Scenario Discovery with Multiple Criteria: An Evaluation of the Robust Decision‐Making Framework for Climate Change Adaptation

There is increasing concern over deep uncertainty in the risk analysis field as probabilistic models of uncertainty cannot always be confidently determined or agreed upon for many of our most pressing contemporary risk challenges. This is particularly true in the climate change adaptation field, and has prompted the development of a number of frameworks aiming to characterize system vulnerabilities and identify robust alternatives. One such methodology is robust decision making (RDM), which uses simulation models to assess how strategies perform over many plausible conditions and then identifies and characterizes those where the strategy fails in a process termed scenario discovery. While many of the problems to which RDM has been applied are characterized by multiple objectives, research to date has provided little insight into how treatment of multiple criteria impacts the failure scenarios identified. In this research, we compare different methods for incorporating multiple objectives into the scenario discovery process to evaluate how they impact the resulting failure scenarios. We use the Lake Tana basin in Ethiopia as a case study, where climatic and environmental uncertainties could impact multiple planned water infrastructure projects, and find that failure scenarios may vary depending on the method used to aggregate multiple criteria. Common methods used to convert multiple attributes into a single utility score can obscure connections between failure scenarios and system performance, limiting the information provided to support decision making. Applying scenario discovery over each performance metric separately provides more nuanced information regarding the relative sensitivity of the objectives to different uncertain parameters, leading to clearer insights on measures that could be taken to improve system robustness and areas where additional research might prove useful.     

Effect of Brief Staff‐Assisted Career Service Delivery on Drop‐In Clients

Brief staff‐assisted career counseling services based on cognitive information processing theory were evaluated using 138 drop‐in clients who completed pre‐ and postintervention self‐report measures of knowledge about next steps, confidence in making next steps, and anxiety about career concerns. Significant changes occurred in each dimension and were significantly associated with the quality of career adviser interaction. Participants rated the experience as highly positive. Decreases in anxiety about career concerns were unrelated to the intervention. Implications include that a brief service delivery model yields positive outcomes for many clients but requires adjustments in organizational culture, physical environment, practitioner approaches, and client expectations. Future research should examine relational variables related to decreased anxiety with a brief service delivery model, the degree to which this approach works with specific career needs, and client attributes leading to successful versus unsuccessful outcomes with this model.     

Rainbow Research: Challenges and Recommendations for Sexual Orientation and Gender Identity and Expression (SOGIE) Survey Design

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - A growing number of people around the world identify, in some way, as Lesbian, Gay, Bisexual, Transgender, and Queer...     

Artificial Intelligence for Natural Hazards Risk Analysis: Potential,Challenges, and Research Needs

Artificial intelligence (AI) methods have seen increasingly widespread use in everything from consumer products and driverless cars to fraud detection and weather forecasting. The use of AI has transformed many of these application domains. There are ongoing efforts at leveraging AI for disaster risk analysis. This article takes a critical look at the use of AI for disaster risk analysis. What is the potential? How is the use of AI in this field different from its use in nondisaster fields? What challenges need to be overcome for this potential to be realized? And, what are the potential pitfalls of an AI-based approach for disaster risk analysis that we as a society must be cautious of?     

Popular Nonprofit Management Research Sources on CD-ROM

Nonprofit World 1983–1996. Madison, Wis.: Learning Institute for Nonprofit Organizations, 1997. $50 CD-ROM. FC Search: The Foundation Center's Database on CD-ROM, Version 2.0. New York: Foundation Center, 1997. $1,995 CD-ROM personal version.     

Portrait of the Social Entrepreneur: Statistical Evidence from a US Panel

Social entrepreneurship is an increasingly important concept in the study of voluntary and nonprofit organizations. In spite of the growing recognition of this concept, little is known about what individual characteristics might describe or explain who in society is likely to be (or become) a social entrepreneur. This preliminary study empirically addresses this question using data from a United States online panel. Our results suggest that social entrepreneurs are likely to be female, non-white, younger, and college-educated individuals with some business experience and who live in big cities. Social entrepreneurs also tend to have more social capital, as measured by their activity in clubs and organizations other than work, and they are more likely to be happy, interested in politics, extroverted, giving (to charity), and liberal ideologically. Although exploratory, these findings help describe the social entrepreneur and suggest ways in which this important actor in civil society can be better identified, understood, and perhaps cultivated.     

Prestorm Estimation of Hurricane Damage to Electric Power Distribution Systems

Hurricanes frequently cause damage to electric power systems in the United States, leading to widespread and prolonged loss of electric service. Restoring service quickly requires the use of repair crews and materials that must be requested, at considerable cost, prior to the storm. U.S. utilities have struggled to strike a good balance between over‐ and underpreparation largely because of a lack of methods for rigorously estimating the impacts of an approaching hurricane on their systems. Previous work developed methods for estimating the risk of power outages and customer loss of power, with an outage defined as nontransitory activation of a protective device. In this article, we move beyond these previous approaches to directly estimate damage to the electric power system. Our approach is based on damage data from past storms together with regression and data mining techniques to estimate the number of utility poles that will need to be replaced. Because restoration times and resource needs are more closely tied to the number of poles and transformers that need to be replaced than to the number of outages, this pole‐based assessment provides a much stronger basis for prestorm planning by utilities. Our results show that damage to poles during hurricanes can be assessed accurately, provided that adequate past damage data are available. However, the availability of data can, and currently often is, the limiting factor in developing these types of models in practice. Opportunities for further enhancing the damage data recorded during hurricanes are also discussed.     

Using the NIAAA brief alcohol screener in social work practice for selected prevention targeting youth

ABSTRACT

This article discusses the use of brief screeners in social work practice to identity adolescents in need of selected interventions for alcohol and other drug use problems. Brief screeners can increase access to alcohol or other drug intervention services and promote the diffusion of evidence-based interventions to underserved communities when integrated in Screening, Brief Intervention, and Referral to Treatment (SBIRT) initiatives. The two-item NIAAA Brief Alcohol Use Screener is discussed as a developmentally tailored assessment tool that can be integrated into SBIRT in social work practice to improve detection of early-stage alcohol problems among adolescents who lack routine access to preventative health care. The use of brief, empirically supported alcohol screeners in trainings for social work students and new professionals can enhance their preparation and competence to offer child and adolescent clients appropriate selected intervention options to reduce harms associated with underage alcohol use.     

Education, Ability, and Civic Engagement in the Contemporary United States

Participation in civic activities is widely recognized as the necessary foundation for any democratic government. Education has long been identified as a key determining factor of such civic engagement. Occasionally, this traditional finding has been called into question. Some scholars have hypothesized, while others firmly asserted, that cognitive ability fills the role usually attributed to education. This hypothesis implies that intelligence determines participation and, consequently, good citizenship. Thus, an inquiry into the relationships among education, cognitive ability, and civic participation raises important questions about citizenship in a democracy. By examining data from the 1976 American National Election Study (ANES), the 1974–1990 General Social Surveys (GSS), and the 1957–1975 Wisconsin Longitudinal Study (WLS), I test the hypothesis that education is only a proxy for cognitive ability. I find that analysis using the ANES, which includes a very poor ability measure, produces inconclusive evidence. Yet analysis of data that include better measures of ability, the GSS and the WLS, rejects this hypothesis. In conclusion, the assertion of ability's dominance in determining civic participation is empirically untenable.