Jonathan
Yu-Meng
Li
Assistant
Professor,
Telfer
School
of
Management,
University
of
Ottawa
Ottawa,
ON
Jonathan.Li@telfer.uOttawa.ca
Abstract
Accounting
for
the
adverse
impact
of
"non-average"
events
has
become
essential
in
many
applications
involving
decision
making
under
uncertainty. Its
implementation
through
decision
models,
namely
stochastic
programs,
requires
careful measurement
of
risk
that
reflects
one's
concern
about
uncertain
outcomes.
Important
theories
such
as
convex
risk
measures outline
conditions
required
for
risk
measurement
but
provide
little guidance
for
cases
not
meeting
the
conditions.
Unfortunately,
such
cases
are
more
than
common in
real-life
situations.
In
particular,
in
this
talk,
we
study
cases
where
the
distribution
required
by
a
law
invariant
risk
measure
is
not
available
and/or
the
risk
preference
required
by
a
risk
measure
cannot
be
identified. We
aim
to provide
theoretical,
computational,
and
empirical
evidence
that
in
these
cases
optimization
can
be
a
powerful
tool
to
measure
risk in
a
systematic
fashion that
is
hard
to
achieve
otherwise. Applications
to operation
management
and
finance
will
be
presented.
Biographical
Sketch
Jonathan
Yu-Meng
Li
is
an
assistant
professor
in
the
Telfer
School
of
Management
at
University
of
Ottawa,
Canada.
His
research
focuses
on
the
interplay
between
optimization
theory
(stochastic,
robust,
and
inverse
optimization,
as
well
as
hybrids
thereof)
and
risk
theory
and
its
application
in
risk
management.
Jonathan
holds
a
PhD
from
the
University
of
Toronto
in
operations
research.
*Light refreshments will be served at 2:30pm