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Here the Radboud University presents theses written by students affiliated with the various bachelor and master programmes offered at the Radboud University, as well as papers written by students of the Radboud Honours Academy.

Recent Submissions

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    Feministische leiderschap binnen geloofsgemeenschappen
    (2023-10-25) de Vries, Japke
    Om maatschappelijke relevantie te behouden en zichtbaar te blijven binnen een seculiere samenleving, zijn christelijke geloofsgemeenschappen op zoek naar nieuwe vormen van leiderschap. Of een feministische leiderschapsstijl passend is, heeft binnen deze context nog geen wetenschappelijke aandacht gehad. Deze scriptie wil een bijdrage leveren aan het debat en onderzoekt vanuit een theoretisch kader door kwalitatief onderzoek hoe een feministische leiderschapsstijl vorm krijgt binnen geloofsgemeenschappen. Uit de empirisch verkregen data is een praktisch feministisch leiderschapsprofiel opgesteld waar leiderschap een proces is en traditionele feminiene en masculiene waarden elkaar versterken. Het is een authentieke leiderschapsstijl die zich inzet voor nieuwe, laagdrempelige en inclusieve vormen van kerkzijn binnen haar lokale gemeenschap en die een brug wil slaan tussen religie en samenleving.
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    Effect of Open Communication on Predator Behavior in Simulated Environments
    (2022-01-30) Gather, R.L.J.H.
    Will simulated predators communicate if they are given the option and means to broadcast and receive information over an unconstrained communication channel in a simulated world? If they do, what will they talk about? In this thesis, predators and prey learn to chase and avoid each other, respectively. The aim of this research is to investigate the use and content of adding an open communication channel to the predator agents. Using a genetic algorithm, predators and prey evolve in settings where withinand between-population communication is turned on or off. The predators evolve to only communicate when prey are not listening, that they tend to move towards communicating agents, and that most communication is done by predators eating from the carcass of a prey. Effectively, they perform sharing behavior to form a group, instead of hunting alone.
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    Explaining Bayesian decisions appropriate to users’ levels of expertise
    (2022-06-28) Gadhia, Manthan
    This thesis proposes a computational-level formalisation to address the lack of a user model in the present formalisation of MAP-Independence (Kwisthout, 2021) as it attempts to explicate all the intermediate variables which were relevant to some inferential decision made by a Decision Support System (DSS) based on a Bayesian network. The approach taken to mend this gap involves having the user indicate their level of domain knowledge to the DSS, which in turn is taken into account when a optimal justification is being computed for the user such that it matches their expertise in the field. In the process, many potential justifications are generated and a coherence measure is used to rank them. The likeness that decision making in human cognition shares with the notion of coherence motivated this choice over other Bayesian/likelihood based measures.
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    Arti cial Intelligence: From Feminine to Feminist
    (2022-01-28) Erscoi, Lelia
    The idea of Arti cial Intelligence can be seen as the immortalization of humanity's hopes through promises of a better, longer, easier life. These aspirations, however, are parallels with gender stereotypes about women, interpreted through the lter of technology: to have a good performance with little resources, to be subordinate to man, to always be a supportive gure, to always be in the best possible shape. These biases resulted in male-skewed technology constructed on the concept of the \ideal" woman | issue ampli ed by the lack of women in the AI eld. This paper addresses the sources of such hurtful technology and the women-adverse consequences of current applications of AI. Furthermore, I argue that AI holds the potential to be helpful to women and thus seek to illustrate some ways women can be empowered within and through AI.
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    Comparing quantified MAP-dependence to other measures of relevance in Bayesian networks
    (2022-06-20) Elteren, van, Merlijn
    In the fields of explainable AI and Bayesian networks, a recently introduced concept is a measure called MAP-independence. It is meant to assist in the justification of decisions made using decision support systems, by identifying irrelevant intermediate variables in a decision problem. In this paper, we argue that the binary nature of this measure is too crude, and may lead to a large set of relevant variables, some of which only change the Maximum A Posteriori outcome for very specific and maybe unlikely observations. We hypothesise that “quantifying” the measure (meaning “to change it from a binary measure to a measure on the [0,1] scale”) would allow us to identify differences in how relevant each variable in a set of relevant variables is. We name this new measure quantified MAP-dependence. We make an implementation which is, to the best of our knowledge, the first implementation of both MAP-independence and quantified MAP-dependence. Furthermore, we apply these measures to the ALARM network. For comparison, we also apply an older measure of relevance named intrinsic relevance. Based on the results, we conclude the following: Firstly, that our hypothesis about the usefulness of quantifying MAP-independence is true. Secondly, that compared to intrinsic relevance, quantified MAP-dependence describes a fundamentally different interpretation of what makes an intermediate variable relevant.

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