Abstract Online Fair Divi Marti UNSW Australia and Data61 martin.aleksand1 Introduction 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; see [Byrne and Anderson, 2014]. 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 spon-sorship covered the delivery of the food; see [Carter, 2014]. Can we reduce the transportation costs implied by the food allocation? Finally, the Meal Gap in New York reached 250 millions in 2014; see [Agi, 2015]. 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. 2 Related Work and Research Plan A greatly investigated topic in resource allocation is offline fair division. Since [Steinhaus, 1948], various mechanisms have been developed that allocate goods offline; see e.g. [Brams and Taylor, 1996]. Today, however, we witness the age of high technologies that enable us to solve complex online problems efficiently. We therefore turn our attention to online fair division. For example, [Walsh, 2011] cut cake on-line by exploiting offline fair division procedures. Further, we cooperate with Foodbank Australia to improve the foodallocation to charities. The food arrives online and is allocated immediately to the charities. [Walsh, 2015] formulated an online model for this setting in which there is a number of agents and indivisible items arrive in rounds. Each agent has some (private) utility for each of the items. As an item arrivethey then bid for the item thus revealing their valuations for it and a mechanism allocates it to one of the agents. [Walsh, 2015] proposed two such mechanisms. L IKE gives uniformly at random an item to an agent that bids positively. The allo-