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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

Daniel Martin Katz

May 27, 2021
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  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

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  2. 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 …

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  3. 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 …

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  4. L AW I S
    E N G I N E E R I N G

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  5. 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

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  6. 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

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  7. 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

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  8. 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

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  9. 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

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  10. 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

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  11. 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 ?

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  12. 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

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  13. 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

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  14. 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

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  15. 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

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  16. 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

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  17. 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

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  18. 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

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  19. 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

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  20. 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

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  21. 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

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  22. BY WAY OF
    INTRODUCTION

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  23. I AM AN
    ACADEMIC

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  24. I AM A SCIENTIST & TECHNOLOGIST

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  25. I AM A SCIENTIST & TECHNOLOGIST

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  26. I AM A SCIENTIST & TECHNOLOGIST

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

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  28. I AM A
    TEACHER

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  29. I AM A
    TEACHER

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  30. I AM A
    TEACHER

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  31. I AM A
    THOUGHT
    LEADER

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  32. I AM A
    THOUGHT
    LEADER

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  33. I AM IN THE
    LEGAL
    INNOVATION
    AND VENTURE
    SPACE AS AN
    ADVISOR

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  34. LexPredict.com
    CONSULTANT
    & ADVISOR
    TO THE
    FORTUNE
    1000,
    AM LAW 200,
    LEGAL TECH
    STARTUPS,
    ETC.

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  35. I AM A SUCCESSFUL
    ENTREPRENEUR

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  36. I MENTION ALL OF
    THIS ONLY SO THAT
    YOU MIGHT BE ABLE
    TO ORIENT SOME OF
    MY COMMENTARY

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  37. AND UNDERSTAND
    THAT I AM *NOT* A
    MERE SPECTATOR OR
    A COMMENTATOR BUT
    RATHER AN ACTIVE
    PARTICIPANT …
    #SKININTHEGAME

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  38. SO TODAY ALLOW ME
    TO SHARE SOME OF MY
    NOTES FROM THE FIELD

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  39. 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

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  40. 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 …

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  41. L AW H A S A
    CO M P L E X I TY
    C H A L L E N G E …

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  42. IF YOU TAKE A STEP BACK

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  43. THE REAL ANIMATING
    DRIVER IN THE DEMAND
    FOR UNITS OF LEGAL
    PRODUCTION

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  44. IS COMPLEXITY

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  45. SOCIAL, ECONOMIC AND
    POLITICAL COMPLEXITY

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  46. MANIFESTS ITSELF IN
    OUR DOMAIN AS
    LEGAL COMPLEXITY

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  47. WHILE IT IS
    SOMEWHAT HARD TO
    PRECISELY DEFINE …

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  48. VIRTUALLY EVERY WAY
    YOU MIGHT CONSIDER IT
    LEGAL COMPLEXITY
    HAS GROWN

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  49. 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)

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  50. SIGNIFICANT GROWTH IN LAWS AND REGULATIONS

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  51. SO THE QUESTION IS
    HOW TO MATCH THAT
    COMPLEXITY WITH THE
    APPROPRIATE

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  52. COMPLEXITY
    MITIGATION
    TACTICS …

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  53. MIXTURES OF PEOPLE,
    PROCESS AND
    TECHNOLOGY

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  54. DESIGNING MORE
    SCALABLE SYSTEMS

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  55. BECAUSE THE LEGACY
    MODEL HAS BEEN

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

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  57. BUT THAT APPEARS
    TO BE REACHING
    ITS PRACTICAL
    AND ECONOMIC
    LIMITS

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  58. THE APPROACH NEEDS
    TO BE MORE
    FUNDAMENTAL

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  59. AND LEVERAGE THE BEST
    OF SCIENCE AND TECH

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  60. 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

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  61. PA RT I I
    S I X F L AVO R S
    O F M O D E L I N G

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  62. 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

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  63. 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

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  64. T H E M O D E L I N G O F
    L E G A L SY ST E M S

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  65. 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

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  66. 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

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  67. S O W H AT I S
    M O D E L I N G ?

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  68. 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

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  69. H E R E A R E S O M E
    E L E M E N T S

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  70. 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

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  71. 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

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  72. 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 . .

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  73. 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

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  74. 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 )

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  75. 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

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  76. 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

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  77. 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

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  78. 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

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  79. H E R E A R E S O M E
    E X A M P L E S …

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  80. 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.

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  81. 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.

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  82. 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)

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  83. 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

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  84. 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)

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  85. LEVERAGED INFO MAP
    (MAP EQUATION)
    TO ALGORITHMICALLY
    REORGANIZE THE
    STATUTORY LAW INTO
    MORE COHERENT
    CATEGORIES (TOPICS)

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  86. MAP EQUATION
    MAP EQUATION SOURCE
    ROSVALL, M., AXELSSON, D. & BERGSTROM, C. THE MAP
    EQUATION. EUR. PHYS. J. SPEC. TOP. 178, 13–23 (2009).

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  87. RESULTING CLUSTER
    QUOTIENT GRAPHS

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  88. TRACE THE
    CLUSTER
    QUOTIENT
    GRAPHS
    OVER TIME

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  89. 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)

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  90. OF
    COURSE
    THERE ARE
    EVEN
    LARGER
    SCALES
    (WE ARE ONLY
    COVERING THE
    RED TILES)

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  91. 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

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  92. 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

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  93. 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 ?

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  94. 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 ?

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  95. 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 ?

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  96. 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

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  97. 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

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  98. 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

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  99. 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 ’

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  100. 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 ”

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  101. 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

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  102. 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

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  103. 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

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  104. 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.

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  105. http://info.worldbank.org/governance/wgi/#doc-methodology

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  106. https://worldjusticeproject.org/

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  107. 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

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  108. A S A N
    I N T E L L E C T UA L
    F I E L D

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  109. L AW FAC E D A
    C R E D I B I L I TY
    R E VO LU T I O N

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  110. 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

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  111. 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

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  112. 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

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  113. 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

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  114. 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

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  115. Legal Scholarship
    Has become far more ‘empirical’ in nature

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  116. 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

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  117. 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 )

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  118. 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

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  119. 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 .

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  120. 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

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  121. 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

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  122. 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

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  123. 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

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  124. https://papers.ssrn.com/sol3/JELJOUR_Results.cfm?
    form_name=journalBrowse&journal_id=2123644

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  125. 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

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  126. 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

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  127. 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

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  128. 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

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  129. 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

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  130. 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.

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  131. 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

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  132. 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

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  133. 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

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  134. 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

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  135. 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

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  136. 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

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  137. 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

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  138. 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

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  139. 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

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  140. 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

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  141. 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

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  142. 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 .

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  143. 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

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  144. 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

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  145. 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 .

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  146. 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

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  147. 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

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  148. 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

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  149. 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

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  150. 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

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  151. 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 )

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  152. 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

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  153. I T I S B A S E D U P O N
    M O D E L S

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  154. 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

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  155. 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

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  156. 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

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  157. THERE ARE KNOWN
    METHODOLOGIES FOR
    MANAGING PROCESS
    CENTRIC WORK
    PROJECT MGMT
    PROCESS IMPROVEMENT

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  158. INDEED, ACROSS THE
    ECONOMY THERE ARE
    MANY EFFORTS TO
    CONVERT AN ARTISANAL
    PROCESS INTO AN
    INDUSTRIAL PROCESS

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  159. THE
    INDUSTRIALIZATION
    OF THE
    ARTISAN

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

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

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  162. the toyota
    production system
    lean ideas
    lean for
    enterprises
    (white collar, etc.)

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  163. WE WANT TO ENSURE
    THAT WE CAN MAINTAIN
    THE ARTISAN ELEMENTS
    THAT DO ADD VALUE

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  164. WITH RESPECT TO A GIVEN
    PROCESS THERE IS OFTEN A
    SIGNIFICANT SPREAD

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  165. LEAN, SIX SIGMA
    AND OTHER
    PROCESS IMPROVEMENT
    METHODOLOGIES

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  166. CAN HELP IMPROVE
    ALMOST EVERY
    SUBSECTOR IN LAW

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  167. CONVERT HIGH VOLATILITY PROCESS

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  168. CONVERT HIGH VOLATILITY PROCESS
    INTO A LOWER VOLATILITY PROCESS

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  169. WHAT YOU ARE
    LEFT WITH IS A
    VERY USEFUL
    ARTIFACT …

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  170. Responsible
    Role
    Task
    Description
    Estimated
    Time
    Billing Code
    Precedential
    Document
    Tool/
    Technology
    Used
    Participant
    INDIVIDUAL PROCESS STEP

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  171. THE PROCESS MAP

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  172. IT CAN AID IN
    Increasing Response Times
    Predicting Resource Loads
    Coordination Across Stakeholders
    Increasing Margins on Work

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  173. I TEACH A FULL LENGTH
    CLASS AT ILLINOIS TECH
    LED BY OUR COLLEAGUES
    FROM THE LAW FIRM
    SEYFARTH SHAW

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  174. SO THINK OF THE PROCESS
    MAP AS A FIRST ORDER
    ESTIMATION OF YOUR
    ACTUAL PROCESSES

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  175. RICH / GRANULAR DATA
    CAN HELP ILLUMINATE
    THE ACTUAL PROCESSES
    PRESENT IN VARIOUS
    (LEGAL) ORGANIZATIONS

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  176. FOR EACH NODE IN A
    PROCESS WE WANT TO
    BE ABLE TO RENDER A
    PREDICTION ABOUT
    THINGS SUCH AS
    DURATION, COST, ETC.

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  177. COURTS CAN PROVIDE
    SCIENTIFIC SCHEDULE
    OF CASES, ETC.

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  178. EACH UNIT OF TIME LINKED
    + LOGGED TO A NODE ON
    THE PROCESS MAP

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  179. LEAN
    PROCESS
    MAPPING

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  180. IF THERE IS NOT A NODE
    THEN IT CAN BE ADDED
    AND THUS THE MAP
    BECOMES MORE
    REFLECTIVE OF REALITY

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  181. JUST BE CAREFUL NOT TO
    CREATE A
    #RIDICULOGRAM

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  182. WITH PREDICTIONS ABOUT
    INDIVIDUAL NODES

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  183. WE CAN THEN SUM TO
    GENERATE PREDICTIONS
    ABOUT THE DISTRIBUTIONAL
    MOMENTS OF AN OVERALL
    MATTER (OR PHASE)
    (i.e. mean, variance, skewness, kurtosis)

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  184. THIS MATTER SHOULD TAKE …
    BETWEEN 9-15 MONTHS
    IN 85% OF THE SIMILAR MATTERS
    (WHAT ABOUT THE LONG TAIL?)

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  185. 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 … )

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  186. SPEAKING OF
    #PREDICTION

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  187. 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

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  188. H O W LO N G W I L L
    T H I S M AT T E R L A ST ?

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

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  190. W I L L W E W I N ?

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  191. WHAT ARE THE RIGHT
    RESOURCES TO USE AT THE
    MATTER AND TASK LEVEL ?

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  192. 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

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  193. 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

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  194. 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

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  195. 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

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  196. 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

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  197. P R E D I C T I O N
    MACHINE
    LEARNING
    NATURAL
    LANGUAGE
    PROCESSING
    A N D / O R
    =

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  198. 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

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  199. #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

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  200. A N D F R O M A
    G LO B A L
    P E R S P E C T I V E

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  201. 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

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  202. 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

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  203. #JudicialAnalytics
    Quantitative Legal Prediction

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  204. #PredictiveCoding #E-Discovery
    Quantitative Legal Prediction

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  205. 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

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  206. “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

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  207. “…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.”

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  208. 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.

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  209. 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

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  210. https://motoraccidents.lawnet.sg/

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  211. https://civilresolutionbc.ca/

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  212. 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

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  213. 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 …

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  214. A N D T H I S I S W H E R E
    YO U CO M E I N …

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  215. PA RT I I I
    L AW M E E T S ST E M

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  216. 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

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  217. LIBERAL ARTS LEGAL.EDU

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  218. POLYTECHNIC LEGAL.EDU
    LIBERAL ARTS LEGAL.EDU

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  219. Daniel Martin Katz, The MIT School of Law? A
    Perspective on Legal Education in the 21st Century,
    University of Illinois Law Review (2014)

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  220. I OUTLINE IN THIS PAPER
    MY VISION FOR WHAT
    LAW.EDU COULD BE ...
    (AND I AM NOT ALONE IN THIS VIEW)

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  221. THERE QUITE A
    FEW LAW
    SCHOOLS WITH
    SOME CREDIBLE
    VERSION OF A
    POLYTECHNIC
    OFFERING
    (SOME ARE MORE SERIOUS THAN OTHERS)

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  222. THE
    DEMOGRAPHIC
    REALITY …

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  223. WE ARE ABOUT TO
    HAVE THE
    LARGEST
    GENERATIONAL
    TRANSFER IN
    HUMAN HISTORY

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  224. THIS IS A MAJOR
    CHALLENGE TO
    EVERY
    ORGANIZATION

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  225. ORGANIZATIONS
    NEED DIFFERENT
    (BETTER) HUMAN
    CAPITAL IN ORDER
    TO SUPPORT ALL
    OF THESE TRENDS
    HIGHLIGHTED
    HEREIN

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  226. INCLUDING HELPING
    SOLVE THE
    COMPLEXITY
    CHALLENGE IN LAW

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  227. WE NEED MORE
    MULTI
    DISCIPLINARY
    INDIVIDUALS

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  228. WE NEED MORE
    MULTI
    DISCIPLINARY
    TEAMS

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  229. AND THIS SHOULD
    BE REFLECTED IN
    THE CONTENT OF
    LEGAL EDUCATION

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  230. T Shaped Professionals

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  231. I N T H I S
    P R E S E N TAT I O N

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  232. 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

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  233. 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

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  234. 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

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  235. 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 …

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  236. 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 )

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  237. 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

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  238. AT T H E
    I N T E R S E C T I O N O F
    L AW + ST E M

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  239. 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

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  240. 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

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  241. 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

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  242. 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

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