Adoption and permanence planning in Scotland: Impact of the Adoption and Children (Scotland) Act 2007 on key processes and timescales

Adoption and permanence planning has been a key feature of Scotland's policy in relation to children and young people who are “looked after.” Although policy and law has significantly developed in recent years, there has been comparatively little research on permanence processes in Scotland. This paper outlines key findings from the first comprehensive study of permanence planning in Scotland. It examines the process for two cohorts of children where adoption or other types of permanence orders were made. The children were selected under the long standing Adoption (Scotland) Act 1978 and the more recent Adoption and Children (Scotland) Act 2007. In total, 300 cases were examined, analysing data from the children's first contact with services through to the order made by the Scottish Courts. This paper pays particular attention to the timescales found at key stages under the two sets of legislation and asks what difference the change in legislation has made.     

基于交叉效率DEA与群体共识的区间乘性语言偏好关系群决策

针对区间乘性语言偏好关系群决策问题，提出了一种基于交叉效率DEA和群体共识的群决策方法。首先，提出乘性语言偏好关系导出函数的定义，并构建产出导向的DEA模型，证明了一致性乘性语言偏好关系的DEA效率得分与排序向量之间存在比例关系。在此基础上，建立基于理想值的交叉效率DEA模型，提出乘性语言偏好关系的通用排序方法。同时，基于群体共识建立目标规划模型来计算各语言偏好关系的权重系数。最后，利用Monte Carlo随机模拟的方法对群体语言偏好空间进行统计分析，得到群决策期望排序向量及其可信度。算例分析表明本文方法能够有效的避免信息损失，具有较强的适用性和较高的可信度。     

Names and naming in adoption: Birth heritage and family‐making

In this article, we consider the significance and practices of names and naming in adoption to develop understanding of the issue amongst scholars, policymakers, adoption practitioners, and adoption communities. Research on the topic of names and adoption is scarce and focused mostly on international and domestic adoptions in the United States. We draw on the research literature to critically explore names and naming in adoption through two recurrent and related themes. The first is “birth heritage,” where names, forenames especially, are shown to be important in the approach taken by adopters to their child's birth family and cultural identity. The second theme is “family‐making,” where namesaking and homogeneity of surname emerge, amongst others, as strategies to create, sustain, and display “family.” In gathering together for the first time an otherwise scattered, disparate body of research, our article showcases names and naming as illuminating distillations of key contemporary challenges experienced by families formed through adoption. We conclude our article by considering emergent gaps in existing knowledge and understanding about names, naming, and adoption that might usefully be addressed, to inform professional advice and familial decisions about names, and enhance outcomes for adoptees and their families.     

Politics in evaluation: Politically responsive evaluation in high stakes environments

The role of politics has often been discussed in evaluation theory and practice. The political influence of the situation can have major effects on the evaluation design, approach and methods. Politics also has the potential to influence the decisions made from the evaluation findings. The current study focuses on the influence of the political context on stakeholder decision making. Utilizing a simulation scenario, this study compares stakeholder decision making in high and low stakes evaluation contexts. Findings suggest that high stakes political environments are more likely than low stakes environments to lead to reduced reliance on technically appropriate measures and increased dependence on measures better reflect the broader political environment.     

基于混合加权距离的毕达哥拉斯模糊TOPSIS多属性决策方法研究

针对决策信息为毕达哥拉斯模糊集的多属性决策问题，提出了一种基于混合加权测度的TOPSIS决策方法。在分析了现有距离测度方法不足的基础上，首先给出了一种新的毕达哥拉斯模糊距离测度——毕达哥拉斯模糊有序加权距离（PFOWD），并研究了该测度权重的确定方法；在PFOWD基础上，进一步提出了毕达哥拉斯模糊混合加权距离（PFHWD），同时探讨了其特征和与现有毕达哥拉斯模糊测度的关系；最后提出了一种基于PFHWD测度的毕达哥拉斯模糊TOPSIS多属性方法，并用实例验证了方法的有效性和可行性。     

Farmers’ Risk‐Based Decision Making Under Pervasive Uncertainty: Cognitive Thresholds and Hazy Hedging

Researchers in judgment and decision making have long debunked the idea that we are economically rational optimizers. However, problematic assumptions of rationality remain common in studies of agricultural economics and climate change adaptation, especially those that involve quantitative models. Recent movement toward more complex agent‐based modeling provides an opportunity to reconsider the empirical basis for farmer decision making. Here, we reconceptualize farmer decision making from the ground up, using an in situ mental models approach to analyze weather and climate risk management. We assess how large‐scale commercial grain farmers in South Africa (n = 90) coordinate decisions about weather, climate variability, and climate change with those around other environmental, agronomic, economic, political, and personal risks that they manage every day. Contrary to common simplifying assumptions, we show that these farmers tend to satisfice rather than optimize as they face intractable and multifaceted uncertainty; they make imperfect use of limited information; they are differently averse to different risks; they make decisions on multiple time horizons; they are cautious in responding to changing conditions; and their diverse risk perceptions contribute to important differences in individual behaviors. We find that they use two important nonoptimizing strategies, which we call cognitive thresholds and hazy hedging, to make practical decisions under pervasive uncertainty. These strategies, evident in farmers' simultaneous use of conservation agriculture and livestock to manage weather risks, are the messy in situ performance of naturalistic decision‐making techniques. These results may inform continued research on such behavioral tendencies in narrower lab‐ and modeling‐based studies.     

Designing Graphs that Promote Both Risk Understanding and Behavior Change

Graphs show promise for improving communications about different types of risks, including health risks, financial risks, and climate risks. However, graph designs that are effective at meeting one important risk communication goal (promoting risk‐avoidant behaviors) can at the same time compromise another key goal (improving risk understanding). We developed and tested simple bar graphs aimed at accomplishing these two goals simultaneously. We manipulated two design features in graphs, namely, whether graphs depicted the number of people affected by a risk and those at risk of harm (“foreground+background”) versus only those affected (“foreground‐only”), and the presence versus absence of simple numerical labels above bars. Foreground‐only displays were associated with larger risk perceptions and risk‐avoidant behavior (i.e., willingness to take a drug for heart attack prevention) than foreground+background displays, regardless of the presence of labels. Foreground‐only graphs also hindered risk understanding when labels were not present. However, the presence of labels significantly improved understanding, eliminating the detrimental effect of foreground‐only displays. Labels also led to more positive user evaluations of the graphs, but did not affect risk‐avoidant behavior. Using process modeling we identified mediators (risk perceptions, understanding, user evaluations) that explained the effect of display type on risk‐avoidant behavior. Our findings contribute new evidence to the graph design literature: unlike what was previously feared, we demonstrate that it is possible to design foreground‐only graphs that promote intentions for behavior change without a detrimental effect on risk understanding. Implications for the design of graphical risk communications and decision support are discussed.     

“I feel and think,therefore I am”: An Affect-Cognitive Theory of management decisions

I propose an Affect-Cognitive Theory to comprehensively understand how decisions occur in organizations. To this aim, I first review the assumptions of sensemaking and decision-making streams of research, especially the influence of bounded rationality, affective states and their relationships with cognition; then, I integrate them on the common basis of socially situated cognition. This new theory emphasizes the role of affective states in determining/being determined by cognition and its errors, pointing out decision makers’ affect as the result of multi-level adaptations to the physical and social environment. Management decisions are path dependent but not immutable; they, indeed, bank on the predominant feeling resulting from the modifying interactions and regulations of decision makers with their physical and social environment. Here, decision makers are proposed as “emotional cognizers” overcoming the thinking-feeling dichotomy that has often featured in the study of management decisions. This theory is beneficial for behavioral strategy, offering the needed assumptions to intertwine human cognition, emotions, and social behavior.     

Deep Uncertainties in Sea-Level Rise and Storm Surge Projections: Implications for Coastal Flood Risk Management

Sea levels are rising in many areas around the world, posing risks to coastal communities and infrastructures. Strategies for managing these flood risks present decision challenges that require a combination of geophysical, economic, and infrastructure models. Previous studies have broken important new ground on the considerable tensions between the costs of upgrading infrastructure and the damages that could result from extreme flood events. However, many risk-based adaptation strategies remain silent on certain potentially important uncertainties, as well as the tradeoffs between competing objectives. Here, we implement and improve on a classic decision-analytical model (Van Dantzig 1956) to: (i) capture tradeoffs across conflicting stakeholder objectives, (ii) demonstrate the consequences of structural uncertainties in the sea-level rise and storm surge models, and (iii) identify the parametric uncertainties that most strongly influence each objective using global sensitivity analysis. We find that the flood adaptation model produces potentially myopic solutions when formulated using traditional mean-centric decision theory. Moving from a single-objective problem formulation to one with multiobjective tradeoffs dramatically expands the decision space, and highlights the need for compromise solutions to address stakeholder preferences. We find deep structural uncertainties that have large effects on the model outcome, with the storm surge parameters accounting for the greatest impacts. Global sensitivity analysis effectively identifies important parameter interactions that local methods overlook, and that could have critical implications for flood adaptation strategies.