Using Reinforcement Learning to Integrate Subjective Wellbeing into Climate Adaptation Decision Making

Subjective wellbeing is a fundamental aspect of human life, influencing life expectancy and economic productivity, among others. Mobility plays a critical role in maintaining wellbeing, yet the increasing frequency and intensity of both nuisance and high-impact floods due to climate change are expected to significantly disrupt access to activities and destinations, thereby affecting overall wellbeing. Addressing climate adaptation presents a complex challenge for policymakers, who must select and implement policies from a broad set of options with varying effects while managing resource constraints and uncertain climate projections. In this work, we propose a multi-modular framework that uses reinforcement learning as a decision-support tool for climate adaptation in Copenhagen, Denmark. Our framework integrates four interconnected components: long-term rainfall projections, flood modeling, transport accessibility, and wellbeing modeling. This approach enables decision-makers to identify spatial and temporal policy interventions that help sustain or enhance subjective wellbeing over time. By modeling climate adaptation as an open-ended system, our framework provides a structured framework for exploring and evaluating adaptation policy pathways. In doing so, it supports policymakers to make informed decisions that maximize wellbeing in the long run.

Deep-seeded Clustering for Unsupervised Valence-Arousal Emotion Recognition from Physiological Signals

Emotions play a significant role in the cognitive processes of the human brain, such as decision making, learning and perception. The use of physiological signals has shown to lead to more objective, reliable and accurate emotion recognition combined with raising machine learning methods. Supervised learning methods have dominated the attention of the research community, but the challenge in collecting needed labels makes emotion recognition difficult in large-scale semi- or uncontrolled experiments. Unsupervised methods are increasingly being explored, however sub-optimal signal feature selection and label identification challenges unsupervised methods' accuracy and applicability. This article proposes an unsupervised deep cluster framework for emotion recognition from physiological and psychological data. Tests on the open benchmark data set WESAD show that deep k-means and deep c-means distinguish the four quadrants of Russell's circumplex model of affect with an overall accuracy of 87%. Seeding the clusters with the subject's subjective assessments helps to circumvent the need for labels.