Proceedings Abstracts of the Twenty-Fifth International Joint Conference on Artificial Intelligence

Online Fair Division Redux / 3970
Martin Aleksandrov

Hunger is a major problem worldwide. Food banks around the globe combine forces with various welfare agencies towards alleviating the hunger by assisting people in need. For example, Foodbank Australia cooperates with local charities in order to effectively allocate food as it is donated. In 2014, nearly 10% of these relief organizations could not meet the demand and thus left around 24,000 children with no breakfast in their schools. Can we improve the food allocation? Further, the Foodbanking network in Canada has a long-standing tradition in handling customer demands, but in the last year 60% of their sponsorship covered the delivery of the food. Can we reduce the transportation costs implied by the food allocation? Finally, the Meal Gap in New York reached 250 millions in 2014. How do we allocate food in cities that "never sleep" and in which there are high time and spatial dynamics? Evidently, a food bank needs an allocation mechanism that takes all these features into account. Such a mechanism should be able to (1) allocate resources online, (2) be robust to stochastic changes in the allocation preferences and (3) inform dispatching solutions. I address exactly such complex real-world features in here.