Abstracts (PDF file, 1.8MB) - Society for Risk Analysis

SRA 2013 Annual Meeting Abstracts
P.148 Zhuang, J; Wang, X*; Song, C; Xu, J; University at
Buffalo, SUNY; xwang54@buffalo.edu
Robust Approval Process in the Face of Strategic
Adversaries and Normal Applicants
The objective of this project is to explore a new class of decision
models to provide structural insights for robust screening when
dealing with adaptive applicants and incomplete information.
This research is motivated by public concerns on balancing
congestion and safety due to security screening. Such
screening has been used to identify and deter potential threats
(e.g., terrorists, attackers, smugglers, spies) among normal
applicants wishing to enter an organization, location, or facility.
In-depth screening could reduce the risk of being attacked.
However it may also create delays and deter normal applicants,
which decreases the welfare of both the approver (authority,
manager, screener) and the normal applicants. This research
will consider the factors of security, congestion, equity, and the
strategic and non-strategic responses from various applicant
types. In particular, this research studies the applicants'
strategies of applying, reneging, learning, and deceiving. This
research also studies the approver's strategies of screening,
dynamic service rates, multiple-servers and priority processing,
multi-layer screening, and secrecy and deception. If successful,
this research will lead to new frameworks that decision makers
can use for screening diverse groups of strategic applicants.
These new frameworks have the potential to reduce costs, avoid
unnecessary waiting and inconvenience, and improve
effectiveness and efficiency of the approval processes. Potential
applications of this research include immigration systems, job
market background checks, and airport/container/border
controls. The relevance is illustrated by the recent national
debate on selective "pat-downs" and "advanced
imaging"screening, and the associated changing travel
patterns. This research will engage many graduate,
undergraduate, and high school students, including those from
under-represented groups. The results of this research will be
disseminated broadly to local, national and international
communities.
P.151 Zhuang, J; Fu, J*; Jose, VRR; University at Buffalo,
SUNY; jfu3@buffalo.edu
Modeling attacker-defender games with risk preferences
Traditional models of attacker-defender games generally
assume that players are risk-neutral; i.e. they choose strategies
that maximize their expected payoff or benefit. In practice,
decision makers could be either risk seeking or risk averse,
which has not been extensively studied in the attacker-defender
game literature. For example, terrorists could have risk
preferences (Yang et al. 2013). Government could be risk
averse: Standish (2002) argued that Western governments tend
to be extremely risk-avers and constantly introduce disruptive
risk-averse policies in many areas such as air travel security;
Stewart et al. (2011) stated that the amount of government
spending for “many homeland security measures would fail a
cost-benefit analysis using standard expected value methods of
analysis [suggesting] not surprisingly that policy makers within
the US Government /DHS are risk-averse.” Unfortunately, the
growing attacker-defender game literature has not rigorously
studied the players’ risk preferences. To fill this gap, the
research objective of this proposal is to study how the
incorporation of alternative models of behavior (e.g., expected
utility and cumulative prospect theories) affects the equilibrium
behavior of players in attacker-defender games. Though
Zhuang and Bier (2007, Operations Research) asserted that the
more risk-averse a defender is, the more likely she is to defend
and that the less risk-averse an attacker is, the more likely he is
to attack, our preliminary results show that this behavior may
not be true when other factors such as initial wealth, risk
aversion/risk seekingness, loss aversion, and optimism are
incorporated into a model. If successful, this project results can
enrich our understanding of how players in attacker-defender
games when a rich set of empirically recognized aspects of
human behavior and decision making is introduced in analytic
models, and help transform a large group of existing models
which ignore player preferences.
P.152 Zhuang, J; Bier, V; Zhang, J*; University at Buffalo,
SUNY; University of Wisconsin-Madison;
Jingzhang42@buffalo.edu
First Conference on Validating Models of Adversary
Behavior
Hundreds of billions of dollars have been spent on homeland
security since September 11, 2001, and numerous models have
been developed to study the strategic interactions be tween
defenders and adversaries (e.g., attackers or terrorists).
Unfortunately, few if any models have yet been validated using
empirical data, limiting the application of those models in
practice. Supported by the U. S. Department of Homeland
Security (DHS) through the National Consortium for the Study
of Terrorism and Responses to Terrorism (START) and the
National Center for Risk and Economic Analysis of Terrorism
Events (CREATE), Drs. Jun Zhuang and Vicki Bier organized a
conference on validating models of adversary behavior in
Buffalo/Niagara Falls, NY, in June 23-26, 2013. This conference
is intended to bridge theoretical and empirical research on
adversarial modeling, and facilitate transitioning of the best
existing models of adversary behavior into practice by
assessing and demonstrating their validity and applicability to
real-world problems. A secondary goal of the conference is to
encourage synergy and communication between risk analysts,
statisticians, economists, and other social scientists engaged in
terrorism modeling and research. In this poster presentation,
we summarize the summary of the conference findings.
T1-I.1 Zimmerman, R; New York University;
rae.zimmerman@nyu.edu
Network approaches to assess critical infrastructure risks
Portraying critical infrastructure characteristics as networks is
an important foundation for analyzing risks to these
infrastructures from extreme events. Risk and network
concepts are combined for critical infrastructure, primarily
transportation, with implications for others. Three analyses are
presented for infrastructure network changes and adaptations.
First, infrastructure age as a risk factor under extreme
conditions is analyzed for U.S. bridge collapses involving
critical network nodes in bridge structures and system-wide
transportation networks. Age and bridge collapse date are
correlated and compared to bridge inventory age. Age is
significant for function and structural deterioration but not as
much for extreme events, since age may not reflect non-age
related factors, e.g., design flaws, misuse, and conditional or
combined probabilities of separate conditions. Second,
transportation users with low incomes may be more vulnerable
to disconnected links in transportation systems affecting their
mobility to jobs during extreme event travel disruptions. For
selected urban areas, percent changes in low income
populations are compared to rail transit availability. Third,
disaster-related network breakages with widespread impacts on
users are identified. For such conditions, network-related risk
reduction measures for rail transit are presented, e.g.,
redundant/alternative routing or links, interconnected nodes,
and multiple travel modes. Where transportation disruptions
involve energy or water failures, alternatives to strengthen
power/transportation nodes and alleviate flooding are examined
in networks terms. These findings have widespread implications
for network-based infrastructure risk policies for user and
system risks in extreme events and climate change disruptions.
Findings extend research from the author’s book, Transport,
the Environment and Security (2012) and are illustrated using
extreme U.S. weather events, including Hurricanes Sandy and
Irene.
December 8-11, 2013 - Baltimore, MD