The chapter discusses the challenges and future directions of sustainability science, emphasizing the need for interdisciplinary approaches to address complex environmental issues. It highlights three main points: the link between sustainability science and existing disciplines, the impact of globalization on environmental systems, and the role of uncertainty and learning in decision-making. 1. **Disciplines in Sustainability Science**: The chapter argues that while interdisciplinary efforts have led to more flexible and adaptive solutions, disciplinary biases often lead to insufficiently specific approaches. For example, economists tend to focus on market solutions for unsustainable resource use, which can be overly simplistic and fail to address the complexity of the problem. 2. **Globalization**: The increasing integration of global social-ecological systems (SESs) and the growing uncertainty due to environmental changes pose significant challenges. Solutions that ignore these dimensions, such as models of disease spread that neglect social and biophysical factors, are problematic. The chapter emphasizes the need for models that can account for the dynamic and interconnected nature of these systems. 3. **Uncertainty and Learning**: The concept of bounded rationality in economics is discussed, highlighting the importance of heuristics and intuition in decision-making. The chapter also introduces Bayesian model averaging as a method for selecting among competing models, which can help manage uncertainty and improve predictions. Early warning signals of threshold effects in data are identified as a potential tool for detecting impending changes in complex systems. Overall, the chapter underscores the need for a more integrated and flexible approach to sustainability science, one that can effectively address the multifaceted challenges posed by global environmental issues.The chapter discusses the challenges and future directions of sustainability science, emphasizing the need for interdisciplinary approaches to address complex environmental issues. It highlights three main points: the link between sustainability science and existing disciplines, the impact of globalization on environmental systems, and the role of uncertainty and learning in decision-making. 1. **Disciplines in Sustainability Science**: The chapter argues that while interdisciplinary efforts have led to more flexible and adaptive solutions, disciplinary biases often lead to insufficiently specific approaches. For example, economists tend to focus on market solutions for unsustainable resource use, which can be overly simplistic and fail to address the complexity of the problem. 2. **Globalization**: The increasing integration of global social-ecological systems (SESs) and the growing uncertainty due to environmental changes pose significant challenges. Solutions that ignore these dimensions, such as models of disease spread that neglect social and biophysical factors, are problematic. The chapter emphasizes the need for models that can account for the dynamic and interconnected nature of these systems. 3. **Uncertainty and Learning**: The concept of bounded rationality in economics is discussed, highlighting the importance of heuristics and intuition in decision-making. The chapter also introduces Bayesian model averaging as a method for selecting among competing models, which can help manage uncertainty and improve predictions. Early warning signals of threshold effects in data are identified as a potential tool for detecting impending changes in complex systems. Overall, the chapter underscores the need for a more integrated and flexible approach to sustainability science, one that can effectively address the multifaceted challenges posed by global environmental issues.