Expert Status and Performance

Expert judgements are essential when time and resources are stretched or we face novel dilemmas requiring fast solutions. Good advice can save lives and large sums of money. Typically, experts are defined by their qualifications, track record and experience [1], [2]. The social expectation hypothesis argues that more highly regarded and more experienced experts will give better advice. We asked experts to predict how they will perform, and how their peers will perform, on sets of questions. The results indicate that the way experts regard each other is consistent, but unfortunately, ranks are a poor guide to actual performance. Expert advice will be more accurate if technical decisions routinely use broadly-defined expert groups, structured question protocols and feedback

Quantifying the Effects of Expert Selection and Elicitation Design on Experts' Confidence in Their Judgments About Future Energy Technologies.

Expert elicitations are now frequently used to characterize uncertain future technology outcomes. However, their usefulness is limited, in part because: estimates across studies are not easily comparable; choices in survey design and expert selection may bias results; and overconfidence is a persistent problem. We provide quantitative evidence of how these choices affect experts' estimates. We standardize data from 16 elicitations, involving 169 experts, on the 2030 costs of five energy technologies: nuclear, biofuels, bioelectricity, solar, and carbon capture. We estimate determinants of experts' confidence using survey design, expert characteristics, and public R&D investment levels on which the elicited values are conditional. Our central finding is that when experts respond to elicitations in person (vs. online or mail) they ascribe lower confidence (larger uncertainty) to their estimates, but more optimistic assessments of best-case (10th percentile) outcomes. The effects of expert affiliation and country of residence vary by technology, but in general: academics and public-sector experts express lower confidence than private-sector experts; and E.U. experts are more confident than U.S. experts. Finally, extending previous technology-specific work, higher R&D spending increases experts' uncertainty rather than resolves it. We discuss ways in which these findings should be seriously considered in interpreting the results of existing elicitations and in designing new ones

Stochastic Population Forecasting Based on Combinations of Expert Evaluations Within the Bayesian Paradigm

The paper suggests a procedure to derive stochastic population forecasts adopting an expert-based approach. As in a previous work by Billari et al. (2012), experts are required to provide evaluations, in the form of conditional and unconditional scenarios, on summary indicators of the demographic components determining the population evolution, i.e. fertility, mortality and migration. Here two main purposes are pursued. First, the demographic components are allowed to have some kind of dependence. Second, as a result of the existence of a body of shared information, possible correlations among experts are taken into account. In both cases, the dependence structure is not imposed by the researcher but it is indirectly derived through the scenarios elicited from the experts. To address these issues, the method is based on a mixture model, within the so-called Supra-Bayesian approach according to which expert evaluations are treated as data. The derived posterior distribution for the demographic indicators of interest is used as forecasting distribution and a Markov Chain Monte Carlo algorithm is designed to approximate this posterior. The paper provides the questionnaire which was designed by the authors to collect expert opinions. Finally, an application to the forecast of the Italian Population from 2010 up to 2065 is proposed

Perceived Versus Predicted Risks of Colorectal Cancer and Self-Reported Colonoscopies by Members of Mismatch Repair Gene Mutation-Carrying Families Who Have Declined Genetic Testing

People carrying germline mutations in mismatch repair genes are at high risk of colorectal cancer (CRC), yet about half of people from mutation-carrying families decline genetic counselling and/or testing to identify mutation status. We studied the association of quantitative measures of risk perception, risk prediction and self-reported screening colonoscopy in this elusive yet high-risk group. The sample of 26 participants (mean age 43.1 years, 14 women) in the Australasian Colorectal Cancer Family Registry were relatives of mutation carriers; had not been diagnosed with any cancer at the time of recruitment and had declined an invitation to attend genetic counselling and/or testing. A structured elicitation protocol captured perceived CRC risk over the next 10 years. Self-reported colonoscopy screening was elicited during a 45-minute semi-structured interview. Predicted 10-year CRC risk based on age, gender, known mutation status and family history was calculated using "MMRpro." Mean perceived 10-year risk of CRC was 31 % [95 % CI 21, 40], compared with mean predicted risk of 4 % [2, 7] (p < 0.001); this was independent of age and sex (p = 0.9). Among those reporting any medical advice and any screening colonoscopy (n = 18), those with higher risk perception had less frequent colonoscopy (Pearson's r = 0.49 [0.02, 0.79]). People who decline genetic testing for CRC susceptibility mutations perceive themselves to be at substantially higher risk than they really are. Those with high perceived risk do not undertake screening colonoscopy more often than those who perceive themselves to be at average risk

Planning for ex situ conservation in the face of uncertainty

Ex situ conservation strategies for threatened species often require long-term commitment and financial investment to achieve management objectives. We present a framework that considers the decision to adopt ex situ management for a target species as the end point of several linked decisions. We used a decision tree to intuitively represent the logical sequence of decision making. The first decision is to identify the specific management actions most likely to achieve the fundamental objectives of the recovery plan, with or without the use of ex-situ populations. Once this decision has been made, one decides whether to establish an ex situ population, accounting for the probability of success in the initial phase of the recovery plan, for example, the probability of successful breeding in captivity. Approaching these decisions in the reverse order (attempting to establish an ex situ population before its purpose is clearly defined) can lead to a poor allocation of resources, because it may restrict the range of available decisions in the second stage. We applied our decision framework to the recovery program for the threatened spotted tree frog (Litoria spenceri) of southeastern Australia. Across a range of possible management actions, only those including ex situ management were expected to provide \u3e50% probability of the species\u27 persistence, but these actions cost more than use of in situ alternatives only. The expected benefits of ex situ actions were predicted to be offset by additional uncertainty and stochasticity associated with establishing and maintaining ex situ populations. Naïvely implementing ex situ conservation strategies can lead to inefficient management. Our framework may help managers explicitly evaluate objectives, management options, and the probability of success prior to establishing a captive colony of any given species