17,533 research outputs found
Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective
Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada
Quantifying uncertainty in pest risk maps and assessments : adopting a risk-averse decision maker’s perspective
Pest risk maps are important decision support tools when devising strategies to minimize introductions of invasive organisms and mitigate their impacts. When possible management responses to an invader include costly or socially sensitive activities, decision-makers tend to follow a more certain (i.e., risk-averse) course of action. We presented a new mapping technique that assesses pest invasion risk from the perspective of a risk-averse decision maker. We demonstrated the method by evaluating the likelihood that an invasive forest pest will be transported to one of the U.S. states or Canadian provinces in infested firewood by visitors to U.S. federal campgrounds. We tested the impact of the risk aversion assumption using distributions of plausible pest arrival scenarios generated with a geographically explicit model developed from data documenting camper travel across the study area. Next, we prioritized regions of high and low pest arrival risk via application of two stochastic ordering techniques that employed, respectively, first- and second-degree stochastic dominance rules, the latter of which incorporated the notion of risk aversion. We then identified regions in the study area where the pest risk value changed considerably after incorporating risk aversion. While both methods identified similar areas of highest and lowest risk, they differed in how they demarcated moderate-risk areas. In general, the second-order stochastic dominance method assigned lower risk rankings to moderate-risk areas. Overall, this new method offers a better strategy to deal with the uncertainty typically associated with risk assessments and provides a tractable way to incorporate decisionmaking preferences into final risk estimates, and thus helps to better align these estimates with particular decision-making scenarios about a pest organism of concern. Incorporation of risk aversion also helps prioritize the set of locations to target for inspections and outreach activities, which can be costly. Our results are especially important and useful given the huge number of camping trips that occur each year in the United States and Canada
Scaling properties and universality of first-passage time probabilities in financial markets
Financial markets provide an ideal frame for the study of crossing or
first-passage time events of non-Gaussian correlated dynamics mainly because
large data sets are available. Tick-by-tick data of six futures markets are
herein considered resulting in fat tailed first-passage time probabilities. The
scaling of the return with the standard deviation collapses the probabilities
of all markets examined, and also for different time horizons, into single
curves, suggesting that first-passage statistics is market independent (at
least for high-frequency data). On the other hand, a very closely related
quantity, the survival probability, shows, away from the center and tails of
the distribution, a hyperbolic decay typical of a Markovian dynamics
albeit the existence of memory in markets. Modifications of the Weibull and
Student distributions are good candidates for the phenomenological description
of first-passage time properties under certain regimes. The scaling strategies
shown may be useful for risk control and algorithmic trading.Comment: 7 pages, 5 figure
Rank-Ordering Statistics of Extreme Events: Application to the Distribution of Large Earthquakes
Rank-ordering statistics provides a perspective on the rare, largest elements
of a population, whereas the statistics of cumulative distributions are
dominated by the more numerous small events. The exponent of a power law
distribution can be determined with good accuracy by rank-ordering statistics
from the observation of only a few tens of the largest events. Using analytical
results and synthetic tests, we quantify the systematic and the random errors.
We also study the case of a distribution defined by two branches, each having
a power law distribution, one defined for the largest events and the other for
smaller events, with application to the World-Wide (Harvard) and Southern
California earthquake catalogs. In the case of the Harvard moment catalog, we
make more precise earlier claims of the existence of a transition of the
earthquake magnitude distribution between small and large earthquakes; the
-values are for large shallow earthquakes and for smaller shallow earthquakes. However, the cross-over
magnitude between the two distributions is ill-defined. The data available at
present do not provide a strong constraint on the cross-over which has a
probability of being between magnitudes and for shallow
earthquakes; this interval may be too conservatively estimated. Thus, any
influence of a universal geometry of rupture on the distribution of earthquakes
world-wide is ill-defined at best. We caution that there is no direct evidence
to confirm the hypothesis that the large-moment branch is indeed a power law.
In fact, a gamma distribution fits the entire suite of earthquake moments from
the smallest to the largest satisfactorily. There is no evidence that the
earthquakes of the Southern California catalog have a distribution with tw
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