Inroduction to aim, scope and content of the course.
Syllabus.
The importance of critical thinking and reasoning.
Overview of coursework
requirements (six assignments,
two exams,
one presentation,
one final report
and class summaries assigned at random)
Probability as an essential ingredient of the course.
Course combines qualitative and quantitative aspects.
The Rootclaim website as a model.
How to ask investigative questions.
Different types of questions:
about definitions,
causal relationships,
events in space and time,
policy questions.
Examples of topics for investigative questions:
Morandi bridge collapse in Genoa, Italy;
Monsanto verdict.
Answering questions and formulating hypotheses.
Going over assignments
#1
and
#2.

Week 2: Sept 3 - no class: Labor day

Office hours on
Wed Sept 5 in CA-365
from noon to 5 PM.
Please stop by to hand in
assignment #1.
We will read it and discuss it together so
that you'll be prepared to do assignment
#2.

Week 3: Sept 10 - no class: Rosh Hashanah

Even if today it's a holiday,
please work on assignment
#2.

Week 4: Sept 17

Review of past assignments #1
and
#2.
Going over new assignment #3.
Discussion of types of evidence:
eywitness testimonies,
scientific studies,
photgraphic evidence,
video evidence,
etc.
Relationship between evidence and hypotheses.
Evidence for/against a hypothesis.
How to assess the reliability of a piece of evidence.
How to summarize, present and document your evidence.

Week 5: Sept 24

Review of assignment #3.
Intuitive ways to weigh evidence.
Importance of probability theory
to handle uncertainity.

PART 2: Probability Theory

Week 6: Oct 1

Basics of probability theory.
Role of probability in your investigative progejct.
How probable are your hypotheses given the evidence you've collected?
Venn diagrams.
Sets.
Union, intersection, complementation.
Space of all equipossible outcomes.
Probability law, for example,
Pr(A)= #(successful outcomes where A obtains)/#(all possible outcomes).
Conceptions of probability.
Probability as relative frequency.
Probability as evidence-based subjective estimate.

Conditional probability.
Distinction between Pr(A|B) and Pr(B|A)
an its significance.
Illustrations.

Week 9: Oct 22

Exam #1
Review.

Week 10: Oct 29

Bayes's theorem.
Prior probabilities Pr(H).
Probability of evidence given hypothesis Pr(E | H).
Probability of hypothesis given evidence Pr(H | E).
Medical diagnoses.
Legal applications.

Week 11: Nov 5

More on Bayes's theorem.

Week 12: Nov 12

Exam #2 and review.

PART 3: Applying Probability

Week 13: Nov 19

How to assign prior probabilities to hypotheses.
Base rates.
Objective/subjective estimates.
Examples from Rootclaim.

Week 14: Nov 26

Evidence given hypothesis, Pr(E | H).
Not to be confused with hypothesis
given evidence, Pr(H | E).
Relevance of the distinction
for investigative project.

Week 15: Dec 3

Bayes' theorem applied.
Posterior probabilities given all the pieces of evidence combined,
say, Pr(H | E1 & E2 & E3 & ...)
Using Bayes' networks to compute posterior probabilities.
Program Genie to ease calculations.