论文标题
人类效应需要影响：解决机器学习的气候变化的社会心理因素
The Human Effect Requires Affect: Addressing Social-Psychological Factors of Climate Change with Machine Learning
论文作者
论文摘要
机器学习有可能帮助减轻气候变化的人类影响。机器学习以应对气候变化中人类影响的先前应用包括诸如告知个人的碳足迹和减少碳足迹的策略。要使这些方法成为最有效的方法，他们必须考虑每个人的相关社会心理因素。在气候变化中，社会心理因素的作用，情感先前已被确定为感知和愿意从事缓解行为的关键要素。在这项工作中，我们提出了一项调查，以了解如何进行影响，以增强基于机器学习的气候变化干预措施。我们建议将基于情感代理的建模用于气候变化以及使用模拟的气候变化社会困境来探索情感机器学习干预措施的潜在好处。行为和信息干预措施可以成为帮助人类采用缓解行为的强大工具。我们希望利用情感ML可以使干预措施成为更强大的工具，并有助于缓解行为被广泛采用。
Machine learning has the potential to aid in mitigating the human effects of climate change. Previous applications of machine learning to tackle the human effects in climate change include approaches like informing individuals of their carbon footprint and strategies to reduce it. For these methods to be the most effective they must consider relevant social-psychological factors for each individual. Of social-psychological factors at play in climate change, affect has been previously identified as a key element in perceptions and willingness to engage in mitigative behaviours. In this work, we propose an investigation into how affect could be incorporated to enhance machine learning based interventions for climate change. We propose using affective agent-based modelling for climate change as well as the use of a simulated climate change social dilemma to explore the potential benefits of affective machine learning interventions. Behavioural and informational interventions can be a powerful tool in helping humans adopt mitigative behaviours. We expect that utilizing affective ML can make interventions an even more powerful tool and help mitigative behaviours become widely adopted.
