The Experts below are selected from a list of 297 Experts worldwide ranked by ideXlab platform
D. E. Medjadi - One of the best experts on this subject based on the ideXlab platform.
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Single particle calculations for a Woods–Saxon potential with triaxial deformations, and large Cartesian oscillator basis (new version code)
Computer Physics Communications, 2007Co-Authors: B. Mohammed-azizi, D. E. MedjadiAbstract:Abstract We present a new version of the computer program which solves the Schrodinger equation of the stationary states for an average nuclear potential of Woods–Saxon type. In this work, we take specifically into account triaxial (i.e. ellipsoidal) nuclear surfaces. The deformation is specified by the usual Bohr parameters. The calculations are carried out in two stages. In the first, one calculates the representative matrix of the Hamiltonian in the Cartesian oscillator basis. In the second stage one diagonalizes this matrix with the help of subroutines of the EISPACK library. This new version calculates all the eigenvalues up to a given cutoff energy, and gives the components of the corresponding eigenfunctions. For a more convenient handling, these results are stored simultaneously in the computer memory, and on a files. Program summary Title of program:Triaxial2007 Catalogue identifier:ADSK_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADSK_v2_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Summary of revision:One input file instead two. Reduced Number of input parameters. Storage of eigenvalues and eigenvectors in memory in a very simple way which makes the code very convenient to the user. Reasons for the new version: More convenient handling of the eigenvectors Catalogue Number old version: ADSK Catalogue Number new version:ADSK_v2_0 Journal: Computer Physics Commun. 156 (2004) 241–282 Licensing provisions: none Computer: PC Pentium 4, 2600 MHz Hard disk: 40 Gb RAM: 256 Mb Swap file: 4 Gb Operating system: WINDOWS XP Software used: Compaq Visual FORTRAN (with full optimizations in the settings project options) Programming language used:Fortran 77/90 (double Precision) Number of bits in a word: 32 No. of lines in distributed program, including test data, etc.:4058 No. of bytes in distributed program, including test data, etc.:75 590 Distribution format:tar.gz Nature of the problem: The single particle energies and the single particle wave functions are calculated from one-body Hamiltonian including a central field of Woods–Saxon type, a spin–orbit interaction, and the Coulomb potential for the protons. We consider only ellipsoidal (triaxial) shapes. The deformation of the nuclear shape is fixed by the usual Bohr parameters ( β , γ ) . Method of solution: The representative matrix of the Hamiltonian is built by means of the Cartesian basis of the anisotropic harmonic oscillator, and then diagonalized by a set of subroutines of the EISPACK library. Two quadrature methods of Gauss are employed to calculate, respectively, the integrals of the matrix elements of the Hamiltonian, and the integral defining the Coulomb potential. Restrictions: There are two restrictions for the code: The Number of the major shells of the basis does not have to exceed N max = 26 . For the largest values of N max (∼23–26), the diagonalization takes the major part of the running time, but the global run-time remains reasonable. Typical running time: (With full optimization in the project settings of the Compaq Visual Fortran on Windows XP) With N max = 23 , for the neutrons case, and for both parities, the running time is about 40 sec on the P4 computer at 2.6 GHz. In this case, the calculation of the matrix elements takes only about 17 sec. If all unbound states are required, the runtime becomes larger.
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Single particle calculations for a Woods–Saxon potential with triaxial deformations, and large Cartesian oscillator basis
Computer Physics Communications, 2004Co-Authors: B. Mohammed-azizi, D. E. MedjadiAbstract:Abstract We present a computer program which solves the Schrodinger equation of the stationary states for an average nuclear potential of Woods–Saxon type. In this work, we take specifically into account triaxial (i.e. ellipsoidal) nuclear surfaces. The deformation is specified by the usual Bohr parameters. The calculations are carried out in two stages. In the first, one calculates the representative matrix of the Hamiltonian in the Cartesian oscillator basis. In the second stage one diagonalizes this matrix with the help of subroutines of the Eispack library. If it is wished, one can calculate all eigenvalues, or only the part of the eigenvalues that are contained in a fixed interval defined in advance. In this latter case the eigenvectors are given conjointly. The program is very rapid, and the run-time is mainly used for the diagonalization. Thus, it is possible to use a significant Number of the basis states in order to insure a best convergence of the results. Program summary Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Title of program: Triaxial Catalogue Number: ADSK Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADSK Licensing provisions: None Computer: PC. AMD Athlon 1000 MHz Hard disk: 40 Go Ram: 256 Mo Swap file: 4 Go Operating system: WINDOWS XP Software used: Microsoft Visual Fortran 5.0A (with full optimizations in the settings project options) Programming language: Fortran 77/90 (double Precision) Number of bits in a word: 32 Number of lines: 7662 No. of bytes in distributed program, including test data, etc.: 174 601 Distribution format: tar gzip file Nature of the problem: The single particle energies and the single particle wave functions are calculated from one-body Hamiltonian including a central field of Woods–Saxon type, a spin-orbit interaction, and the Coulomb potential for the protons. We consider only ellipsoidal (triaxial) shapes. The deformation of the nuclear shape is fixed by the usual Bohr parameters ( β , γ ). Method of solution: The representative matrix of the Hamiltonian is built by means of the Cartesian basis of the anisotropic harmonic oscillator, and then diagonalized by a set of subroutines of the Eispack library. Two quadrature methods of Gauss are employed to calculate respectively the integrals of the matrix elements of the Hamiltonian, and the integral defining the Coulomb potential. Restrictions: There are two restrictions for the code: The Number of the major shells of the basis does not have to exceed Nmax=26. For the largest values of Nmax (∼23–26), the diagonalization takes the major part of the running time, but the global run-time remains reasonable. Typical running time: (With full optimization in the project settings of the Microsoft Visual Fortran 5.0A on Windows XP.) With Nmax=23, for the neutrons case, and for both parities, if we need all eigenenergies and all eigenfunctions of the bound states, the running time is about 80 sec on the AMD Athlon computer at 1 GHz. In this case, the calculation of the matrix elements takes only about 20 sec. If all unbound states are required, the runtime becomes larger.
William B. Runciman - One of the best experts on this subject based on the ideXlab platform.
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A comparison of the performance of 20 pulse oximeters under conditions of poor perfusion.
Anaesthesia, 1991Co-Authors: D. G. Clayton, R K Webb, A. C. Ralston, D. Duthie, William B. RuncimanAbstract:Summary The performance of 20 pulse oximeters with finger probes was evaluated by comparison of their readings with directly measured arterial blood oxygen saturations. The samples were taken from patients who had undergone cardiac surgery under hypothermic cardiopulmonary bypass and had poor peripheral perfusion. The mean difference (bias, accuracy), standard deviation (Precision) and drop-out rate for each pulse oximeter was determined. An overall ranking of performance of each pulse oximeter was calculated using five criteria (accuracy, Precision, Number of readings within 3% of standard, percentage of readings given within 3% of standard, expectedoverread limit in 95% of cases). Two pulse oximeters achieved a combination of accuracy and Precision such that 95% of measurements would be expected to be within 4% of the co-oximeter value; these two also had the lowest dropout rate.
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Pulse oximeter probes. A comparison between finger, nose, ear and forehead probes under conditions of poor perfusion.
Anaesthesia, 1991Co-Authors: D. G. Clayton, R K Webb, A. C. Ralston, D. Duthie, William B. RuncimanAbstract:The performances of 10 pulse oximeters using finger probes were compared with the same pulse oximeters using alternative probes (eight finger probes, two nose probes and a forehead probe) in poorly perfused patients. All readings were then compared with directly measured arterial blood oxygen saturations. The mean difference (bias, 'accuracy'), standard deviation (Precision) and 'drop out' rate for each pulse oximeter combination was determined. An overall ranking of performance of each pulse oximeter was calculated using five criteria (accuracy, Precision, Number of readings within 3% of standard, percentage of readings given within 3% of standard, expected overread limit in 95% of cases). Nose and forehead probes performed poorly. Some ear probes performed well compared to some finger probes, but the overall performance of probes in other sites compared to finger probes was worse, (p = 0.05). Two of eight ear probes and no nose or forehead probes would be expected to be within 4% of the reference value in 95% of readings. The use of finger probes rather than probes in other sites is recommended in the patient with poor peripheral perfusion.
B. Mohammed-azizi - One of the best experts on this subject based on the ideXlab platform.
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Single particle calculations for a Woods–Saxon potential with triaxial deformations, and large Cartesian oscillator basis (new version code)
Computer Physics Communications, 2007Co-Authors: B. Mohammed-azizi, D. E. MedjadiAbstract:Abstract We present a new version of the computer program which solves the Schrodinger equation of the stationary states for an average nuclear potential of Woods–Saxon type. In this work, we take specifically into account triaxial (i.e. ellipsoidal) nuclear surfaces. The deformation is specified by the usual Bohr parameters. The calculations are carried out in two stages. In the first, one calculates the representative matrix of the Hamiltonian in the Cartesian oscillator basis. In the second stage one diagonalizes this matrix with the help of subroutines of the EISPACK library. This new version calculates all the eigenvalues up to a given cutoff energy, and gives the components of the corresponding eigenfunctions. For a more convenient handling, these results are stored simultaneously in the computer memory, and on a files. Program summary Title of program:Triaxial2007 Catalogue identifier:ADSK_v2_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADSK_v2_0 Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Summary of revision:One input file instead two. Reduced Number of input parameters. Storage of eigenvalues and eigenvectors in memory in a very simple way which makes the code very convenient to the user. Reasons for the new version: More convenient handling of the eigenvectors Catalogue Number old version: ADSK Catalogue Number new version:ADSK_v2_0 Journal: Computer Physics Commun. 156 (2004) 241–282 Licensing provisions: none Computer: PC Pentium 4, 2600 MHz Hard disk: 40 Gb RAM: 256 Mb Swap file: 4 Gb Operating system: WINDOWS XP Software used: Compaq Visual FORTRAN (with full optimizations in the settings project options) Programming language used:Fortran 77/90 (double Precision) Number of bits in a word: 32 No. of lines in distributed program, including test data, etc.:4058 No. of bytes in distributed program, including test data, etc.:75 590 Distribution format:tar.gz Nature of the problem: The single particle energies and the single particle wave functions are calculated from one-body Hamiltonian including a central field of Woods–Saxon type, a spin–orbit interaction, and the Coulomb potential for the protons. We consider only ellipsoidal (triaxial) shapes. The deformation of the nuclear shape is fixed by the usual Bohr parameters ( β , γ ) . Method of solution: The representative matrix of the Hamiltonian is built by means of the Cartesian basis of the anisotropic harmonic oscillator, and then diagonalized by a set of subroutines of the EISPACK library. Two quadrature methods of Gauss are employed to calculate, respectively, the integrals of the matrix elements of the Hamiltonian, and the integral defining the Coulomb potential. Restrictions: There are two restrictions for the code: The Number of the major shells of the basis does not have to exceed N max = 26 . For the largest values of N max (∼23–26), the diagonalization takes the major part of the running time, but the global run-time remains reasonable. Typical running time: (With full optimization in the project settings of the Compaq Visual Fortran on Windows XP) With N max = 23 , for the neutrons case, and for both parities, the running time is about 40 sec on the P4 computer at 2.6 GHz. In this case, the calculation of the matrix elements takes only about 17 sec. If all unbound states are required, the runtime becomes larger.
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Single particle calculations for a Woods–Saxon potential with triaxial deformations, and large Cartesian oscillator basis
Computer Physics Communications, 2004Co-Authors: B. Mohammed-azizi, D. E. MedjadiAbstract:Abstract We present a computer program which solves the Schrodinger equation of the stationary states for an average nuclear potential of Woods–Saxon type. In this work, we take specifically into account triaxial (i.e. ellipsoidal) nuclear surfaces. The deformation is specified by the usual Bohr parameters. The calculations are carried out in two stages. In the first, one calculates the representative matrix of the Hamiltonian in the Cartesian oscillator basis. In the second stage one diagonalizes this matrix with the help of subroutines of the Eispack library. If it is wished, one can calculate all eigenvalues, or only the part of the eigenvalues that are contained in a fixed interval defined in advance. In this latter case the eigenvectors are given conjointly. The program is very rapid, and the run-time is mainly used for the diagonalization. Thus, it is possible to use a significant Number of the basis states in order to insure a best convergence of the results. Program summary Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Title of program: Triaxial Catalogue Number: ADSK Program summary URL: http://cpc.cs.qub.ac.uk/summaries/ADSK Licensing provisions: None Computer: PC. AMD Athlon 1000 MHz Hard disk: 40 Go Ram: 256 Mo Swap file: 4 Go Operating system: WINDOWS XP Software used: Microsoft Visual Fortran 5.0A (with full optimizations in the settings project options) Programming language: Fortran 77/90 (double Precision) Number of bits in a word: 32 Number of lines: 7662 No. of bytes in distributed program, including test data, etc.: 174 601 Distribution format: tar gzip file Nature of the problem: The single particle energies and the single particle wave functions are calculated from one-body Hamiltonian including a central field of Woods–Saxon type, a spin-orbit interaction, and the Coulomb potential for the protons. We consider only ellipsoidal (triaxial) shapes. The deformation of the nuclear shape is fixed by the usual Bohr parameters ( β , γ ). Method of solution: The representative matrix of the Hamiltonian is built by means of the Cartesian basis of the anisotropic harmonic oscillator, and then diagonalized by a set of subroutines of the Eispack library. Two quadrature methods of Gauss are employed to calculate respectively the integrals of the matrix elements of the Hamiltonian, and the integral defining the Coulomb potential. Restrictions: There are two restrictions for the code: The Number of the major shells of the basis does not have to exceed Nmax=26. For the largest values of Nmax (∼23–26), the diagonalization takes the major part of the running time, but the global run-time remains reasonable. Typical running time: (With full optimization in the project settings of the Microsoft Visual Fortran 5.0A on Windows XP.) With Nmax=23, for the neutrons case, and for both parities, if we need all eigenenergies and all eigenfunctions of the bound states, the running time is about 80 sec on the AMD Athlon computer at 1 GHz. In this case, the calculation of the matrix elements takes only about 20 sec. If all unbound states are required, the runtime becomes larger.
D. G. Clayton - One of the best experts on this subject based on the ideXlab platform.
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A comparison of the performance of 20 pulse oximeters under conditions of poor perfusion.
Anaesthesia, 1991Co-Authors: D. G. Clayton, R K Webb, A. C. Ralston, D. Duthie, William B. RuncimanAbstract:Summary The performance of 20 pulse oximeters with finger probes was evaluated by comparison of their readings with directly measured arterial blood oxygen saturations. The samples were taken from patients who had undergone cardiac surgery under hypothermic cardiopulmonary bypass and had poor peripheral perfusion. The mean difference (bias, accuracy), standard deviation (Precision) and drop-out rate for each pulse oximeter was determined. An overall ranking of performance of each pulse oximeter was calculated using five criteria (accuracy, Precision, Number of readings within 3% of standard, percentage of readings given within 3% of standard, expectedoverread limit in 95% of cases). Two pulse oximeters achieved a combination of accuracy and Precision such that 95% of measurements would be expected to be within 4% of the co-oximeter value; these two also had the lowest dropout rate.
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Pulse oximeter probes. A comparison between finger, nose, ear and forehead probes under conditions of poor perfusion.
Anaesthesia, 1991Co-Authors: D. G. Clayton, R K Webb, A. C. Ralston, D. Duthie, William B. RuncimanAbstract:The performances of 10 pulse oximeters using finger probes were compared with the same pulse oximeters using alternative probes (eight finger probes, two nose probes and a forehead probe) in poorly perfused patients. All readings were then compared with directly measured arterial blood oxygen saturations. The mean difference (bias, 'accuracy'), standard deviation (Precision) and 'drop out' rate for each pulse oximeter combination was determined. An overall ranking of performance of each pulse oximeter was calculated using five criteria (accuracy, Precision, Number of readings within 3% of standard, percentage of readings given within 3% of standard, expected overread limit in 95% of cases). Nose and forehead probes performed poorly. Some ear probes performed well compared to some finger probes, but the overall performance of probes in other sites compared to finger probes was worse, (p = 0.05). Two of eight ear probes and no nose or forehead probes would be expected to be within 4% of the reference value in 95% of readings. The use of finger probes rather than probes in other sites is recommended in the patient with poor peripheral perfusion.
Steve Begg - One of the best experts on this subject based on the ideXlab platform.
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CogSci - Number Preference, Precision and Implicit Confidence
Cognitive Science, 2011Co-Authors: Matthew Welsh, Daniel J. Navarro, Steve BeggAbstract:Number Preference, Precision and Implicit Confidence Matthew B. Welsh 1 , Daniel J. Navarro 2 & Steve H. Begg 1 ([matthew.welsh, daniel.navarro, steve.begg]@adelaide.edu.au) Australian School of Petroleum, 2 School of Psychology University of Adelaide, North Terrace Adelaide, SA, 5005, Australia Abstract measurements that are presumed to be accurate to ±0.5mm. Thinking in these, pragmatic terms (Sperber & Wilson, 1986), one could conclude, therefore, that the second estimate is, in fact, worse. This is because it is precise to the nearest metre but the true value lies more than 80 metres beyond the 8924.5 to 8925.5m interval resulting from the addition of an appropriate error. The first estimate, by comparison, implies a range of 8.5 to 9.5km and the true value falls well within this. The conclusion to be drawn from the above is that the consideration of Precision can alter our perceptions of accuracy. Although seemingly unremarkable, this has important implications for the way in which we should interpret estimates given by participants during elicitation procedures, as discussed below. In elicitation tasks, people are asked to make estimates under conditions of uncertainty but elicitors then interpret these estimates as if the estimator were certain of them. An analysis of people’s patterns of responding during the elicitation of uncertainty, indicates that there are markers of confidence incorporated into these estimates that can be used to predict the person’s true level of confidence. One such marker is the Precision (Number of significant figures) of the estimate. Analyses of elicited data show the expected positive relationships between accuracy, Precision and explicit confidence and, further, that Precision offers information beyond that of explicit confidence ratings. We then demonstrate the importance of incorporating this information on an overconfidence task, showing that it can account for a 9% difference in calibration. Keywords: Number preference, confidence, Precision, elicitation, judgment and decision making. Elicitation of Uncertainty Introduction Studies of human judgment typically make use of estimates of some quantity given by participants, with a view to assessing the quality of these estimates. However, exactly how to make this assessment is not always straightforward as people’s estimates can contain more information than just a numerical value. For example, imagine that you have asked two individuals how high Mt Everest is. The first answers “9km”; while the second responds “8925 metres”. Later you have the opportunity to check the true answer and find that Mt Everest is 8844.43m high (PRCSBSM, 2005). Which of the two is a better estimate? One answer, of course, is that the second estimate (8925m) is better as it missed the precisely measured value by only 80.57m whereas the first estimate missed by 155.57m. In terms of human interactions, however, the answer is less clear. While the second answer is closer to the true value than the first, it is also far more precise –stating the height to the nearest metre. The first estimate, by comparison, it is stated only to the nearest kilometre. The inference a listener might draw from these different levels of Precision is that the first speaker is giving an approximate height while the second is giving an exact height – an distinction referred to by Yaniv and Foster (1995) as “graininess”. Generally, the less precise an estimate is, then, the less confidence we expect the estimator to have in their estimate being precisely right. This conversational rule mimics the rules of measurement used in the physical sciences where values are given with an error range of ± half the smallest calibration of the measurement device. Thus, a ruler marked in millimetres yields The elicitation of uncertainty describes the process of converting a person’s subjective beliefs regarding uncertain events into a numerical form to allow easier analysis (Wolfson, 2001). Various techniques designed to do this are used where probabilistic forecasting is required in fields such as Petroleum Exploration (Attanasi & Schuenemeyer, 2002), Hydrology (Krzysztofowicz, 2001) and Meteorology (Morgan & Keith, 1995). The technique most commonly used in the oil and gas industry, for example, is the elicitation of 80% confidence ranges (see, e.g., Hawkins, Coopersmith, & Cunningham, 2002). Here the elicitee is asked to give a range of values such that they are 80% certain that the true value of whatever parameter they are estimating will fall within it. Overconfidence The common observation of people using elicitation techniques, however, is that people are overconfident (Lichtenstein, Fischhoff, & Phillips, 1982) – that is, they give ranges that are too narrow, such that values fall outside their 80% ranges more than the expected 20% of times. Given this tendency of people to be overconfident in their estimates, it is not surprising that much of the literature on uncertainty elicitation relates directly to mechanisms for overcoming uncertainty or “debiasing” participants. Various techniques from simple advice to widen ranges (Lichtenstein, et al., 1982) through repeated feedback (Murphy & Winkler, 1977) to the use of probabilistic games (Hawkins, et al., 2002) are recommended. The common observation, however, is that such techniques reduce but do not eliminate overconfidence (Morgan & Henrion, 1990).
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Number preference Precision and implicit confidence
Cognitive Science, 2011Co-Authors: Matthew Welsh, Daniel J. Navarro, Steve BeggAbstract:Number Preference, Precision and Implicit Confidence Matthew B. Welsh 1 , Daniel J. Navarro 2 & Steve H. Begg 1 ([matthew.welsh, daniel.navarro, steve.begg]@adelaide.edu.au) Australian School of Petroleum, 2 School of Psychology University of Adelaide, North Terrace Adelaide, SA, 5005, Australia Abstract measurements that are presumed to be accurate to ±0.5mm. Thinking in these, pragmatic terms (Sperber & Wilson, 1986), one could conclude, therefore, that the second estimate is, in fact, worse. This is because it is precise to the nearest metre but the true value lies more than 80 metres beyond the 8924.5 to 8925.5m interval resulting from the addition of an appropriate error. The first estimate, by comparison, implies a range of 8.5 to 9.5km and the true value falls well within this. The conclusion to be drawn from the above is that the consideration of Precision can alter our perceptions of accuracy. Although seemingly unremarkable, this has important implications for the way in which we should interpret estimates given by participants during elicitation procedures, as discussed below. In elicitation tasks, people are asked to make estimates under conditions of uncertainty but elicitors then interpret these estimates as if the estimator were certain of them. An analysis of people’s patterns of responding during the elicitation of uncertainty, indicates that there are markers of confidence incorporated into these estimates that can be used to predict the person’s true level of confidence. One such marker is the Precision (Number of significant figures) of the estimate. Analyses of elicited data show the expected positive relationships between accuracy, Precision and explicit confidence and, further, that Precision offers information beyond that of explicit confidence ratings. We then demonstrate the importance of incorporating this information on an overconfidence task, showing that it can account for a 9% difference in calibration. Keywords: Number preference, confidence, Precision, elicitation, judgment and decision making. Elicitation of Uncertainty Introduction Studies of human judgment typically make use of estimates of some quantity given by participants, with a view to assessing the quality of these estimates. However, exactly how to make this assessment is not always straightforward as people’s estimates can contain more information than just a numerical value. For example, imagine that you have asked two individuals how high Mt Everest is. The first answers “9km”; while the second responds “8925 metres”. Later you have the opportunity to check the true answer and find that Mt Everest is 8844.43m high (PRCSBSM, 2005). Which of the two is a better estimate? One answer, of course, is that the second estimate (8925m) is better as it missed the precisely measured value by only 80.57m whereas the first estimate missed by 155.57m. In terms of human interactions, however, the answer is less clear. While the second answer is closer to the true value than the first, it is also far more precise –stating the height to the nearest metre. The first estimate, by comparison, it is stated only to the nearest kilometre. The inference a listener might draw from these different levels of Precision is that the first speaker is giving an approximate height while the second is giving an exact height – an distinction referred to by Yaniv and Foster (1995) as “graininess”. Generally, the less precise an estimate is, then, the less confidence we expect the estimator to have in their estimate being precisely right. This conversational rule mimics the rules of measurement used in the physical sciences where values are given with an error range of ± half the smallest calibration of the measurement device. Thus, a ruler marked in millimetres yields The elicitation of uncertainty describes the process of converting a person’s subjective beliefs regarding uncertain events into a numerical form to allow easier analysis (Wolfson, 2001). Various techniques designed to do this are used where probabilistic forecasting is required in fields such as Petroleum Exploration (Attanasi & Schuenemeyer, 2002), Hydrology (Krzysztofowicz, 2001) and Meteorology (Morgan & Keith, 1995). The technique most commonly used in the oil and gas industry, for example, is the elicitation of 80% confidence ranges (see, e.g., Hawkins, Coopersmith, & Cunningham, 2002). Here the elicitee is asked to give a range of values such that they are 80% certain that the true value of whatever parameter they are estimating will fall within it. Overconfidence The common observation of people using elicitation techniques, however, is that people are overconfident (Lichtenstein, Fischhoff, & Phillips, 1982) – that is, they give ranges that are too narrow, such that values fall outside their 80% ranges more than the expected 20% of times. Given this tendency of people to be overconfident in their estimates, it is not surprising that much of the literature on uncertainty elicitation relates directly to mechanisms for overcoming uncertainty or “debiasing” participants. Various techniques from simple advice to widen ranges (Lichtenstein, et al., 1982) through repeated feedback (Murphy & Winkler, 1977) to the use of probabilistic games (Hawkins, et al., 2002) are recommended. The common observation, however, is that such techniques reduce but do not eliminate overconfidence (Morgan & Henrion, 1990).