Designs for generalized linear models with several variables and model uncertainty

Standard factorial designs sometimes may be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables. A method is proposed for finding exact designs for such experiments that uses a criterion allowing for uncertainty in the link function, the linear predictor, or the model parameters, together with a design search. Designs are assessed and compared by simulation of the distribution of efficiencies relative to locally optimal designs over a space of possible models. Exact designs are investigated for two applications, and their advantages over factorial and central composite designs are demonstrated.

The role of uncertainty in the relationship between fairness evaluations and willingness to pay

Participants in contingent valuation studies may be uncertain about a number of aspects of the policy and survey context. The uncertainty management model of fairness judgments states that individuals will evaluate a policy in terms of its fairness when they do not know whether they can trust the relevant managing authority or experience uncertainty due to insufficient knowledge of the general issues surrounding the environmental policy. Similarly, some researchers have suggested that, not knowing how to answer WTP questions, participants convey their general attitudes toward the public good rather than report well-defined economic preferences. These contentions were investigated in a sample of 840 residents in four urban catchments across Australia who were interviewed about their WTP for stormwater pollution abatement. Four sources of uncertainty were measured: amount of prior issue-related thought, trustworthiness of the water authority, insufficient scenario information, and WTP response uncertainty. A logistic regression model was estimated in each subsample to test the main effects of the uncertainty sources on WTP as well as their interaction with fairness and proenvironmental attitudes. Results indicated support for the uncertainty management model in only one of the four samples. Similarly, proenvironmental attitudes interacted rarely with uncertainty to a significant level, and in ways that were more complex than hypothesised. It was concluded that uncertain individuals were generally not more likely than other participants to draw on either fairness evaluations or proenvironmental attitudes when making decisions about paying for stormwater pollution abatement.

Methicillin-resistant Staphylococcus aureus genotyping using a small set of polymorphisms

The aim of this study was to identify a set of genetic polymorphisms that efficiently divides methicillin-resistant Staphylococcus aureus (MRSA) strains into groups consistent with the population structure. The rationale was that such polymorphisms could underpin rapid real-time PCR or low-density array-based methods for monitoring MRSA dissemination in a cost-effective manner. Previously, the authors devised a computerized method for identifying sets of single nucleoticle polymorphisms (SNPs) with high resolving power that are defined by multilocus sequence typing (MLST) databases, and also developed a real-time PCR method for interrogating a seven-member SNP set for genotyping S. aureus. Here, it is shown that these seven SNPs efficiently resolve the major MRSA lineages and define 27 genotypes. The SNP-based genotypes are consistent with the MRSA population structure as defined by eBURST analysis. The capacity of binary markers to improve resolution was tested using 107 diverse MRSA isolates of Australian origin that encompass nine SNP-based genotypes. The addition of the virulence-associated genes cna, pvl and bbplsdrE, and the integrated plasmids pT181, p1258 and pUB110, resolved the nine SNP-based genotypes into 21 combinatorial genotypes. Subtyping of the SCCmec locus revealed new SCCmec types and increased the number of combinatorial genotypes to 24. It was concluded that these polymorphisms provide a facile means of assigning MRSA isolates into well-recognized lineages.

Staphylococcus aureus genotyping using novel real-time PCR formats

One approach to microbial genotyping is to make use of sets of single-nucleotide polymorphisms (SNPs) in combination with binary markers. Here we report the modification and automation of a SNP-plus-binary-marker-based approach to the genotyping of Staphylococcus aureus and its application to 391 S. aureus isolates from southeast Queensland, Australia. The SNPs used were arcC210, tpi243, arcC162, gmk318, pta294, tpi36, tpi241, and pta383. These provide a Simpson's index of diversity (D) of 0.95 with respect to the S. aureus multilocus sequence typing database and define 61 genotypes and the major clonal complexes. The binary markers used were pvl, cna, sdrE, pT181, and pUB110. Two novel real-time PCR formats for interrogating these markers were compared. One of these makes use of light upon extension (LUX) primers and biplexed reactions, while the other is a streamlined modification of kinetic PCR using SYBR green. The latter format proved to be more robust. In addition, automated methods for DNA template preparation, reaction setup, and data analysis were developed. A single SNP-based method for ST-93 (Queensland clone) identification was also devised. The genotyping revealed the numerical importance of the South West Pacific and Queensland community-acquired methicillin-resistant S. aureus (MRSA) clones and the clonal complex 239 Aus-1/Aus-2 hospital-associated MRSA. There was a strong association between the community-acquired clones and pvl.

The state-contingent approach to production under uncertainty

The central claim of this paper is that the state-contingent approach provides the best way to think about all problems in the economics of uncertainty, including problems of consumer choice, the theory of the firm, and principal-agent relationships. This claim is illustrated by recent developments in, and applications of, the state-contingent approach.

Motivations for group membership: The role of subjective importance and uncertainty reduction

An experiment was conducted to investigate the idea that an important motive for identifying with social groups is to reduce subjective uncertainty, particularly uncertainty on subjectively important dimensions that have implications for the self-concept (e.g., Hogg, 1996; Hogg & Mullin, 1999). When people are uncertain on a dimension that is subjectively important, they self-categorize in terms of an available social categorization and, thus, exhibit group behaviors. To test this general hypothesis, group membership, task uncertainty, and task importance were manipulated in a 2 x 2 x 2 between-participants design (N = 128), under relatively minimal group conditions. Ingroup identification and desire for consensual validation of specific attitudes were the key dependent measures, but we also measured social awareness. All three predictions were supported. Participants identified with their group (H1), and desired to obtain consensual validation from ingroup members (H2) when they were uncertain about their judgments on important dimensions, indicating that uncertainty reduction motivated participants towards embracing group membership. In addition, identification mediated the interactive effect of the independent variables on consensual validation (H3), and the experimental results were not associated with an increased sense of social awareness and, therefore, were unlikely to represent only behavioral compliance with generic social norms. Some implications of this research in the study of cults and totalist groups and the explication of genocide and group violence are discussed.

Quantification of geological uncertainty and risk using stochastic simulation and applications in the coal mining industry

Stochastic simulation is a recognised tool for quantifying the spatial distribution of geological uncertainty and risk in earth science and engineering. Metals mining is an area where simulation technologies are extensively used; however, applications in the coal mining industry have been limited. This is particularly due to the lack of a systematic demonstration illustrating the capabilities these techniques have in problem solving in coal mining. This paper presents two broad and technically distinct areas of applications in coal mining. The first deals with the use of simulation in the quantification of uncertainty in coal seam attributes and risk assessment to assist coal resource classification, and drillhole spacing optimisation to meet pre-specified risk levels at a required confidence. The second application presents the use of stochastic simulation in the quantification of fault risk, an area of particular interest to underground coal mining, and documents the performance of the approach. The examples presented demonstrate the advantages and positive contribution stochastic simulation approaches bring to the coal mining industry