Nature computational science最新文献

Prosocial motives such as social equality and efficiency are key to altruistic behaviors. However, predicting the range of altruistic behaviors in varying contexts and individuals proves challenging if we limit ourselves to one or two motives. Here we demonstrate the numerous, interdependent motives in altruistic behaviors and the possibility to disentangle them through behavioral experimental data and computational modeling. In one laboratory experiment (N = 157) and one preregistered online replication (N = 1,258), across 100 different situations, we found that both third-party punishment and third-party helping behaviors (that is, an unaffected individual punishes the transgressor or helps the victim) aligned best with a model of seven socioeconomic motives, referred to as a motive cocktail. For instance, the inequality discounting motives imply that individuals, when confronted with costly interventions, behave as if the inequality between others barely exists. The motive cocktail model also provides a unified explanation for the differences in intervention willingness between second parties (victims) and third parties, and between punishment and helping.

Weather-related extreme events — such as heat waves, floods, and droughts — are on the rise, and the human-caused emission of greenhouse gases has been reported to increase the frequency and intensity of such events. However, identifying and quantifying the exact contribution of anthropogenic climate change to extreme events remains a challenging task. Recent advances in event attribution studies have attempted to quantify the impact of anthropogenic forcings, but they come with certain limitations, such as high uncertainty in attribution estimates due to the limited length of observational records, and high computational cost, which makes rapid attribution assessments difficult to perform. In a recent work, Noah S. Diffenbaugh and colleagues introduce a deep learning-based framework to address the aforementioned gaps and assess the contribution of human-caused climate change to individual extreme heat events.

The authors make use of convolutional neural networks (CNNs) as the basis of their framework. Notably, multiple CNNs are trained to predict daily maximum air temperature (TMAX) using climate model simulation data. To understand how a historical extreme event is influenced by anthropogenic climate forcing, first, unseen historical reanalysis data (which combine observations of past weather with simulations) are used as inputs to these CNNs to accurately predict TMAX at various levels of global mean surface temperature (GMT). Then, the authors employ partial dependence analysis — an explainable method that shows how a particular feature affects the predicted outcome — to create counterfactual versions of the extreme event under different levels of annual GMT. Ultimately, by calculating the sensitivity of the counterfactual CNN predictions to the GMT input value, the framework is able to quantify the contribution of anthropogenic forcing to the event magnitude. In their experiments, the authors analyzed different historical heat wave events, with the results broadly in agreement with previous reports and published results. Overall, the work suggests that deep learning has the potential to be used to perform rapid and low-cost attribution assessment of extreme events.