Risk-Based Approaches to Deal with Uncertainty in a Data-Poor System: Stakeholder Involvement in Hazard Identification for Marine National Parks and Marine Sanctuaries in Victoria, Australia

Management responsibilities for the system of marine national parks and sanctuaries declared in Victoria, Australia in 2002 have created imperatives for robust, scientifically defendable approaches to identifying threats to valued ecological attributes of the parks, setting management priorities, and developing monitoring systems. We are developing a protocol for ecological risk assessment in the parks that has due regard for the perception of risks by individuals, and ensures that stakeholder values are an intrinsic part of decision making. The inclusive and transparent protocol provides an opportunity for stakeholder involvement in the identification of valued attributes, as well as in the assessment of associated risks. Our approach brings together ideas about how science enters the community engagement domain in ways that promote collaboration and transparency in decision making. A series of stakeholder workshops across the state drew on the expertise of agency staff, community groups, fishers, industry representatives, academics, and knowledgeable park neighbors to identify hazards of major concern in the parks. Many hazards involved predictable, tangible threats like pollution and exotic species, but the approach also identified a number of less obvious threats related to governance issues and the knowledge-base for the parks. Importantly, the workshops with their broad range of stakeholders identified threats not previously considered by the management agency in its internal assessments, and several of these "new" threats are already the subject of action by the agency. The deliberate incorporation of local knowledge and local networks in the risk assessment process also provided opportunities for greater engagement of stakeholders with the management agency.     

Symmetry, Identifiability, and Prediction Uncertainties in Multistage Clonal Expansion (MSCE) Models of Carcinogenesis

Many models of exposure-related carcinogenesis, including traditional linearized multistage models and more recent two-stage clonal expansion (TSCE) models, belong to a family of models in which cells progress between successive stages-possibly undergoing proliferation at some stages-at rates that may depend (usually linearly) on biologically effective doses. Biologically effective doses, in turn, may depend nonlinearly on administered doses, due to PBPK nonlinearities. This article provides an exact mathematical analysis of the expected number of cells in the last ("malignant") stage of such a "multistage clonal expansion" (MSCE) model as a function of dose rate and age. The solution displays symmetries such that several distinct sets of parameter values provide identical fits to all epidemiological data, make identical predictions about the effects on risk of changes in exposure levels or timing, and yet make significantly different predictions about the effects on risk of changes in the composition of exposure that affect the pharmacodynamic dose-response relation. Several different predictions for the effects of such an intervention (such as reducing carcinogenic constituents of an exposure) that acts on only one or a few stages of the carcinogenic process may be equally consistent with all preintervention epidemiological data. This is an example of nonunique identifiability of model parameters and predictions from data. The new results on nonunique model identifiability presented here show that the effects of an intervention on changing age-specific cancer risks in an MSCE model can be either large or small, but that which is the case cannot be predicted from preintervention epidemiological data and knowledge of biological effects of the intervention alone. Rather, biological data that identify which rate parameters hold for which specific stages are required to obtain unambiguous predictions. From epidemiological data alone, only a set of equally likely alternative predictions can be made for the effects on risk of such interventions.     

The structure and substance of automobile racing, or how hi-tech keeps its greasy side down

Automobile racing is approached as a feature of advanced capitalist society. First, the organized sport of automobile racing is considered in a Levi-Straussian construct, and the myths and structures underlying that sport are examined. A cognitive model is then developed to illustrate further the interaction of the rules or structures that govern this 'game.'
L'auteur aborde le sujet des courses d'automobiles comme étant un attribut des sociétés capitalistes. D'abord, il considère le sport organisé des courses d'automobiles selon 1'approche de Lévi-Strauss, et il examine les mythes et les structures à la base de ce sport. Ensuite, il développe un modèle cognitif, dans la perspective du coureur, pour illustrer davantage l'action réciproque des règies ou des structures qui gouvernent ce 'jeu.'     

Effectiveness of Group Reciprocity Counseling with Married Couples*

This research examined the efficacy of using a reciprocity procedure with married couples in a group format. Seven couples, enrolled in a non-credit extension course, participated in an eight-week workshop. In-workshop and at-home assignments were completed by the participants. Data were collected using daily marital happiness ratings and a pre-workshop inventory. Results were analyzed using t-tests and multiple baseline procedure. Commitment to and optimism about marriage and ratings on 3 of 9 areas of couple interaction significantly increased over the seven-week period. Introduction of specific reciprocity procedures and increased ratings of marital happiness were related in a contingent manner.     

Probabilistic sensitivity analysis of complex models: a Bayesian approach

Summary.  In many areas of science and technology, mathematical models are built to simulate complex real world phenomena. Such models are typically implemented in large computer programs and are also very complex, such that the way that the model responds to changes in its inputs is not transparent. Sensitivity analysis is concerned with understanding how changes in the model inputs influence the outputs. This may be motivated simply by a wish to understand the implications of a complex model but often arises because there is uncertainty about the true values of the inputs that should be used for a particular application. A broad range of measures have been advocated in the literature to quantify and describe the sensitivity of a model's output to variation in its inputs. In practice the most commonly used measures are those that are based on formulating uncertainty in the model inputs by a joint probability distribution and then analysing the induced uncertainty in outputs, an approach which is known as probabilistic sensitivity analysis. We present a Bayesian framework which unifies the various tools of prob- abilistic sensitivity analysis. The Bayesian approach is computationally highly efficient. It allows effective sensitivity analysis to be achieved by using far smaller numbers of model runs than standard Monte Carlo methods. Furthermore, all measures of interest may be computed from a single set of runs.     

USING REPEATED MEASURES DESIGNS IN PROGRAM EVALUATION OF FAMILY THERAPY*

Data from 14 simulated families were analyzed with both a repeated measures and an ordinary analysis of variance design in order to illustrate the differences between the two approaches. The advantages of repeated measures designs for the analysis of family data in the program evaluation of family therapy are highlighted.     

A NEW APPROACH TO MAXIMUM LIKELIHOOD ESTIMATION OF THE THREE-PARAMETER GAMMA AND WEIBULL DISTRIBUTIONS

A new approach, is proposed for maximum likelihood (ML) estimation in continuous univariate distributions. The procedure is used primarily to complement the ML method which can fail in situations such as the gamma and Weibull distributions when the shape parameter is, at most, unity. The new approach provides consistent and efficient estimates for all possible values of the shape parameter. Its performance is examined via simulations. Two other, improved, general methods of ML are reported for comparative purposes. The methods are used to estimate the gamma and Weibull distributions using air pollution data from Melbourne. The new ML method is accurate when the shape parameter is less than unity and is also superior to the maximum product of spacings estimation method for the Weibull distribution.