Payment channels allow parties to utilize the blockchain to send transactions for a cheaper fee. Previous work has analyzed to which degree a party can profit by facilitating the transaction process. The aim is to increase the usability of the network and to be rewarded for providing this service. However, previous work focuses on maximizing the reward of the individual player in isolation, a model that we aim to expand. That is why in this work we extend the action space to allow other parties to act and react, and observe the impact this has on the rewards of the player that would otherwise act in isolation.
Testing existing placement strategies by performing channel placement games, we can assess the difference in the reward that indicates the potential loss that competition may cause when operating in the Bitcoin Lightning Network.
Furthermore, we have developed a new strategy that is able to improve the performance in the multi-actor model.

Modern societies manage an ever increasing amount of data. By mining
these data-sets, it is possible to gain understanding of problems. Through a
process of informed consent companies have been able to sequence the genome
of large populations. Providing insight to the consumer about their family
lineage and possible future risks that they could face. As a consequence of
providing such services to consumers, companies are in the position where
they can monetize a database of information hat they possess. The primary
issue that will be addressed is how private genetic data should be handled
correctly. As without clear ethical guidance corporations will (un)willingly
abuse trust. The result of aiming to maximize asset value can be unethical
conduct such as selling the data to third party insurance companies. The
apparent need to process larger quantities of data in order to acquire new
information to fill our knowledge gaps is a trade off between privacy and
anonymity of the individuals within society. Creating an ethical conundrum
for companies trying to profit. This research makes a contribution to prove
that certain actions when sequencing or using genetic information infringe
on privacy and are not morally permissible. Providing greater clarity when
trying to decide whether a use case of personal data is ethically permissible.
By reviewing modern literature that describes the ethical implications of
informed consent and human genome sequencing the research will identify
key areas requiring further work to develop the ethics of technology in a way
that enables innovation whilst keeping society safe.

For a system to be able to interpret data, learn from it, and use those learnings to reach goals and perform tasks is what it means to be intelligent [1]. Since systems are not a product of nature, but rather made by humans they are called Artificial Intelligence (AI). The field of Side-Channel Attacks (SCA) has benefited from applying AI systems to their problems. Operations previously to resource-intensive to perform can now be executed using AI. Currently, the focus lies on exploring which parameters result in the optimum performance when classifying side-channel traces. But since this application has only recently been applied, there is much more research to be done. As of now, the literature claims that a reduction in the size of the architecture would result in an improvement of the explainability of the models used. However, this change in explainability has not been explicitly proven to hold for SCA models. This created a gap in knowledge. This paper aims to close this gap by exploring these assumptions. The goal is to explore if a reduction in complexity of SCA models leads to improved explainability. An experiment was conducted using two existing SCA architectures with a small and large complexity respectively. Using heatmaps, the explainability of these models were assessed to investigate the existence of patterns. The results show a difference in the consistency of the classification process, where the model with the lowest complexity could more consistently state why a certain classification was made. The results indicate that the explainability of a given SCA model can be improved by decreasing its complexity.