Applying XGBoost and SHAP to Open Source Data to Identify Key Drivers and Predict Likelihood of Wolf Pair Presence.

Wolves have returned to Germany since 2000. Numbers have grown to 209 territorial pairs in 2021. XGBoost machine learning, combined with SHAP analysis is applied to predict German wolf pair presence in 2022 for 10 × 10 km grid cells. Model input consisted of 38 variables from open sources, covering the period 2000 to 2021. The XGBoost model predicted well, with 0.91 as the AUC. SHAP analysis ranked the variables: distance to the closest neighboring wolf pair was the main driver for a grid cell to become occupied by a wolf pair. The clustering tendency of related wolves seems to be an important explanatory factor here. Second was the percentage of wooded area. The next eight variables related to wolf presence in the preceding year, except at fifth, eighth and tenth position in the total order: human density (square root) in the grid, percentage arable land and road density respectively. Other variables including the occurrence of wild prey were the weakest predictors. The SHAP analysis also provided crucial added value in identifying a variable that had threshold values where its contribution to the prediction changed from positive to negative or vice versa. For instance, low density of people increased the probability of wolf pair presence, whereas a high density decreased this probability. Cumulative lift techniques showed that the model performed almost four times better than random prediction. The combination of XGBoost, SHAP and cumulative lift techniques is new in wolf management and conservation, allowing for the focusing of educational and financial resources.

The Role of Mindful Parenting in Individual and Social Decision-Making in Children.

Children are confronted with an increasing amount of choices every day, which can be stressful. Decision-making skills may be one of the most important "21st century skills" that children need to master to ensure success. Many aspects of decision-making, such as emotion regulation during stressful situations, develop in the context of caregiver-child interactions. This study examined whether mindful parenting predicts children's individual and social decision-making. The current study included 63 mother-child dyads from The Netherlands (Child Mage  = 5.11, SD = 0.88, 50.8% girls). Mothers completed the Dutch version of the Interpersonal Mindfulness in Parenting Scale (IM-P). A "Choice Task" was developed to measure individual decision-making skills, and a "Sharing Task" was created to measure social decision-making in young children. Higher maternal mindful parenting significantly predicted more sharing after controlling for covariates (child age, sex, SES, maternal education level; Wald = 4.505, p = 0.034). No main effect of maternal mindful parenting was found for any of the individual decision-making measures. These findings suggest that mindful parenting supports children's social decision-making. Future research should investigate if the combination of mindful parenting and children's early decision-making skills predict key developmental outcomes.

Dispositional greed.

Greed is an important motive: it is seen as both productive (a source of ambition; the motor of the economy) and destructive (undermining social relationships; the cause of the late 2000s financial crisis). However, relatively little is known about what greed is and does. This article reports on 5 studies that develop and test the 7-item Dispositional Greed Scale (DGS). Study 1 (including 4 separate samples from 2 different countries, total N = 6092) provides evidence for the construct and discriminant validity of the DGS in terms of positive correlations with maximization, self-interest, envy, materialism, and impulsiveness, and negative correlations with self-control and life satisfaction. Study 2 (N = 290) presents further evidence for discriminant validity, finding that the DGS predicts greedy behavioral tendencies over and above materialism. Furthermore, the DGS predicts economic behavior: greedy people allocate more money to themselves in dictator games (Study 3, N = 300) and ultimatum games (Study 4, N = 603), and take more in a resource dilemma (Study 5, N = 305). These findings shed light on what greed is and does, how people differ in greed, and how greed can be measured. In addition, they show the importance of greed in economic behavior and provide directions for future studies.

Defining greed.

Although greed is both hailed as the motor of economic growth and blamed as the cause of economic crises, very little is known about its psychological underpinnings. Five studies explored lay conceptualizations of greed among U.S. and Dutch participants using a prototype analysis. Study 1 identified features related to greed. Study 2 determined the importance of these features; the most important features were classified as central (e.g., self-interested, never satisfied), whereas less important features were classified as peripheral (e.g., ambition, addiction). Subsequently, we found that, compared to peripheral features, participants recalled central features better (Study 3), faster (Study 4), and these central features were more present in real-life episodes of greed (Study 5). These findings provide a better understanding of the elements that make up the experience of greed and provide insights into how greed can be manipulated and measured in future research.