Opportunistic Networks have been designed for transmitting data in difficult environments, characterized by high mobility, sporadic connectivity, and constrained resources. To sustain these networks, the literature describes methods such as Epidemic and Spra & Wait, which do not learn from the network behaviour, and Gossiping-based algorithms that collect historical network data to improve efficiency. In this paper, we show that Gossiping-based solutions suffer from pathological behaviour in some simple network scenarios. Under certain conditions almost all the data transmitted by some nodes may get lost in the network, not reaching its destination. To address this problem we have proposed an algorithm that responds in a more robust manner while staying relatively simple. In this work, we show that our algorithm achieves delivery rates comparable to gossiping-based algorithms, while being more robust and providing better fairness. To illustrate this result, we test native implementations of our solution, Path Sampling, and related algorithms on a network simulator