We analyze a zero-sum game between a blind unit-speed searcher and a stationary hider on a given network Q, where the payoff is the time for the searcher to reach the hider. In contrast to the standard game studied in the literature, we do not assume that the searcher has to start from a fixed point (known to the hider) but can choose his starting point. We show that for some networks, including trees, the optimal searcher, and hider strategies have a simple structure.

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E-Commerce marketplaces support millions of daily transactions, and some disagreements between buyers and sellers are unavoidable. Resolving disputes in an accurate, fast, and fair manner is of great importance for maintaining a trustworthy platform. Simple cases can be automated, but intricate cases are not sufficiently addressed by hard-coded rules, and therefore most disputes are currently resolved by people. In this work we take a first step towards automatically assisting human agents in dispute resolution at scale. We construct a large dataset of disputes from the eBay online marketplace, and identify several interesting behavioral and linguistic patterns. We then train classifiers to predict dispute outcomes with high accuracy. We explore the model and the dataset, reporting interesting correlations, important features, and insights.

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