  Honest cooperation among individuals in a network can be achieved in
different ways. In online networks with some kind of central authority, such as
Ebay, Airbnb, etc. honesty is achieved through a reputation system, which is
maintained and secured by the central authority. These systems usually rely on
review mechanisms, through which agents can evaluate the trustworthiness of
their interaction partners. These reviews are stored centrally and are
tamper-proof. In decentralized peer-to-peer networks, enforcing cooperation
turns out to be more difficult. One way of approaching this problem is by
observing cooperative biological communities in nature. One finds that
cooperation among biological organisms is achieved through a mechanism called
indirect reciprocity. This mechanism for cooperation relies on some shared
notion of trust. In this work we aim to facilitate communal cooperation in a
peer-to-peer file sharing network called Tribler, by introducing a mechanism
for evaluating the trustworthiness of agents. We determine a trust ranking of
all nodes in the network based on the Monte Carlo algorithm estimating the
values of Google's personalized PageRank vector. We go on to evaluate the
algorithm's resistance to Sybil attacks, whereby our aim is for sybils to be
assigned low trust scores.
