CLE-TV: Data Based Decision Making in Litigation 2020

Product Type: Course

This course has no current sessions.

DescriptionMore DetailAgenda

Moneyball law—using forecasting and statistics to win!

(Original Course Date: March 4, 2020)

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Who should attend: Civil litigators in all sub-specialties

Learning level: Fundamental/Intermediate

Making known the unknown—improving litigation outcome forecasting using decision analysis.

Lawyers frequently suggest they cannot forecast litigation outcomes reliably, blaming uncertainties in the evidence, the challenge of finding facts, or the vagaries of a judge's predisposition. At best, lawyers resort to platitudes such as a client has a "strong" or "weak" case, or worse: "I have a good feeling." To improve their ability to forecast outcomes, make budgets, and focus resources, lawyers with strong quantitative skills are employing, with greater frequency, decision analysis, regression tools, and simulation models to improve the defensibility and rigour of their analysis. These tools enable lawyers to improve the quality of advice they give clients, make better decisions about where to focus a client's resources and refine their arguments.

In exploring these tools, lawyers will gain a greater fundamental understanding of computing approaches to law. Further, lawyers can gain greater confidence in assessing artificial intelligence and predictive tools gaining traction in our profession.

Chilwin Cheng illustrates how he uses descriptive statistics, decision trees, and Monte Carlo simulations to forecast litigation outcomes and budgets and how solicitors can use these techniques to better quantify and describe "real world" consequences of legal failure. In doing so, he illustrates more broadly quantitative approaches to legal practice in a profession that has historically approached problems with qualitative and narrative techniques.

From this course, you will learn how to:

present legal options to clients using statistical concepts that clients will appreciate and understand
design decision trees to map out legal strategies and where to best deploy a legal budget
use evidence-based techniques to set legal budgets

Join us today!

Law Society of BC CPD Hours: 1.5 hour (this course will NOT involve any professional responsibility and ethics, client care and relations, and/or practice management)

Course Instructor
Chilwin Cheng — Principal, Ascendion Law, Vancouver

Reduced Pricing To support you during COVID-19, CLEBC is offering reduced pricing for courses.
Pricing Last Kick at the Can 2020
Rebroadcast	$149 $105
Rebroadcast Articled Student	$74.50 $52.50
Experiencing financial hardship due to COVID-19? You may be eligible for further reductions in our course prices. Please contact Customer Service for more information. Registration includes an electronic copy of the reference materials. *Please note:* The CPD hours of the rebroadcast will either meet or exceed the CPD hours of the original live webinar course.

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CLEBC Program Lawyer*
Raymond Lee
rlee@cle.bc.ca
*non-practising

Course Instructor
Chilwin Cheng — Principal, Ascendion Law, Vancouver

About the Course Instructor
Chilwin Cheng is a skillful business litigator with deep experience in regulatory and criminal defence work, and complex corporate and commercial litigation. He is a leader in his field, having been Chief Counsel of the regulator of Canada's public equities markets and trading systems, a prosecutor with the BC Securities Commission, and a former Crown Counsel. He's a thought leader in law practice innovation and a pioneer in the legal industry using quantitative approaches to litigation assessment and management. Chilwin actively presents at conferences and client seminars on how to use decision trees, Monte Carlo simulations, scenario planning, regression, and descriptive statistics to budget more accurately and forecast outcomes with greater confidence.

Taking a Quantitative Approach to Litigation

Why is Quantitative Analysis Important for Lawyers?

Introduction

the present use of computational approaches to lawyering using statistics and quantitative approaches
what do we mean by quantitative approaches?
case or narrative analysis versus empirical analysis
predictive coding uses computational approaches to forecast relevance of documents
BlueJay Legal uses computational approaches to forecast relevance of cases

The Future is Not So Far Out

"AI" or computational approaches to practice
IBM Watson and other AI platforms for legal

Statistics 101—Back to High School

Descriptive Statistics—A High School Review

the importance of knowing the uses of averages, medians, and modes
your "worst case" and "best case" scenarios: maximums and minimums
how "skew" should inform uncertainty and forecasting
example: descriptive statistics to describe wrongful dismissal awards
example: descriptive statistics to describe Charter exclusion application success
example: descriptive statistics to describe enforceability of restrictive covenants

Using Statistics to Inform

using graphs and charts to persuade
using timeline charts to show chronologies
example: illustrating potential penalty awards in a regulated industry

Using Decision Trees and Bayesian Logic to Model Outcomes

What is a Decision Tree

nodes
branches
probabilities
outcomes and payoffs
putting the concepts together
example: using a Decision Tree to model a civil litigation claim
example: using a Decision Tree to model a criminal case
summary

Forecasting in a Litigation Context

the distinction between variability and uncertainty
using scenario analysis to account for uncertainty
using Monte Carlo simulations to account for uncertainty
example: using scenario analysis to predict legal budgets
example: using Monte Carlo simulation to predict legal budgets
example: combining Decision Trees and Monte Carlo simulation to forecast litigation outcomes

Questions and Concluding Comments

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