A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.     

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.     

Risk Mapping of Groundwater‐Drawdown‐Induced Land Subsidence in Heterogeneous Soils on Large Areas

Groundwater leakage into subsurface constructions can cause reduction of pore pressure and subsidence in clay deposits, even at large distances from the location of the construction. The potential cost of damage is substantial, particularly in urban areas. The large‐scale process also implies heterogeneous soil conditions that cannot be described in complete detail, which causes a need for estimating uncertainty of subsidence with probabilistic methods. In this study, the risk for subsidence is estimated by coupling two probabilistic models, a geostatistics‐based soil stratification model with a subsidence model. Statistical analyses of stratification and soil properties are inputs into the models. The results include spatially explicit probabilistic estimates of subsidence magnitude and sensitivities of included model parameters. From these, areas with significant risk for subsidence are distinguished from low‐risk areas. The efficiency and usefulness of this modeling approach as a tool for communication to stakeholders, decision support for prioritization of risk‐reducing measures, and identification of the need for further investigations and monitoring are demonstrated with a case study of a planned tunnel in Stockholm.     

Temperament,mothers' reactions to children's emotional experiences,and emotion understanding predicting adjustment in preschool children

It is expected that both children and their parents contribute to children's development of emotion knowledge and adjustment. Bidirectional relations between child temperament (fear, frustration, executive control) and mothers' reactions to children's emotional experiences were examined to explore how these variables predict children's emotion understanding, social competence, and problem behaviors. Preschool‐aged children (N = 306) and their mothers were assessed across four‐time points. Children's temperament and mothers' non‐supportive reactions to children's emotional experiences were assessed when children were 36 and 45 months of age. Emotion understanding was assessed when the children were 54 months of age and teachers reported on children's problem behaviors and social competence when the children were 63 months of age. Covariates included family income, child cognitive ability, gender, and child adjustment at 36 months. Results from path analyses demonstrated that bidirectional relations between children's temperament and mothers' non‐supportive reactions were not significant. However, mother's non‐supportive reactions directly predicted fewer problem behaviors, and children's emotion understanding mediated the relation between children's executive control and their later social competence. As such, emotion understanding appears to be one mechanism through which executive control might impact social competence.     

考虑消费者双重偏好的低碳供应链减排策略研究

为了削弱传统零售商在渠道博弈中的垄断地位，制造商尝试建立在线渠道与零售商展开竞争。本文在考虑消费者低碳偏好的基础上，又将消费者的渠道偏好进行区分，通过不同决策模式的对比及数值算例分析，寻求低碳供应链最优的减排边界。研究表明：1分散决策时，制造商的低碳决策会受消费者渠道偏好的影响；集中决策时，制造商的低碳决策保持不变。2不同决策模式的减排边界由渠道偏好决定，渠道偏好较大时，分散决策的减排边界更高；渠道偏好较低时，集中决策的减排边界更高；渠道偏好中立时，两者的减排边界相同。3制造商可以通过升级低碳技术、实行低碳化管理等方式降低减排成本，同时通过低碳产品认证、广告宣传等方式提高消费者对低碳产品的认可程度。     

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.     

Legitimizing leisure experiences as emotional work: A post‐humanist approach to gendered equine encounters

Due to changes in lifestyle and work patterns, education and values associated with wellbeing, non‐human animals are now incorporated into a range of human experiences and environments. This research specifically focuses on human–equine relations, examining blurred boundaries between therapeutic and recreational interspecies encounters. It is acknowledged that human–equine relations are often gendered and this research focuses mainly on women's narratives. Viewed through the post‐humanist lens, horses now form kinship and companionship roles, particularly for women, where relations have become mutually emotionally dependent as a result of interspecies communication and embodied encounters. Research utilizes feminist post‐humanist and cultural politics of emotion frameworks associate with the co‐agency on the co‐agency of animals. Embedded in the concept of equiscapes, or post‐humanist leisure spaces, research methods employ qualitative approaches, including in‐depth interviews, participant diaries and multispecies ethnography. Findings reveal how women make considerable investments in equine activities, which develops mutual welfare and wellbeing. Yet, despite these benefits, emotional and other expenditures are justified in work discourses to legitimize them as valuable to themselves, their families and their communities.     

Efficient Inference for Longitudinal Data Varying‐coefficient Regression Models

Informative identification of the within‐subject correlation is essential in longitudinal studies in order to forecast the trajectory of each subject and improve the validity of inferences. In this paper, we fit this correlation structure by employing a time adaptive autoregressive error process. Such a process can automatically accommodate irregular and possibly subject‐specific observations. Based on the fitted correlation structure, we propose an efficient two‐stage estimator of the unknown coefficient functions by using a local polynomial approximation. This procedure does not involve within‐subject covariance matrices and hence circumvents the instability of calculating their inverses. The asymptotic normality of resulting estimators is established. Numerical experiments were conducted to check the finite sample performance of our method and an example of an application involving a set of medical data is also illustrated.