Risk and related concepts - exam questions Flashcards
Suppose we define risk = “the probability of an unfortunate event occurring”. Present an argument against this definition of risk.
The definition does not highlight the knowledge supporting the probability. Another argument is that it does not reflect the consequences of the event
Suppose we define risk = “Value at Risk”. Present two arguments against this definition of risk. (Is this irrelevant?)
Value at risk does not say anything about the “shape” of the distribution beyond the quantile value. 2) Nor does it reflect the knowledge which this number is based on.
Explain the concept of resilience.
Resilience is a system’s ability to sustain and restore its functionality given some disruption, threat, hazard and/or opportunity
Give an example how risk considerations can contribute to resilience.
One example is hazard/threat identification. This helps identifying what disruptions, threats, hazards and/or opportunities that may occur.
If a system is judged antifragile, would you say that there is still risk for bad outcomes?
Why/why not?
Yes, there is still risk as there is still a potential for bad outcomes, negative consequences and associated uncertainties.
A risk analyst has introduced a Bernoulli probability model and estimated the parameter p, the failure probability of a safety barrier given an initiating event A, to be p*= 0.015.
Provide an interpretation of p
Is it correct to say that p* is a measure of the analyst’s uncertainty? Why/why not?
The analyst further states that she has confidence in the estimate as she has generated the quite narrow 90% confidence interval [0.013, 0.017].
How should her statement be interpreted?
The parameter p is interpreted as the fraction of times the safety barrier fails given event A, if the situation was repeated over and over again, towards infinity.
No, p* is the analyst’s best estimate of p, and as such, is not a measure of the analyst’s
uncertainty.
If the “experiment” of generating the interval was repeated over and over again, towards infinity, p would be in the interval 90% of the times. When the interval generated is narrow, the analyst feels confident that p* is quite close to p.
Explain how risk is defined in curriculum.
We consider a future activity [interpreted in a wide sense to also cover, for example, natural phenomena], for example the operation of a system, and define risk in relation to the consequences (effects, implications) of this activity with respect to something that humans value. The consequences are often seen in relation to some reference values (planned values, objectives, etc.), and the focus is often on negative, undesirable consequences. There is always at least one outcome that is considered as negative or undesirable. Risk is the consequences C of the activity and associated uncertainties U.
Give also a general risk description and present briefly the key components.
What are the main benefits of such a set-up, i.e. distinguishing between how we define risk and how we describe risk?
A general risk description is the triplet (C’,Q,K), where C’ is some specified consequences, Q a measure of uncertainty associated with C’ (typically probability), and K the background knowledge that supports C’ and Q (which includes a judgment of the strength of this knowledge)
This allows for different descriptions of risk to be used depending on the situation at hand. It also stimulate discussion and reflections what is a good description of risk
Besides being able to label past events, how does the Black swan metaphor as defined by Aven improve risk analysis?
Besides being able to label past events, the Black swan metaphor as defined by Aven improve risk analysis by putting more focus on the knowledge supporting the risk description. It suggests that surprises relative to one’s beliefs may occur which in terms means that the knowledge supporting the risk description should be highlighted/investigated. A key point is that the knowledge could be wrong, and surprises occur. The focus on the metaphor helps us addressing that type of issue and risk
A risk analyst has introduced a Bernoulli probability model with parameter p, the probability that a new type of law will be passed. The analyst has further specified a 90% credibility interval for p, to inform the management of a company that will be affected by the law.
A) Do you agree with this approach? Why/why not?
B) Generally, in what sense does an approach like this (Bernoulli probability model with parameter p) address both variation and uncertainty? Illustrate with an example.
The approach should not be used in this case. It assumes that there exists a true frequentist probability of the law being passed. However, this is a unique situation where a frequentist probability cannot be meaningfully defined or estimated.
We can think an experiment where a pin is thrown and either will land with the tip down or up. In such a case, the parameter p reflects the variation in the outcomes if the experiment was repeated a huge number of times. The credibility interval reflects my uncertainty about the value of p
A person states that the probably that the future global temperature will increase with more than 1 degree Celsius in the coming 50 years is at least 0.90. Provide an explanation/interpretation of this probability statement. What do we call this type of probability?
Is there uncertainty about this probability relative to an underlying ‘true’ probability? Explain
It is an imprecise knowledge-based (subjective, judgmental) probability; it expresses that the person has the same uncertainty or degree of belief for this event (the future global temperature will increase with more than 1 degree Celsius in the coming 50 years) to occur as randomly drawing a red ball out of an urn comprising 100 balls where at least 90 are red.
No, as there is no underlying true probability to compare with. However, the supporting knowledge could be more or less strong and even wrong.
Use the boulder example from the curriculum to explain what vulnerability means. Use both symbols and daily language.
Discuss in general to what extent a system that is considered not vulnerable (i.e. is robust) could have undesirable consequences for some possible events A.
A: boulder dislodges from the ledge
Vulnerability: (C,U|A) potential for fatalities or injuries given the boulder is dislodged from the ledge – combination of fatalities/injuries C and associated uncertainties U, given event A
Two main points:
i) not vulnerable (robust) means that the conditional ‘risk’ (description), (C’,Q,K|A’), is
judged small for a given A’ - and hence not likely undesirable consequences occur - but there is some probability so undesirable consequences may occur, also surprises may occur relative to the knowledge of the analysts leading to undesirable consequences.
ii) for some As, we cannot require i) but the total risk contribution (A’,Q,K) should be judged small - for such As it could be rather likely to have undesirable consequences.
In a specific industry, a total number of 4 fatalities were registered last year. You talk to a risk analyst about the related risks.
This analyst states
a) Risk is thus equal to 4.
A colleague of this analyst disagrees and states that
b) 4 is a risk estimate
Do you consider these two statements to be in line with risk science as defined by the course curriculum? Why?/why not? Answer a) and b) separately
No, as risk is not historical data, risk relates to the future, 4 can provide input to describe risk but there is a fundamental leap between what is observed in the past and what will happen in the future. Risk is a combination of future unknown consequences C, and associated uncertainties, and clearly 4 is different from (C,U).
No, we do not estimate the risk (C,U), but we describe it by (C’,Q,K). 4 can be seen as
an estimate of C, but C is not the same as risk. If risk were defined by the expected
number of fatalities (using frequentist probabilities), we could have spoken about 4
being an estimate of risk, but that is not as defined in the curriculum.
Some analyst also states that:
c) If the uncertainties are large probabilities cannot be determined, and
again the colleague of this analyst disagrees and states that
d) frequentist probabilities can be defined when the uncertainties are large.
Do you consider these statements to be in line with risk science as defined by the course curriculum? Why?/why not? Answer c) and d) separately
No, subjective (knowledge-based) probabilities can always be determined/assigned
(although the supporting knowledge is weak).
A frequentist probability is a model concept and needs to be justified, for unique
situations it cannot be meaningfully defined, as the concept needs repetition of the
situation considered over and over again infinitely.
What is ‘risk science’? Are all risk assessments contributing to new risk science
knowledge? Why/why not?
Risk science is the most warranted statements (justified beliefs) produced by the risk field or discipline, it is about concepts, principles, approaches and methods on how to understand, assess, characterise, communicate, manage and govern risk
Risk science is the most updated and justified knowledge on risk fundamentals (concepts), risk assessment, risk perception and communication and risk management and governance. Risk science is also about the process – the practice – that gives us this knowledge.
No, as not all lead to new knowledge on concepts, principles, approaches or methods on how to understand, assess, characterise, communicate, manage or govern risk. Not all risk assessment are published in risk science journals, they do not have scientific contributions in relation to risk science
