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

SRA 2013 Annual Meeting Abstracts
P.4 Convertino, MC*; Liang, SL; University of Minnesota ;
matteoc@umn.edu
Food Safety? A Supply Chain Matter: Probabilistic Risk
Model based on the Agro-Food Trade Network
Food safety is a major issue for the worldwide population.
Foodborne outbreaks in the USA caused in 2010 a cost of $ 152
billion related to 325,000 hospitalized persons and 5000 deaths
due to a foodborne illness. To fight this increasing trend a
risk-based system built upon data-driven analysis to inform the
efficient targeting of efforts to minimize foodborne risks to the
US consumer. Here we propose a model for the assessment of
the potential total health risk of food based on the food supply
chain (FSC) as a subset of the international agro- food trade
network. The number of connected countries, the betweenness
centrality of the exporting countries, and the average path
length are the supply network variables considered.
Considering the safety of each country and the network
variables we introduce a global safety index (GSI) for
characterizing the riskiness of each country based on local and
FSC variables. The intermediary country risk, the
food-pathogen health risk, and the company reliability are the
second most important factors for the total health risk. Policies
that act on both the supply chain variables and the safety index
by means of the GSI reduce of 44% the average total health
risk. This reduction is much larger than the reduction of
policies focused on individual risk factors of the food life-cycle.
The proposed FSC model is scalable to any level of the global
food system and of- fers a novel perspective in which the global
public health is conceived, monitored and regulated.
P.41 Convertino, MC*; Munoz-Carpena, RMC; Kiker, GK; Perz,
SP; University of Minnesota; matteoc@umn.edu
Metacommunity Resilience of the Amazon Tropical Forest
Facing Human and Natural Stressors
Climate extremes and rapid urbanization are stressors that
both shape and threat ecosystems. Thus, questions arise about
future scenarios for ecosystems and how we as society can
potentially control ecosystem evolution considering natural
variability and human needs. Here we reproduce biodiversity
patterns of the Amazon’s MAP (Madre de Dios - Acre - Pando)
tropical rainforest affected by the construction of the
transoceanic highway and climate change with a neutral
metacommunity model at different scales and resolutions. The
influence of environmental variability in species loss and
richness increases with scale and decreases with tree
clumpiness heterogeneity. At the ecosystem scale drought
sensitivity is 37 % higher than at the plot scale where the
difference in scales is of seven orders of magnitude.
Conversely, the anthropic disturbance played by the road is
much larger at the plot scale, and undetectable at the
ecosystem scale because dispersal is not affected. A non trivial
pattern is found between the species cluster size and the
persistence time. Bimodal distributions of clumpiness results in
highly stable species richness and persistence time
distributions. The species persistence time follows a power law
function whose exponent increases with the magnitude of
disturbance. This power law is preserved together with the
distribution of tree cover despite changes in the shape of the
species richness distribution. We propose the product of the
persistence time, its probability of occurrence and the average
species cluster size as a measure of metacommunity risk of
ecosystems as a function of its resilience. A spatial resilience
index as ratio of metacommity risks in the disturbed and
undisturbed case is calculated to identify the most resilient
communities. Our results show that societal development
pressure should consider the ecosystem tree distribution of to
minimize and maximize biodiversity loss and persistence time,
respectively. The spatial resilience index can be used to plan
agricultural and urban expansion that preserve resilient
communities.
T5-C.1 Cox, T; Cox, Tony; Cox Associates and University of
Colorado; tcoxdener@aol.com
Possible Futures for Risk Analysis
Both the field and the journal Risk Analysis have made major
contributions to risk-informed policy making in recent years.
Demand is high for skilled assessment and communication of
risks and uncertainties, creation and validation of trustworthy
risk models, less expensive and more effective procedures to
implement risk management guidelines, and better principles
and methods for deciding what to do when not enough is known
to formulate a conventional decision analysis model. Risk
analysis is responding to these and other vital conceptual and
practical challenges, and will continue to do so. New and
rapidly growing areas, such as infrastructure risk analysis, are
likely to add to the importance and impact of the journal. At the
same time, the very success of the field has created important
challenges to its long-term integrity and credibility. Strong
demand for risk-informed decision-making has encouraged
short-cuts and methods of dubious reliability which are still
branded as risk analyses. Among these are use of unvalidated
guesses and input assumptions elicited from selected experts;
use of unvalidated statistical and computer simulation risk
models and risk projections; presentation of
authoritative-looking outcome probabilities and confidence
intervals which hide key uncertainties about their underlying
assumptions; and ad hoc risk scoring, rating, and ranking
procedures deployed without critically assessing whether they
actually lead to improved risk management decisions. A strong
journal will help to overcome these and other methodological
and practical challenges. By showcasing excellent work,
encouraging the development and application of sound
methods, and explaining and exposing important risk analyses
to the scrutiny of a community which cares about genuinely
trustworthy and valuable analysis, we can dodge the pitfalls
and magnify the value of risk analysis in supporting better
policy decisions.
M3-C.2 Cox, T; Cox Associates and University of Colorado;
tcoxdenver@aol.com
Adapting Risk Management to Reduce Regret
Two principles for choosing among alternative risk
management policies are: (a) Seek to maximize ex ante
expected social utility (roughly equivalent to expected net
benefit); and (b) Seek to minimize ex post regret, defined as the
difference between the maximum value (or net benefit) that
could have been achieved, as assessed in hindsight, and the
value that actually was achieved. We show that these two
principles typically lead to different recommended choices, for
both individuals and groups, especially when there are
uncertainties or disagreements about probabilities or
preferences. Under these realistic conditions of conflict and
uncertainty, effective policy-making requires learning to make
choices that adaptively reduce or minimize regret.
Risk-cost-benefit and expected utility maximization principles
that instead seek to identify the best next action, using
realistically imperfect information, are subject to over-fitting
and other biases that typically over-estimate the net benefits
from costly interventions. We discuss conditions under which
policy-making can be improved by switching from trying to
maximize expected net benefits to trying to minimize ex post
regret. This change helps to resolve some long-standing
difficulties in risk-cost-benefit analysis, such as how to avoid
over- or under-discounting of far future effects and how to
decide what to do when future preferences and effects of
current actions are highly uncertain.
December 8-11, 2013 - Baltimore, MD