Daniel Martin Katz
February 17, 2021
280

# The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms

Professor Daniel Martin Katz presentation -- The Three Forms of (Legal) Prediction - Experts, Crowds + Algorithms as well as a discussion of SCOTUS and Associated Stock Market Returns aka "Law on the Market"

## Daniel Martin KatzPRO

February 17, 2021

## Transcript

1. ### Experts, Crowds + Algorithms Applied to SCOTUS daniel martin katz

The Three Forms of (Legal) Prediction blog | ComputationalLegalStudies.com edu | illinois tech - chicago kent law lab | TheLawLab.com page | DanielMartinKatz.com
2. ### TODAY I WOULD LIKE TO BEGIN WITH A PRACTICAL SET

OF LEGAL AND LAW RELATED PROBLEMS
3. ### AS THESE ARE THE CLASSIC QUESTIONS THAT CLIENTS POSE ON

AN ONGOING BASIS …

LIABILITY?

BE OFFERED

reasoning)
16. ### ABSTRACTION OF A PROJECTING WEIGHTS INTO A DECISION f( )

dimension 1 dimension 2 dimension 3 . . . . dimension n OUTPUT (Prediction, Decision, etc.) and / or INPUTS
17. ### PATTERN MATCHING evolutionary biology is an algorithm which privileges good

pattern matching

Charts

25. ### MORE SIMPLY STATED … WE CAN ASK EXPERT CROWD ALGORITHM

TO PREDICT SOMETHING
26. ### IN LAW, BOTH HISTORICALLY AND STILL TODAY HUMAN DECISION MAKING

IS THE DOMINANT FORM OF COMPUTATION
27. ### IN THE PAST DECADE THERE HAS BEEN AT LEAST SOME

EXPLORATION AT ALTERNATIVES

29. ### OR MORE REALISTICALLY IT SHOULD BE CALLLED THE USE OF

ALGORITHMS TO INTERROGATE PATTERNS IN DATA

33. ### THIS IS A TOPIC FOR WHICH I AM OFTEN CALLED

UPON TO COMMENT

OUTCOMES

45. ### #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict

Contract Terms/Outcomes Data Driven Transactional Work
46. ### #Predict Relevant Documents Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict

Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
47. ### #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven

Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work
48. ### #Predict Relevant Documents #Predict Case Outcomes / Costs Data Driven

Legal Underwriting Data Driven EDiscovery/Due Diligence (Predictive Coding) #Predict Rogue Behavior Data Driven Compliance #Predict Contract Terms/Outcomes Data Driven Transactional Work #Predict Regulatory Outcomes Data Driven Lobbying, etc.
49. ### TODAY I WANT TO TAKE THESE AND OTHER IDEAS AND

DISCUSS THEIR APPLICATION TO SCOTUS PREDICTION

51. ### EVERY YEAR, LAW REVIEWS, MAGAZINE AND N E W S

PA P E R A R T I C L E S , T E L E V I S I O N A N D RADIO TIME, CONFERENCE PANELS, BLOG P O S T S , A N D T W E E T S A R E D E V O T E D T O QUESTIONS SUCH AS: H O W W I L L T H E C O U R T R U L E I N T H I S PARTICULAR CASE? IN THE DIRECTION OF WHICH PARTY WILL AN INDIVIDUAL JUSTICE VOTE?

QUEST
53. ### YOU COULD START WITH HOLMES AND THE LEGAL REALISTS BUT

THESE WERE *NOT* REALLY SCIENTIFIC EFFORTS
54. ### FRED KORT, PREDICTING SUPREME COURT DECISIONS MATHEMATICALLY: A QUANTITATIVE ANALYSIS

OF THE “RIGHT TO COUNSEL” CASES, 51 AMER. POL. SCI. REV. 1 (1957). 1957 S. SIDNEY ULMER, QUANTITATIVE ANALYSIS OF JUDICIAL PROCESSES: SOME PRACTICAL AND THEORETICAL APPLICATIONS, 28 LAW & CONTEMP. PROBS. 164 (1963). 1963 A CO U P L E O F E A R LY E F F O RT S
55. ### COMPUTERWORLD JULY 1971 PROGRAM WRITTEN IN FORTRAN (THE 91% PREDICTION

MARK WAS LIMITED TO CERTAIN CASES) HAROLD SPAETH
56. ### JEFFREY A. SEGAL, PREDICTING S U P R E M

E C O U R T C A S E S PROBABILISTICALLY: THE SEARCH AND SEIZURE CASES, 1962-1981, 78 AMERICAN POLITICAL SCIENCE REVIEW 891 (1984) 1984 A N I M P O RTA N T L AT E R E F F O RT
57. ### Columbia Law Review (2004) Theodore W. Ruger, Pauline T. Kim,

Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project: B U T T H I S WA S T H E PA P E R T H AT I N S P I R E D O U R E F F O RT S 2004

59. ### Columbia Law Review October, 2004 Theodore W. Ruger, Pauline T.

Kim, Andrew D. Martin, Kevin M. Quinn Legal and Political Science Approaches to Predicting Supreme Court Decision Making The Supreme Court Forecasting Project:

61. ### Case Level Prediction Justice Level Prediction 67.4% experts 58% experts

From the 68 Included Cases for the 2002-2003 Supreme Court Term

S&P500

68. ### like many other forms human endeavor law is full of

noise predictors …

74. ### “Software developers were asked on two separate days to estimate

the completion time for a given task, the hours they projected differed by 71%, on average. When pathologists made two assessments of the severity of biopsy results, the correlation between their ratings was only .61 (out of a perfect 1.0), indicating that they made inconsistent diagnoses quite frequently. Judgments made by different people are even more likely to diverge.”
75. ### FA N TA SY S COT U S I S

A S CO O L A S I T S O U N D S
76. ### FA N TA SY S COT U S WA S

F O U N D E D B Y J O S H B L AC K M A N I N 2 0 0 9
77. ### P R I Z E S A N D S

P O N S O R S H I P H AV E VA R I E D B U T T H E R E H AV E B E E N T E N S O F T H O U S A N D S O F \$ \$ A N D P R I Z E S D I ST R I B U T E D OV E R T H E Y E A R S
78. ### U S E R S C R E AT E

A LO G I N A N D ACC E S S T H E S I T E
79. ### O N A CA S E B Y CA S

E B A S I S , U S E R S CA N E N T E R T H E I R R E S P E C T I V E P R E D I C T I O N S
80. ### U S E R S A R E F R

E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
81. ### https://ﬁvethirtyeight.com/features/ obamacares-chances-of-survival-are-looking- better-and-better/ ( S O M E T I

M E S I N I N T E R E ST I N G WAY S A S S H O W N A B OV E ) U S E R S A R E F R E E TO C H A N G E T H E I R P R E D I C T I O N S U N T I L T H E DAT E O F F I N A L D E C I S I O N
82. ### F O R E AC H CA S E ,

W E A R E A B L E TO T R AC K TO P E R F O R M A N C E O F P L AY E R S A N D CO M PA R E I T TO T H E O U TCO M E O F T H E CA S E S

I C S

86. ### 4 2 5 L I ST E D CA S

E S 6 S COT U S T E R M S
87. ### 7 2 8 4 U N I Q U E

PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 S COT U S T E R M S
88. ### 7 2 8 4 U N I Q U E

PA RT I C I PA N T S 4 2 5 L I ST E D CA S E S 6 3 6 8 5 9 P R E D I C T I O N S 6 S COT U S T E R M S
89. ### W E H AV E A S I G N

I F I CA N T A M O U N T O F P L AY E R T U R N OV E R WO R K I N G W I T H R E A L DATA ( ~ 3 % O F M A X PA RT I C I PAT I O N )
90. ### S O M E T I M E S F

O L K S C H A N G E T H E I R VOT E S 6 3 6 8 5 9 5 4 5 8 4 5 F I N A L P R E D I C T I O N S OV E R A L L P R E D I C T I O N S WO R K I N G W I T H R E A L DATA
91. ### T H E N U M B E R O

F P L AY E R S H A S D E C L I N E D B U T T H E E N G AG E M E N T R AT E H A S I N C R E A S E D WO R K I N G W I T H R E A L DATA
92. ### W E B E L I E V E T

H I S I S * N OT * R E A L LY S U RV I VO R S H I P B I A S B U T R AT H E R A R E V E L AT I O N M E C H A N I S M ( I . E . YO U ST I C K A R O U N D I F T H I N K YO U A R E G O O D AT T H E U N D E R LY I N G TA S K )

95. ### C R O W D S O U R C

I N G C R O W D S O U R C I N G D O E S * N OT * R E F E R TO A S P E C I F I C T E C H N I Q U E O R A LG O R I T H M
96. ### C R O W D S O U R C

I N G G E N E R A L LY R E F E R S TO A P R O C E S S O F AG G R E G AT I O N A N D / O R S E G M E N TAT I O N O F I N F O R M AT I O N S I G N A L S
97. ### VA R I O U S S I G N

A L TY P E S T H E I N P U T S I G N A L S CA N A S S U M E M A N Y D I F F E R E N T F O R M S I N C LU D I N G F R O M M O D E L S O R I N D I V I D UA L S O R S E N S O R S ( O R S O M E CA S E S E V E N OT H E R C R O W D S )
98. ### CROWD OF INDIVIDUALS The most well know approach involves extracting

‘wisdom’ from crowds where crowds are built from individual people
99. ### CROWD OF SENSORS Note crowds need not be composed of

humans but could be networked IT systems Decentralized Distributed Ledgers -or- Oracles -or- IOT sensors with Crowdsourcing Validation #Blockchain #InternetofThings #Crypto

101. ### Random Forest Model Breiman, L.(2001). Random forests. Machine learning, 45(1),

5-32. Grow a set of differentiated trees through bagging and random substrates (predict using a consensus mechanism) C R O W D O F M O D E L S
102. ### A S W E R E V I E W

E D T H E C R O W D S O U R C I N G L I T E R AT U R E …
103. ### W E O B S E RV E D T

H AT I T WA S D I F F I C U LT TO A P P LY T H E P R I N C I P L E S TO C R O W D S S U C H A S O U R S
104. ### C R O W D S O U R C

I N G I S ‘ U N D I S C I P L I N E D ZO O O F M O D E L S ’ J E S S I CA F L AC K P R O F E S S O R S A N TA F E I N ST I T U T E D E C . 2 7 , 2 0 1 7 ( V I A T W I T T E R )
105. ### W E AG R E E … A N D

T H U S I N T H E PA P E R W E J U ST STA RT E D OV E R …
106. ### A N D AT T E M P T E

D TO B U I L D C R O W D S F R O M F I R ST P R I N C I P L E S …

108. ### W E O U T L I N E A

G E N E R A L F R A M E WO R K F O R CO N ST R U C T I N G C R O W D S F R O M F I R ST P R I N C I P L E S
109. ### I N T H E C L A S S

I C CO N D O R C E T J U R Y S E T T I N G , M O D E L S TY P I CA L LY U S E P R E D I C T I O N S F R O M A L L PA RT I C I PA N T S
110. ### H O W E V E R , M O

D E L S CA N A L S O TA K E I N TO ACCO U N T I N F O R M AT I O N ( S I G N A L S ) F R O M S O M E S U B S E T O F PA RT I C I PA N T S ( D E F I N E D U S I N G E I T H E R I N C LU S I O N R U L E S O R E XC LU S I O N R U L E S )
111. ### E X P E R I E N C E

P E R F O R M A N C E R A N K STAT I ST I CA L T H R E S H O L D I N G W E I G H T I N G C R O W D CO N ST R U C T I O N R U L E S
112. ### C R O W D CO N ST R U

C T I O N R U L E S T H E I N T E R AC T I O N O F T H E S E R U L E S Y I E L D S * N OT * A N I N D I V I D UA L M O D E L B U T R AT H E R A M O D E L S PAC E
113. ### T H E M O D E L S PAC

E M O D E L S PAC E F E AT U R E S 2 7 7 , 2 0 1 P OT E N T I A L M O D E L S
114. ### T H E M O D E L S PAC

E 1 + ( 2 8 · 9 9 · 1 0 0 ) = 2 7 7 2 0 1 F I R ST T E R M I S T H E S I M P L E ST C R O W D S O U R C I N G M O D E L W I T H N O S U B S E T O R W E I G H T I N G R U L E S S E CO N D T E R M CO R R E S P O N D S TO 2 8 M O D E L S ( CO M B I N AT I O N O F P E R F O R M A N C E T H R E S H O L D / W E I G H T I N G R U L E S ) F O R E AC H CO M B I N AT I O N O F 9 9 R A N K A N D 1 0 0 E X P E R I E N C E T H R E S H O L D S

116. ### W E S I M U L AT E T

H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S M O D E L ( S ) ACC U R ACY
117. ### A LT H O U G H I T I

S A L A R G E M O D E L S PAC E W E D O H I G H L I G H T T H E P E R F O R M A N C E O F F O U R E X A M P L E M O D E L S ( A N D T H E N U L L M O D E L )
118. ### B A S E L I N E A LWAY

S G U E S S R E V E R S E N U L L M O D E L
119. ### M O D E L 1 A L L C

R O W D S I M P L E M A J O R I TY
120. ### M O D E L 2 F O L LO

W T H E L E A D E R W I T H N O T H R E S H O L D I N G
121. ### M O D E L 3 F O L LO

W T H E L E A D E R W I T H E X P E R I E N C E T H R E S H O L D I N G ( X P = 5 )
122. ### M O D E L 4 M A X I

M U M ACC U R ACY ( T H E R E A R E AC T UA L LY S E V E R A L M O D E L CO N F I G U R AT I O N S W H I C H O F F E R R O U G H LY E Q U I VA L E N T P E R F O R M A N C E ) E X P E R I E N C E T H R E S H O L D O F 5 C R O W D S I Z E I S CA P P E D AT 2 2 E X P O N E N T I A L W E I G H T W I T H A L P H A O F 0 . 1

126. ### W E S I M U L AT E T

H E P E R F O R M A N C E O F * E AC H * O F T H E 2 7 7 , 2 0 1 P OT E N T I A L C R O W D M O D E L S R O B U ST N E S S O F P E R F O R M A N C E
127. ### ROBUSTNESS VISUALIZED T H I S I S A L

L R E L AT I V E TO T H E N U L L M O D E L ( O F A LWAY S G U E S S R E V E R S E )
128. ### ROBUSTNESS VISUALIZED T H E CO N TO U R

P LOT F L AT T E N S T H E D I M E N S I O N A L I TY O F T H E S PAC E ( E AC H C E L L I S T H E AV E R AG E M O D E L P E R F O R M A N C E OV E R A L L OT H E R M O D E L PA R A M E T E R AT E AC H E X P E R I E N C E , R A N K CO M B O )
129. ### ROBUSTNESS VISUALIZED J U ST I C E L E

V E L CA S E L E V E L

131. ### N OT A L L M E M B E

R S O F C R O W D A R E M A D E E Q UA L
132. ### W E M A I N TA I N A

‘ S U P E R C R O W D ’ W H I C H I S T H E TO P N O F P R E D I C TO R S U P TO T I M E T- 1
133. ### the ‘supercrowd’ outperforms the overall crowd (and even the best

single player)
134. ### H T T P S : / / A R

X I V. O RG / A B S / 1712 . 0 3 84 6 H T T P S : / / PA P E R S . S S R N . C O M / S O L 3 / PA P E R S . C F M ? A B S T R AC T _ I D = 3 0 8 5710

136. ### 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.

138. ### T H E S O U R C E CO

D E F O R O U R A LG O PA P E R I S AVA I L A B L E O N
139. ### W E CA L L O U R A LG

O PA P E R A ‘ G E N E R A L’ A P P R OAC H
140. ### B E CAU S E W E A R E

N OT I N T E R E ST E D I N A LO CA L LY T U N E D M O D E L B U T R AT H E R A M O D E L T H AT CA N ‘ STA N D T H E T E ST O F T I M E ’
141. ### G E N E R A L S COT U

S P R E D I C T I O N 243,882 28,009 Case Outcomes Justice Votes 1816-2015
142. ### G E N E R A L S COT U

S P R E D I C T I O N 70.2% accuracy at the case outcome level 71.9% at the justice vote level 1816-2015
143. ### W E P R E D I C T *

N OT * A S I N G L E Y E A R B U T R AT H E R ~ 2 0 0 Y E A R S ( 1 8 1 6 - 2 0 1 5 ) O U T O F S A M P L E
144. ### N O W I T I S WO RT H

N OT I N G T H AT P R E D I C T I O N O R I E N T E D PA P E R S A R E C U R R E N T LY S W I M M I N G AG A I N ST M A I N ST R E A M S O C I A L S C I E N C E ( A N D L AW )
145. ### CAU S A L I N F E R E

N C E I S T H E H A L L M A R K O F M O ST Q UA N T O R I E N T E D L AW + S O C I A L S C I E N C E S C H O L A R S H I P
146. ### I T I S B E ST S U I

T E D TO P O L I CY E VA LUAT I O N ( S U C H A S D O E S T H I S 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 I T S STAT E D O B J E C T I V E S )
147. ### O R I N STA N C E S W

H E R E E STA B L I S H I N G L I N K S B E T W E E N CAU S E A N D E F F E C T A R E C R I T I CA L
148. ### B U T T H E R E I S

A N A LT E R N AT I V E PA R A D I G M # P R E D I C T I O N
149. ### M AC H I N E L E A R

N I N G P R E D I C T I V E A N A LY T I C S ‘ I N V E R S E ’ P R O B L E M B -S C H O O L CO M P S C I P H Y S I C S P R E D I C T I O N
150. ### Andrew D. Martin, Kevin M. Quinn, Theodore W. Ruger &

Pauline T. Kim, Competing Approaches to Predicting Supreme Court Decision Making, 2 Perspectives on Politics 761 (2004). “the best test of an explanatory theory is its ability to predict future events. To the extent that scholars in both disciplines (social science and law) seek to explain court behavior, they ought to test their theories not only against cases already decided, but against future outcomes as well.”
151. ### https://www.computationallegalstudies.com/2017/08/28/legal-analytics-versus-empirical- legal-studies-causal-inference-vs-prediction/ https://www.slideshare.net/Danielkatz/legal-analytics-versus-empirical- legal-studies-or-causal-inference-vs-prediction-redux M O R E O N

T H AT TO P I C H E R E
152. ### T H E R E I S G R O

W I N G I N T E R E ST I N T H E P R E D I C T I O N C E N T R I C A P P R OAC H
153. ### “There are two cultures in the use of statistical modeling

to reach conclusions from data. One assumes that the data are generated by a given stochastic data model. The other uses algorithmic models and treats the data mechanism as unknown …. If our goal as a ﬁeld is to use data to solve problems, then we need to move away from exclusive dependence on data models and adopt a more diverse set of tools.” Leo Breiman, Statistical modeling: The two cultures (with comments and a rejoinder by the author), 16 Statistical Science 199 (2001) Note: Leo Breiman Invented Random Forests

155. ### 3 5 5 S C I E N C E

6 3 2 4 3 F E B R UA R Y 2 0 1 7 S P E C I A L I S S U E O N P R E D I C T I O N

157. ### P R E D I C T I O N

I S N OT N E C E S S A R I LY # M L A LO N E B U T R AT H E R S O M E E N S E M B L E O F E X P E RT S , C R O W D S + A LG O R I T H M S
158. ### http://www.sciencemag.org/news/ 2017/05/artiﬁcial-intelligence-prevails- predicting-supreme-court-decisions Professor Katz noted that in the long

term …“We believe the blend of experts, crowds, and algorithms is the secret sauce for the whole thing.” May 2nd 2017
159. ### IN MANY INSTANCES BLENDS OF INTELLIGENCE WILL OUTPERFORM A SINGLE

STREAM OF INTELLIGENCE

162. ### crowd forecast learning problem is to discover how to blend

streams of intelligence algorithm forecast ensemble method ENSEMBLE MODEL we can use machine learning methods and metadata such as case topic, lower court as well as crowd metadata to ‘learn’ the conditional weights to apply to the input signals

SOMETHING …
164. ### AND BY PREDICT … I AM TALKING ABOUT PREDICTION AT

THE PORTFOLIO LEVEL …
165. ### I HAVE BEEN VERY INTERESTED IN THE OVERLAP BETWEEN LEGAL

TECH FIN TECH

Law

170. ### When we would present this work on #SCOTUS Prediction folks

would ask us “why do I care about marginal improvements in prediction ? “
171. ### Well at a very minimum — if you could predict

the cases you could perhaps trade on them in the relevant securities market …
172. ### In other words, given our ability to offer forecasts of

judicial outcomes, we wondered if this information could inform an event driven trading strategy ?

~\$3 billion+
176. ### Myraid Genetics “Myriad employs a number of proprietary technologies that

permit doctors and patients to understand the genetic basis of human disease and the role that genes play in the onset, progression and treatment of disease.”
177. ### Myraid Genetics “Myriad was the subject of scrutiny after it

became involved in a lengthy lawsuit over its controversial patenting practices” which including the patenting of human gene sequences ....

180. ### Initial Media Reports Early Afternoon “In early afternoon trading Thursday,

Myriad shares were up 5.4 percent, or \$2.36, at \$35.73.”

187. ### Day 1 Close MYGN is Off Nearly 10% from Open

and 20% from Daily High

192. ### ONE OBVIOUS CHALLENGE IS THE PROSPECT THAT THIS INFORMATION IS

ALREADY INCORPORATED INTO THE PRICE OF THE RELEVANT SECURITY #EfﬁcientMarketHypothesis #Fama #EMH
193. ### IN ALLIED FIELDS OF HUMAN ENDEAVOR, THERE ARE FAIRLY RAPID

MARKET RESPONSES TO CHANGES IN THE INFORMATION ENVIRONMENT
194. ### THIS ALL PRESUPPOSES A RIGOROUS INFORMATION AND MODELING ENVIRONMENT —

THAT IS GENERALLY LACKING IN QUESTIONS OF LEGAL PREDICTION #QuantitativeLegalPrediction #LegalAnalytics #FinLegalTech

197. ### Market Incorporation Hypothesis Are judicial decisions already reﬂected in the

share price ? (If this were true - we would rarely see market move post decision)
198. ### How General Are These Speciﬁc Examples? Theoretical + Empirical Questions

(In other words, is this a general phenomenon ?)
199. ### What is the nature of the signal incorporation environment ?

(In other words, what are the dynamics associated with does the market response ?) Theoretical + Empirical Questions

201. ### (1) Coding / PreProcessing (2) Candidate LOTM Events (3) Formal

Evaluation Using CAPM (market model of returns) (4) Evaluate Speed of Incorporation and Related Informational Dynamics
202. ### (1) Coding / PreProcessing We reviewed and coded 1,363 total

cases decided over the period in questions. We asked a simple question - could this case plausibly impact a publicly traded security ?
203. ### All Data & Code is Available Here^ ^Other than the

WRDS Data which is *not* open source but can be obtained from Wharton https://github.com/mjbommar/law-on-the-market https://wrds-web.wharton.upenn.edu/wrds/

205. ### Abnormal Returns Common approach is to use index as baseline

and seek to identify statistically signiﬁcant deviations from that baseline We want to isolate the effect of the event from other general market movements
206. ### This Paper Leverages 5 Minute Data -5 Days, +5 Days

A KEY POINT much higher frequency than most papers in literature

213. ### This is very slow … Perhaps the real action is

in the options market ?
214. ### In conclusion, we believe that this research raises many questions

and justiﬁes a range of future work in the area
215. ### Future Work Real Trading Strategy Analysis Other Classes of Litigation

Events 8k’s and Docket Arbitrage Higher and Lower Order Analysis Litigation Reserves, etc.

Reserves

219. ### TODAY I HAVE GIVEN YOU AN OVERVIEW OF THREE APPROACHES

TO LEGAL PREDICTION