Statistical Characterization of Hazards and Risk in Coastal Areas 26.1 Overview of Risk and Uncertainty 569
Part C | 26.1
we would collect information on whether it rained
on any given day for a period of time (say 1 year).
The probability of rain would then be
probability of precipitation
D
number of days with precipitation
Total number of days
:
Probability distribution: The basic definition of
probability works for discrete events, but when the
event or parameter being evaluated is continuous in
nature (i. e., the maximum temperature on any given
day, the stage of a river on any given day), then
a single probability is not sufficient. Instead, possible values are grouped into discrete ranges and the
number of occurrences in that range are counted,
then divided by the total number of measurements.
Risk: The potential for realization of unwanted, adverse consequences to human life, health, property,
or the environment. The estimation of risk is usually
quantified using the expected value of the conditional probability of the event occurring times the
consequence of the event given that it has occurred.
This definition is currently used by the Society of
Risk Analysis. Mathematically, this is expressed as
R D P.A/ P.BjA/ ;
where R – Risk (a probability from 0 to 1), P.A/ –
probability of event (A) occurring, P.BjA/ – probability of a consequence (B) occurring given that
event A occurred.
This technical definition is what will be used
through this handbook. However, there are other
definitions in other fields of which the knowledgeable practitioner should be aware (Sect. 26.A). Besides having multiple potential definitions, risk can
also be differentiated by types. Different types of
risk, even if they are of the same quantitative value,
are often managed differently or even ignored.
Some of the common risk types that greatly influence both how these risks are assessed, managed
and communicated are defined below (Sect. 26.A
for full list).
– Actual risk: A scientifically verifiable risk.
For example, it is well researched and documented that smoking places you at-risk for
cancer [26.3–6]. Actual risk is sometimes referred to as objective risk, but whether risk is
subjective or objective is related more to its
ability to be measured than it is to its actual verifiability.
– Perceived risk: Risk that is thought to exist by
an individual or group that either is understated
or is exaggerated. This often occurs in situations
where the public is misinformed or in which
media reports instill unnecessary panic. Food
safety concerns often top the list of such events
and lead to significant public policy debates,
see, for example [26.7].
– Assumed risk: Risk that is taken by choice. Assumed risk can be quantifiably large or small,
and actual or perceived. For example, individuals who choose to partake in risky activities
(skydiving, mountain climbing) choose to assume the relatively large risks associated with
these activities, but choosing to drive a car, take
medicines, or be involved in day-to-day activities all involve some assumed risk.
Imposed risk: Risk that is forced upon an individual,
either without the knowledge of the individual or if
known, without consent. For example, second-hand
smoke exposure is seen as an imposed risk [26.8–
10]. Natural events such as earthquakes, tropical
cyclones (e.g., hurricanes, typhoons), and extreme
weather events are to a large extent imposed risks,
but to some extent individuals assume that risk
based on where they choose to live. For an interesting approach to this, see the work by [26.11].
Uncertainty: Uncertainty arises from the fact that
any model (including risk assessment models) cannot provide 100% certainty in its results. This uncertainty arises from two major sources.
– Epistemic uncertainty: Epistemic uncertainty
arises from the fact that there are things about
the natural system we are analyzing that we
do not know. This lack of knowledge can be
because the knowledge is not yet scientifically
available – and as such, it can be reduced (along
with the resulting epistemic uncertainty) going
forward through additional data, experimentation, theoretical development, and scientific inquiry. However, lack of knowledge also includes
things that we do not even know that we do not
know. This area of lack of knowledge is more
difficult, because it is not easily identifiable and
as such, becomes included in either variability,
or is called error when comparing model results
to reality.
– Alleatory uncertainty: Variability is a range of
potential values for a given parameter. Variability is an inherent characteristic of natural
processes (also called natural variation). It is
describable using probability distributions. The
result of natural variation is sometimes called
alleatory uncertainty. Variability in natural processes (and the resulting alleatory uncertainty)
cannot be reduced, as it is an inherent property
of the process itself.
Part C | 26.1
we would collect information on whether it rained
on any given day for a period of time (say 1 year).
The probability of rain would then be
probability of precipitation
D
number of days with precipitation
Total number of days
:
Probability distribution: The basic definition of
probability works for discrete events, but when the
event or parameter being evaluated is continuous in
nature (i. e., the maximum temperature on any given
day, the stage of a river on any given day), then
a single probability is not sufficient. Instead, possible values are grouped into discrete ranges and the
number of occurrences in that range are counted,
then divided by the total number of measurements.
Risk: The potential for realization of unwanted, adverse consequences to human life, health, property,
or the environment. The estimation of risk is usually
quantified using the expected value of the conditional probability of the event occurring times the
consequence of the event given that it has occurred.
This definition is currently used by the Society of
Risk Analysis. Mathematically, this is expressed as
R D P.A/ P.BjA/ ;
where R – Risk (a probability from 0 to 1), P.A/ –
probability of event (A) occurring, P.BjA/ – probability of a consequence (B) occurring given that
event A occurred.
This technical definition is what will be used
through this handbook. However, there are other
definitions in other fields of which the knowledgeable practitioner should be aware (Sect. 26.A). Besides having multiple potential definitions, risk can
also be differentiated by types. Different types of
risk, even if they are of the same quantitative value,
are often managed differently or even ignored.
Some of the common risk types that greatly influence both how these risks are assessed, managed
and communicated are defined below (Sect. 26.A
for full list).
– Actual risk: A scientifically verifiable risk.
For example, it is well researched and documented that smoking places you at-risk for
cancer [26.3–6]. Actual risk is sometimes referred to as objective risk, but whether risk is
subjective or objective is related more to its
ability to be measured than it is to its actual verifiability.
– Perceived risk: Risk that is thought to exist by
an individual or group that either is understated
or is exaggerated. This often occurs in situations
where the public is misinformed or in which
media reports instill unnecessary panic. Food
safety concerns often top the list of such events
and lead to significant public policy debates,
see, for example [26.7].
– Assumed risk: Risk that is taken by choice. Assumed risk can be quantifiably large or small,
and actual or perceived. For example, individuals who choose to partake in risky activities
(skydiving, mountain climbing) choose to assume the relatively large risks associated with
these activities, but choosing to drive a car, take
medicines, or be involved in day-to-day activities all involve some assumed risk.
Imposed risk: Risk that is forced upon an individual,
either without the knowledge of the individual or if
known, without consent. For example, second-hand
smoke exposure is seen as an imposed risk [26.8–
10]. Natural events such as earthquakes, tropical
cyclones (e.g., hurricanes, typhoons), and extreme
weather events are to a large extent imposed risks,
but to some extent individuals assume that risk
based on where they choose to live. For an interesting approach to this, see the work by [26.11].
Uncertainty: Uncertainty arises from the fact that
any model (including risk assessment models) cannot provide 100% certainty in its results. This uncertainty arises from two major sources.
– Epistemic uncertainty: Epistemic uncertainty
arises from the fact that there are things about
the natural system we are analyzing that we
do not know. This lack of knowledge can be
because the knowledge is not yet scientifically
available – and as such, it can be reduced (along
with the resulting epistemic uncertainty) going
forward through additional data, experimentation, theoretical development, and scientific inquiry. However, lack of knowledge also includes
things that we do not even know that we do not
know. This area of lack of knowledge is more
difficult, because it is not easily identifiable and
as such, becomes included in either variability,
or is called error when comparing model results
to reality.
– Alleatory uncertainty: Variability is a range of
potential values for a given parameter. Variability is an inherent characteristic of natural
processes (also called natural variation). It is
describable using probability distributions. The
result of natural variation is sometimes called
alleatory uncertainty. Variability in natural processes (and the resulting alleatory uncertainty)
cannot be reduced, as it is an inherent property
of the process itself.