In urban environments, social boundaries are the areas that emerge from processes of economic inequality and social segregation. These boundaries are important, as they serve both as areas of interaction and conflict. By applying geographical thinking to classic methods in data science, we can better understand where these boundaries emerge and how they delineate communities. In this talk, I’ll explain a bit about the basics of “boundary detection” in urban analytics. I’ll present a new method, the “geosilhouette,” that builds on previous methods of identifying the boundaries between clusters. And, finally, I’ll show how this can change our understanding of urban community.

Multi-agent reinforcement learning (MARL) has recently shown great promise as an approach to networked system control. Arguably, one of the most difficult and important tasks for which large scale networked system control is applicable is common-pool resource (CPR) management. Crucial CPRs include arable land, fresh water, wetlands, wildlife, fish stock, forests and the atmosphere, of which proper management is related to some of society’s greatest challenges such as food security, inequality and climate change. This talk will consist of three parts. In the first, we will briefly look at climate change and how it poses a significant threat to life on our planet. In the second, we will consider the potential of multi-agent systems for climate change mitigation and adaptation. And finally, in the third, we will discuss recent research from InstaDeep into better understanding the behaviour of networked MARL systems used for CPR management. More specifically, we will see how the tools from empirical game-theoretic analysis may be harnessed to analyse the differences in networked MARL systems. The results give new insights into the consequences associated with certain design choices and provide an additional dimension of comparison between systems beyond efficiency, robustness, scalability and mean control performance.