Classical consensus protocols have been widely deployed by companies such as Google and Facebook to replicate their computing infrastructure--- although traditional deployments are usually in controlled and small-scale environments. The rise of cryptocurrencies has stimulated excitement in large-scale deployments of distributed consensus, e.g., across thousands of nodes and hundreds of (mutually distrustful) organizations. Thus the race is on for the community to create and implement large-scale consensus protocols that are ever more robust and ever more scalable.
Automation, driven by technological progress, has been increasing inexorably for the past several decades. Two schools of economic thinking have for many years been engaged in a debate about the potential effects of automation on jobs: will new technology spawn mass unemployment, as the robots take jobs away from humans? Or will the jobs robots take over create demand for new human jobs?
Over the last decade, the development of fast and reliable motion planning algorithms has deeply influenced many domains in robotics, such as industrial automation and autonomous exploration. Motion planning has also contributed to great advances in an array of unlikely fields, including graphics animation and computational structural biology.
In this talk I will discuss the problem of trying to learn the requirements and preferences of economic agents by observing the outcomes of an allocation mechanism whose rules you also don’t initially know. As an example, consider observing web pages where the agents are advertisers and the winners are those whose ads show up on the given page. We know these ads are placed based on bids and other constraints given to some auction mechanism, but we do not get to see these bids and constraints.