I C T I O N P R O B L E M S W I T H I N L AW
  230. T H E R E A R E T WO

    M A J O R CA N D I DAT E S F O R P R O C E S S I N G P R E D I C T I O N S and / or
  231. CO G N I T I V E S C

    I E N C E CO M P U T E R S C I E N C E
  232. https://speakerdeck.com/danielkatz/the-three-forms-of-legal-prediction-experts-crowds-plus-algorithms I E X PA N D O N T

    H I S OV E R H E R E
  233. None
  234. CO M P U TAT I O N A L

    P R E D I C T I O N W I L L L E A D TO T WO PA RT I C U L A R FA M I L I E S O F M O D E L I N G M E T H O D S
  235. P R E D I C T I O N

    MACHINE LEARNING NATURAL LANGUAGE PROCESSING A N D / O R =
  236. T H E S E M O D E L

    I N G M E T H O D S A R E L E V E R AG E D I N U S E CA S E S S U C H A S T H E F O L LO W I N G
  237. #Predict Substantive Case Outcomes #Predict Rogue Behavior/Compliance #Predict Contract Terms/Outcomes

    #Predict Case Scheduling #Predict Judge Work Allocation #Predict Relevant Legal Information #Predict Costs / Duration for Litigants
  238. None
  239. A N D F R O M A G LO

    B A L P E R S P E C T I V E
  240. T H E R E H A S B E

    E N S I G N I F I CA N T S C I E N T I F I C A N D CO M M E R C I A L I N T E R E ST
  241. I N B U I L D I N G

    A N D L E V E R AG I N G M O D E L S
  242. #JudicialAnalytics Quantitative Legal Prediction

  243. #PredictiveCoding #E-Discovery Quantitative Legal Prediction

  244. General Counsels as Legal Procurement Specialists #LegalSpendAnalytics Quantitative Legal Prediction

    TyMetrix/ELM - Using $80 billion+ in Legal Spend Data to Help GC’s Look for Arbitrage Opportunities, Value Propositions in Hiring Law Firms
  245. “Lawyers say the real value in mediation and arbitration might

    in the future come from large-scale data analysis of arbitrators and mediators themselves, in an effort to predict outcomes and potentially affect the course of settlements … Matthew Saunders, partner at Ashurst, notes that data analytics “could be extended to predicting which way arbitrators or a mediator might go”. #ArbitrationAnalytics
  246. None
  247. None
  248. None
  249. None
  250. “…I study choice of law by analyzing the nearly 1,000,000

    contracts that have been disclosed to the Securities and Exchange Commission between 1996–2012.”
  251. None
  252. None
  253. None
  254. None
  255. None
  256. None
  257. http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0174698 Katz DM, Bommarito MJ II, Blackman J (2017), A

    General Approach for Predicting the Behavior of the Supreme Court of the United States. PLoS ONE 12(4): e0174698.
  258. H E R E A R E A F E

    W E X A M P L E S I N T H E CO N T E X T O F CO U RT A D M I N I ST R AT I O N
  259. https://motoraccidents.lawnet.sg/

  260. https://civilresolutionbc.ca/

  261. https://www.chinadaily.com.cn/a/201901/24/WS5c4959f9a3106c65c34e64ea.html The 206 System Transcribe testimony Transfer physical data and

    documents to electronic databases Display relevant parameters immediately, such as time, place, people, behavior and consequences Identify defective or contradicting evidence Respond to oral commands to display evidence and information on screens around the courtroom Inter-connect with judicial, procuratorial, public security authorities and courts
  262. B U T T H E R E I S

    M U C H M O R E T H AT CA N B E D O N E …
  263. A N D T H I S I S W

    H E R E YO U CO M E I N …
  264. None
  265. PA RT I I I L AW M E E

    T S ST E M
  266. I H AV E LO N G B E L

    I E V E D T H AT A L AW S C H O O L A N D A T E C H N I CA L S C H O O L S H O U L D M E E T
  267. LIBERAL ARTS LEGAL.EDU

  268. POLYTECHNIC LEGAL.EDU LIBERAL ARTS LEGAL.EDU

  269. Daniel Martin Katz, The MIT School of Law? A Perspective

    on Legal Education in the 21st Century, University of Illinois Law Review (2014)
  270. I OUTLINE IN THIS PAPER MY VISION FOR WHAT LAW.EDU

    COULD BE ... (AND I AM NOT ALONE IN THIS VIEW)
  271. THERE QUITE A FEW LAW SCHOOLS WITH SOME CREDIBLE VERSION

    OF A POLYTECHNIC OFFERING (SOME ARE MORE SERIOUS THAN OTHERS)
  272. None
  273. THE DEMOGRAPHIC REALITY …

  274. WE ARE ABOUT TO HAVE THE LARGEST GENERATIONAL TRANSFER IN

    HUMAN HISTORY
  275. THIS IS A MAJOR CHALLENGE TO EVERY ORGANIZATION

  276. None
  277. ORGANIZATIONS NEED DIFFERENT (BETTER) HUMAN CAPITAL IN ORDER TO SUPPORT

    ALL OF THESE TRENDS HIGHLIGHTED HEREIN
  278. INCLUDING HELPING SOLVE THE COMPLEXITY CHALLENGE IN LAW

  279. WE NEED MORE MULTI DISCIPLINARY INDIVIDUALS

  280. WE NEED MORE MULTI DISCIPLINARY TEAMS

  281. AND THIS SHOULD BE REFLECTED IN THE CONTENT OF LEGAL

    EDUCATION
  282. T Shaped Professionals

  283. via

  284. None
  285. I N T H I S P R E S

    E N TAT I O N
  286. I H AV E B R I E F LY

    H I G H L I G H T E D AT L E A ST S I X F O R M S O F M O D E L I N G T H AT M I G H T B E L E V E R AG E D
  287. TO B E T T E R U N D

    E R STA N D A N D H O P E F U L LY I M P R OV E T H E D E L I V E R Y O F J U ST I C E
  288. I H AV E I D E N T I

    F I E D VA R I O U S M E T H O D S A N D I N T E L L E C T UA L F I E L D S W H O S E WO R K M I G H T A L S O B E L E V E R AG E D
  289. M U C H M O R E CO U

    L D B E S A I D I N A LO N G E R F O R M S E S S I O N …
  290. B U T I T H I N K T

    H I S H E L P S M A P T H E I N T E L L E C T UA L T E R R A I N ( AT L E A ST I N PA RT )
  291. A N D H O P E F U L

    LY S P U R S A S E R I E S O F F R U I T F U L CO L L A B O R AT I O N S
  292. AT T H E I N T E R S

    E C T I O N O F L AW + ST E M
  293. None
  294. T H A N K YO U I I T

    D E L H I F O R H O ST I N G M E TO DAY N LU D E L H I
  295. T H A N K YO U DA K S

    H S O C I E TY F O R R E AC H I N G O U T TO I N V I T E M E F O R T H E TA L K
  296. T H A N K YO U DA K S

    H C E N T R E O F E XC E L L E N C E AT I I T- D E L H I F O R H AV I N G M E A S A M E N TO R TO T H E C E N T E R
  297. S O M E T H O U G H

    T S O N M O D E L I N G L E G A L SY ST E M S @computational professor daniel martin katz danielmartinkatz.com Illinois tech - chicago kent law computationallegalstudies.com https://bit.ly/3yxhY2e