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[CCICADA-announce] DIMACS/CCICADA Workshop on Risk-Averse Algorithmic Decision Making

Linda Casals lindac at dimacs.rutgers.edu
Wed Feb 9 09:42:25 EST 2011


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DIMACS/CCICADA Workshop on Risk-Averse Algorithmic Decision Making

  May 9 - 13, 2011
  DIMACS Center, CoRE Building, Rutgers University

Organizers:
    Melike Baykal-Gursoy, Rutgers University, gursoy at rci.rutgers.edu 
    David Brown, Duke University, dbbrown at duke.edu 
    Aleksandar Pekec, Duke University, pekec at duke.edu 
    Andrzej Ruszczynski, Rutgers University, rusz at business.rutgers.edu 
    Dharmashankar Subramanian, IBM Watson Labs dharmash at us.ibm.com 

Presented under the auspices of the Special Focus on Algorithmic
Decision Theory and The Homeland Security Center for Command, Control,
and Interoperability Center for Advanced Data Analysis (CCICADA).

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Announcement:

Properly assessing riskiness of available options is central to
decision making under uncertainty. Measures of risk are often
important in the context of solving dynamic optimization
problems. This workshop will concentrate on recent work developing
alternative measures and their properties, especially in the context
of dynamic optimization. Since a risk measure has to be efficiently
computed to be useful, there is a need to develop efficient algorithms
that address dynamic risk measures in both the objective function and
the constraints. This calls for new methods of ADT. It also calls for
new methods for modeling constraints in a dynamic setting in a
practically useful manner and for ways to understand the interplay
among risk measures, problem formulation, and computational
tractability.

Standard risk measures are sometimes captured by the second moment of
multidimensional random variables (i.e., the covariance
matrix). However, these are often not the most appropriate for a
problem at hand. For example, Value-at-Risk ($VaR$) is a prevalent
measure used by financial institutions: It measures one's exposure to
large losses. (More precisely, for a random variable $X$, VaR at the
confidence level $\alpha$, $VaR_\alpha(X)$, is defined as the value
$P(X < VaR_\alpha(X))=1-\alpha$, i.e., there is only a $1-\alpha$
chance for $X$ to attain a value below $VaR$.). The improper use of
$VaR$ in risk management has been pinpointed as one of the critical
factors that led to the financial crisis of 2008 and its role in the
crisis has even been subject to a Congressional hearing. Computing
$VaR$ across the portfolio of investments is a well-studied
optimization problem, but many logical improvements and extensions of
$VaR$, such as Conditional Value-at-Risk ($CVaR$) that measures
expected value of the loss given the loss is larger than some
threshold, pose interesting and challenging optimization issues that
we will explore.

Efficient algorithms have been developed for solving single-stage,
static mathematical programs that involve a CVaR risk measure in the
objective function and/or constraints. However, extending the ideas to
address risk-aware optimization problems in a dynamic setting is a
relatively new area. Such extension involves choice of an appropriate
dynamic risk measure that satisfies the notion of time-consistency. A
characterization for such measures in the form of conditional risk
mappings can be found in. Other papers address incorporating such risk
measures in the objective function for risk-averse 2-stage linear
programs and Markov decision problems.

Recent research efforts have focused on the interplay between decision
making with classes of risk measures and robust and stochastic
optimization. For example, uncertainty sets in the context of robust
optimization formulations are related to risk measures proposes a more
general model of robust optimization and applies the approach to
stochastic optimization problems involving ambiguity in the underlying
probability distribution; this approach is shown to be closely
connected to convex risk measures developed. Recent work developing
models of choice involving aspiration levels as well as approaches for
performance evaluation have also drawn on dual connections to risk
measures obeying desirable properties. Thus, there is a further need
to develop optimization techniques and algorithms for computing risk
measures, and to understand the limitations that existing optimization
algorithms impose on the viability of a risk measure when efficiency
in computing is important. Consequently, the WS will also focus on
practically useful modeling techniques and efficient algorithmic
approaches for incorporating risk measures in the constraints and/or
objective function.

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Call for Participation:

Talks are by invitation only but participation is open to all that register. 

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Registration:

 For details and to register see:
   http://dimacs.rutgers.edu/Workshops/RiskMeasures/

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 Information on participation, registration, accommodations, 
 and travel can be found at:
   http://dimacs.rutgers.edu/Workshops/RiskMeasures/
    
  **PLEASE BE SURE TO PRE-REGISTER EARLY**
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