Modeling Correlated Discrete Uncertainties in Event Trees with Copulas

Modeling the dependence between uncertainties in decision and risk analyses is an important part of the problem structuring process. We focus on situations where correlated uncertainties are discrete, and extend the concept of the copula‐based approach for modeling correlated continuous uncertainties to the representation of correlated discrete uncertainties. This approach reduces the required number of probability assessments significantly compared to approaches requiring direct estimates of conditional probabilities. It also allows the use of multiple dependence measures, including product moment correlation, rank order correlation and tail dependence, and parametric families of copulas such as normal copulas, t‐copulas, and Archimedean copulas. This approach can be extended to model the dependence between discrete and continuous uncertainties in the same event tree.     

Sociology’s Sacred Victims and the Politics of Knowledge: Moral Foundations Theory and Disciplinary Controversies

The field of sociology has long been subject to critique for alleged ideological bias and left-wing groupthink linked to its social justice mission. Critics contend that the construction of “sacred victims” by progressive intellectuals hinders their ability to objectively appraise the circumstances of such vulnerable groups. To address this criticism, we survey 479 sociologists in national universities and colleges in the U.S. regarding three sensitive controversies: urban poverty in the black community; gendered differences in occupational choices; and immigration. We find significant patterns in the data. Commitment to the field’s “moral mission,” preferred research paradigm, gender, and especially political orientation are all significant predictors of sociologists’ views. The results, we suggest, can be understood by conceptualizing the field of sociology as an “emotive community.” In doing so, we draw upon current social psychological research on moral foundations theory developed by Jonathan Haidt and colleagues.     

On testing random effects in the nested-error regression model

We derive an exact F-test for random effects in the nested-error regression model. The derivation utilizes a matrix decomposition that offers a transformation of the response vector into two independent subvectors. When the random effects are absent, the test statistic reduces to a ratio of two independent residual sums of squares that are computed by fitting a regression model using each subvector. A small simulation study compares the power of the F-test with various recent tests and shows that the proposed test has a competitive performance under small as well as large number of clusters.     

Is Nature‐Oriented Tourism a Pro‐Environmental Practice?: Examining Tourism–Environmentalism Alignments Through Discourse Networks and Intersectoral Relationships

A key tenet of ecotourism is that interacting with nature through tourism cultivates environmental awareness and responsibility. We examine this assumption by analyzing discourse networks and organizational networks that connect tourism and environmentalism in Newfoundland and Labrador, Canada. Using a combination of interviews, field observation, and web‐based data, we ask: Is there an alignment of tourism and environmental discourse regarding human interaction with and use of coastal environments? Are there meaningful organizational ties between tourism and environmental organizations? We conclude that there is little indication that nature‐oriented tourism is working to produce substantial changes to our broader political ecology.     

Why Do Immigrants and Refugees Give Back to Their Communities and What can We Learn from Their Civic Engagement?

Each year thousands of immigrants and refugees begin their lives in new places, speaking new languages, and facing new challenges. Challenges include access to health/mental care, education, transportation, and employment. Researchers and practitioners frequently focus on challenges of newcomers and their deficits in meeting needs for self-sufficiency. This study explores newcomers’ giving back and emphasizes an untapped reservoir of strength and capacity. Based on qualitative semi-structured interviews with 54 immigrants and refugees, themes identified include (1) a desire to maintain ethnic identity and connection; (2) ethnic community as an extension of family; (3) a sense of duty and obligation; and (4) measure of achieved success. Researchers and practitioners should shift their view to recognize the strengths and capacities of newcomers who give back to their communities.     

Evaluating Probabilistic Forecasts with Bayesian Signal Detection Models

We propose the use of signal detection theory (SDT) to evaluate the performance of both probabilistic forecasting systems and individual forecasters. The main advantage of SDT is that it provides a principled way to distinguish the response from system diagnosticity, which is defined as the ability to distinguish events that occur from those that do not. There are two challenges in applying SDT to probabilistic forecasts. First, the SDT model must handle judged probabilities rather than the conventional binary decisions. Second, the model must be able to operate in the presence of sparse data generated within the context of human forecasting systems. Our approach is to specify a model of how individual forecasts are generated from underlying representations and use Bayesian inference to estimate the underlying latent parameters. Given our estimate of the underlying representations, features of the classic SDT model, such as the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC), follow immediately. We show how our approach allows ROC curves and AUCs to be applied to individuals within a group of forecasters, estimated as a function of time, and extended to measure differences in forecastability across different domains. Among the advantages of this method is that it depends only on the ordinal properties of the probabilistic forecasts. We conclude with a brief discussion of how this approach might facilitate decision making.     

An Assessment of Two Parenting Training Manuals Used in Swedish Parenting Interventions

In Sweden, all parents of children aged 0–18 years are entitled to attend free parenting courses as part of a national strategy presented by the Government in 2009. This broad parental support welfare strategy is expected to fulfil the intentions of the UN Convention on the Rights of the Child. In this study, two parenting training manuals were analysed: the Canadian Connect program, based on attachment theory, and the Swedish ABC program, based on social learning theory. The results indicate that the manuals use strategies that can both hinder and support children's rights, regardless of rhetoric of children's best interests.     

Economic design of acceptance sampling plans for truncated life test using Frechet distribution

In this article designing of acceptance sampling plans for the truncated life test is proposed using economic approach by assuming that the lifetime of a product follows Frechet distribution based on median. The optimal sampling plans to meet the consumer’s confidence level with minimum total cost are designed. Efficiency of the proposed plan is also analyzed.     

Urban and agricultural soils: conflicts and trade-offs in the optimization of ecosystem services

On-going human population growth and changing patterns of resource consumption are increasing global demand for ecosystem services, many of which are provided by soils. Some of these ecosystem services are linearly related to the surface area of pervious soil, whereas others show non-linear relationships, making ecosystem service optimization a complex task. As limited land availability creates conflicting demands among various types of land use, a central challenge is how to weigh these conflicting interests and how to achieve the best solutions possible from a perspective of sustainable societal development. These conflicting interests become most apparent in soils that are the most heavily used by humans for specific purposes: urban soils used for green spaces, housing, and other infrastructure and agricultural soils for producing food, fibres and biofuels. We argue that, despite their seemingly divergent uses of land, agricultural and urban soils share common features with regards to interactions between ecosystem services, and that the trade-offs associated with decision-making, while scale- and context-dependent, can be surprisingly similar between the two systems. We propose that the trade-offs within land use types and their soil-related ecosystems services are often disproportional, and quantifying these will enable ecologists and soil scientists to help policy makers optimizing management decisions when confronted with demands for multiple services under limited land availability.     

A Bayesian approach to non-parametric monotone function estimation

Summary.  The paper proposes two Bayesian approaches to non-parametric monotone function estimation. The first approach uses a hierarchical Bayes framework and a characterization of smooth monotone functions given by Ramsay that allows unconstrained estimation. The second approach uses a Bayesian regression spline model of Smith and Kohn with a mixture distribution of constrained normal distributions as the prior for the regression coefficients to ensure the monotonicity of the resulting function estimate. The small sample properties of the two function estimators across a range of functions are provided via simulation and compared with existing methods. Asymptotic results are also given that show that Bayesian methods provide consistent function estimators for a large class of smooth functions. An example is provided involving economic demand functions that illustrates the application of the constrained regression spline estimator in the context of a multiple-regression model where two functions are constrained to be monotone.