These inputs may include data deriving from previous programmes, special skills required to process complex models, knowledge about unknown phenomena which are univocally determined by the specific program, and above all availability of time and money. Experts through their knowledge, accumulated after years of experience and observations, are able to understand and assess phenomena by quickly providing data and info which could not be obtained differently.
When deep analyses, data or theories are not available, expert judgement becomes the most important instrument of risk analysis. Step 1 : Expert Motivation The aim of this step is the establishment for the elicitor of a confident relationship with the expert. Before starting discussion, it is useful for the elicitor to obtain information about the expert's past work experience and carry out the interview in a place where the expert can access materials relevant for the investigation. At the beginning, the elicitor will explain the problem to be investigated and how the analysis will be conducted.
Key sub-step of this phase is the identification of any motivational biases e. If such biases are identified, they may be minimised by disaggregating the risk, or by restructuring the risk presented.
Step 2: Structuring of the elicitation process The elicitation structuring phase consists of four sub-steps: 1 setting of the variable; 2 identification of a range of results; 3 disaggregation of variables if required; 4 selection of an adequate unit of measurement. The purpose of the elicitor is to obtain an unambiguous specification of the quantity to be assessed. The quantity will be specified as clearly as to allow the expert to forecast future scenarios and identify the value which this quantity might assume.
Step 3: Conditioning the expert's judgement In this phase the elicitor will induce the expert to take into consideration all the relevant information he possesses about the uncertain variable. The expert will be induced to consider either the information directly relevant to the risk concerned or the information he possesses about similar cases.
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The expert might be requested to react to different scenarios proposed by the elicitor, or to forecast those scenarios in extreme situations. Or else the same problem might be proposed in different forms. Step 4: Encoding the probability distribution. The purpose of encoding the event probability distribution is to obtain a quantitative description of the subjective probability distribution which better reflects the range of values identified by the expert.
The encoding phase will better start from the extreme values of the distribution. To this purpose the first question for the expert to be asked concerns which values may be regarded as extreme. Through direct techniques experts are requested to answer the elicitor's questions by providing figures. The expert will therefore be requested to carry out a frequency analysis though restricted to few scenarios.
Elicitation of Expert Opinions for Uncertainty and Risks | Taylor & Francis Group
The answers obtained will allow the identification of points allowing the plotting of a diagram representing the subjective probability distributions of the variable identified in step 2. That will be obtained by interpolation, so the higher the number of answers obtained from the expert is, the greater the distribution accuracy will be.
Diagrammatic procedures. By following a diagrammatic procedure the expert selects a pdf diagram which better represents the trend of the variable concerned. Starting from a uniform probability distribution, it is possible by subsequent steps to model the probability distribution more and more accurately thanks to the answers the expert provides to specific questions asked by the elicitor.
The diagrammatic procedure will be better applied if the expert is familiar with the probabilistic risk assessment theories which help understand and select the parameters concerned. In this way, once a priority order has been set of the events probably occurring, relative probability values may be assigned to them thus allowing to draw the probability density function researched.
The expert will be allowed to distinguish among different possible scenarios and compare one with another. Tree graphical representations of events are constructed thanks to information obtained by forward reasoning. Starting from the main risk event, the tree is developed through subsequent branches identifying specific subevents whose probability of occurrence directly leads to the occurrence probability of the main event.
The result investigated on applying this technique consists in the logic determining the risk scenario and especially the probability of occurrence of the event. Any sub-event will then be applied one of the preceding encoding technique in order to identify a probability distribution. The first aspect to consider in designing a method for the elicitation of expert knowledge, is to identify the experts, to evaluate his level of expertise.
When the assessment parameter employed is single and objective, it is easy and effective to determine people expertise on the basis of a score. Unfortunately in most cases expertise boundaries are unclear. Using a single parameter to measure people expertise might not be substantiated; so it will be necessary to adopt different criteria. The best way to assess a group of experts is attributing them a relative weight obtained by individually comparing the same parameters by the AHP 1 method. The expert's knowledge of the undesirable events occurring and their consequences highly depends on the type of events occurring.
For this reason, knowledge about undesirable events may be classified into four categories as follow:. Each of the previously presented elicitation techniques requires a specific type of input data to be implemented. These data consist in information obtained by the experts and so depend on the experts' knowledge; this implies that the type of technique to be adopted will depend on the expert's knowledge available:. The method for the elicitation of expert knowledge will be based on the following assumptions:.
The development of the method proposed is represented as a flow chart in Exhibit 1. The method includes 4 macrophases: A identification of risks and of relevant experts; B a process of knowledge elicitation; C explicitation of the single risk; D explicitation of the program risk. Risk identification. Members of the program team including the elicitor, will meet in a brainstorming meeting. By analysing the WBS work breakdown structure , processes and check lists available, they will identify potential hazards listing them in a watch list where the same will be matched to the names of the relevant experts.
Personal data about the expert involved years of experience in the area concerned, degree of relation between the position occupied and the hazard concerned which will be described and scored. Now, the expert knowledge elicitation process has been completed. A new expert will be now questioned, by considering the same line of matrix M so as to collect other data about the same risk.
The process will be repeated till the last expert in the line has been interviewed. Now all the information concerning the risk is available to pass to the following phase. The risk probability distribution r i will therefore be a function of the consequences:. The risk curve therefore represents the risk variation r i as a function of the programme progress:. By repeating the procedure for the n risks of matrix M, it is possible to make the program total risk explicit, that is by representing it with a single probability distribution and a single risk curve.
The case here reported refers to a proposal about a Payload for the international market. This Payload consists of two sections, operating respectively in Ku band and in Ka band. In addition to the Payload, the supply will include a Telemetry Command and Ranging system TCR through which the satellite receives commands from the earth and sends back telemetries information about the satellite status.
The Monte-Carlo simulation on cost-risks has resulted in the distribution reported in Exhibit 2. The outcome of that has been the risk of a penalty distributed as shown in Exhibit 3. Now it is possible to take into account all the aspects of the risks identified. Through a Monte Carlo simulation the program total risk shown in Figure 4 is obtained, whose expected value is approx. Exhibit 5. As concerns the financial risk assessment, it is possible to draw its trend along the whole life-cycle of the programme.
This trend of the risk along time is shown as the red curve representing the program risk in Exhibit 6. The program risk curve aggregates four curves representing four separate risk subsets as follow: 1 risks of penalties for the repeater; 2 risks of penalties for the antennas; 3 risks for the repeater; 4 risks for the antennas. In T 0 all the programme risks converge, as their potential occurrence is very high at this time. After this time, the risk depending on the availability of the equipment required in T 0 , does not exist any more and so the programme risk curve decreases accordingly.
In the first curve constant phase, all the remaining risk potentialities aggregate at the end of which risks connected to the engineering phase , both as concerns the antennas and the repeater, may disappear or arise in a three month period. After this time, the second constant phase of the Manufacturing and Test curve starts.
In this phase the curves of risks to the repeater remain over those affecting the antennas.
Risks in this phase have a higher degree of occurrence probability mainly due to delays generating penalties. For this reason, at the end of this phase, in a period of six months at the latest, they will disappear completely sharply reducing the programme total risk. At this time both the Payload consignments will have been delivered to the Customer and the only risk left will be relative to maintenance.
The application of the methodology as here suggested has proved that the approach is both effective. The method, in fact, with very short implementation times, succeeds in eliciting from the expert relevant data and information about the real trend of the risk which the expert was not aware of possessing. Each interview takes about twenty minutes to be performed. During this lapse of time it is also possible to verify the flexibility of the method and its capcity of adapting to any risk environment.
In order to guarantee that the final results truly represent the trend of the risk, the method proposes to elicit the knowledge of more experts. In this way the assumptions made about the trend of the risk may be confirmed. Abrahamsson, M. Retrieved on November 09, from: www. Ayyub, B. Methods for expert-opinion elicitation of probabilities and consequences for corps facilities.
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DoD February. Risk management guide for DOD acquisition. Federal Aviation Administration December 20, This will lead to an increased reliance on the proper treatment of uncertainty and on the use of expert opinions. Elicitation of Expert Opinions for Uncertainty and Risks will help prepare you to better understand knowledge and ignorance, to successfully elicit expert opinions, to select appropriate expressions of those opinions, and to use various methods to model and aggregate opinions.
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