"Quite frankly, teachers are
the only profession that teach our children."
- Dan Quayle
I have experience teaching courses in
modeling, decision analysis, systems analysis, and statistics at the
undergraduate, professional, and graduate levels. Additionally, I am interested in teaching undergraduate
courses in environmental engineering and natural resource management and
graduate courses in decision theory, probability theory and applications,
graphical network modeling, and statistical inference, all from either a frequentist or Bayesian perspective.
My teaching style can be characterized as
“decision-oriented” because of its emphasis on the role of students
in formulating problems, selecting methods of analysis, and critically
evaluating the results. This makes the
students active participants in the classroom experience and helps them to
transfer the knowledge and skills they gain to other situations. This approach has been popular with students,
who have commented that my classes are “very challenging and very
interesting. [They require] a lot of
thinking in ways not commonly done in other courses.”
By using real-world problems and data, I
provide students with a compelling context for their learning. The problems I choose are often pressing,
involve some amount of controversy, and have a high degree of scientific
uncertainty. When students have the
opportunity to touch on these aspects, it stimulates their interest in the
implementation of an appropriate method of analysis. Their active role in structuring problems
also allows them to apply their conceptual abilities. This type of thinking is not always
encouraged in conventional quantitative courses.
Student reaction to my teaching approach
has been positive. For example, in my
first year as instructor of the decision analysis course in the Master of
Environmental Management program at Duke, I added a computer laboratory
component that had not previously been part of the curriculum. Comments by students at the end of the
semester included : “Labs were very
beneficial. They cleared up questions
from class and taught cool programs that apply things we learned,” and
“I can’t imagine how the stuff was covered before. One of the reasons why I enjoyed the lab was
because Mark did not waste our time.
Each hour was used to the fullest – and then we actually had to
use it in homework.”
Constructive criticism on the course
related to the examples that I used, which at the time came from a
business-oriented text: “I wish that greater emphasis had been placed on
environmental problems and the applicability of decision theory to
environmental negotiations, problems, decisions.” These criticisms encouraged me to develop
more environmental-themed examples and exercises for subsequent years, often based
on my own research. The pool of real-world
applications that I have assembled has continued to grow. This has led to ongoing updates and
improvements in my courses.
Of course, students’ motivation to
learn is greatly influenced by teacher skill and enthusiasm, and former students
have recognized my love of the subject: “Great instructor! Always there to answer questions, explains
things clearly and patiently, very knowledgeable about this subject,”
“He explains things simply and concisely.
He completely understands the topic and wants others to enjoy it as much
as he does!” – I couldn’t state this aspiration any better
myself!
Fall 2007 Course
Decision-Making under
Risk and Uncertainty
Making decisions under conditions
of risk and uncertainty is a fundamental part of every engineer and
manager’s job, whether the situation involves product design, investment
choice, regulatory compliance, or human health and safety. This course will provide students with both
qualitative and quantitative tools for structuring problems, describing
uncertainty, assessing risks, and reaching decisions, using a variety of case
studies that are not always amenable to standard statistical analysis. Bayesian methods will be introduced,
emphasizing the natural connections between probability, utility, and
decision-making.
Prerequisites: ENGS 27, ENGS 103, or comparable background in
probabilistic reasoning
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Last Revised: August 2007
by MEB