Theory and Practice of Reconciliation in Rwanda
- During recent years, scholars working on the peacebuilding process in Rwanda have often tended to single out specific aspects, for instance judicial responses to the genocide. Little research has been done, however, on the diversity of approaches that constitute the “reconciliation landscape” in Rwanda today. Basing itself on data from field research in 2006, this paper seeks to shed some light on the many programmes carried out in Rwanda related to reconciliation work. Emphasis is put on two case studies. While establishing a theoretical framework of the reconciliation process in the first part of the paper, the following chapters attempt to explain how this relates to the practice of reconciliation in the Rwandan context. The data collected suggest that in the face of political constraints, the Rwandan government must in part rely on civil society actors for the achievement of their goals of “unity and reconciliation”. The multitude of initiatives from actors with a wide range of motivations and approaches should be seen as complementary, while some may have to make up for the shortcomings and constraints of others.
Reputation in Multi Agent Systems and the Incentives to Provide Feedback
- The emergence of the Internet leads to a vast increase in the number of interactions between parties that are completely alien to each other. In general, such transactions are likely to be subject to fraud and cheating. If such systems use computerized rational agents to negotiate and execute transactions, mechanisms that lead to favorable outcomes for all parties instead of giving rise to defective behavior are necessary to make the system work: trust and reputation mechanisms. This paper examines different incentive mechanisms helping these trust and reputation mechanisms in eliciting users to report own experiences honestly.
Measurement of emotional reactions to television advertisements – A state of the art review
- Human emotions and their measurement present a complex and intricate affair
which perpetuates an ongoing discourse in marketing research. Since emotions
play a pivotal role in the success of advertisements, the exploitation of tools for
their precise measurement is crucial to researchers and practitioners alike. Yet,
there is no single gold standard instrument existent that enables a comprehensive
detection of all emotion facets at once. This thesis therefore focuses on the theoretical
conceptualization of emotion, and afterwards presents a variety of measurement
methods that address different emotion components. Thereby, particular
emphasis is placed on their applicability as regards television commercials.
Optimal sensor placement for linear systems
- The aim of sensor placement is to observe the state of a dynamical system while using only a small part of the available output information. Thus, the observer does not need sensors at every possible node of the system. We use sensor placement because it is not practical for large-scale networks, such as power grids, to place sensors at each node. With an optimal sensor placement we obtain a subset of sensors which minimizes the observer error in comparison to any other subset of the same size. This means we generate an optimal observation with the given number of sensors.
We compute the observer error, for the linear dynamical systems we consider, with the H2-norm of the observer error system. In this approach, we optimize both the subset of selected sensors and the observer gain matrix in parallel. The optimization problem is non-convex both in a constraint, which bounds the H2-norm, as well as in the objective function which uses a l0-norm to count the used sensors. To obtain a semidefinite program, we first relax the l0-norm by an
iterative reweighted l1-norm. Second, we use a reformulation of the H2-norm with linear matrix inequalities to replace an occuring bilinear and therefore non-convex term.
We use this computationally efficient formulation of the sensor placement problem to derive three algorithms. Furthermore, existing algorithms, which do not use the convex reformulation of the optimization problem, were implemented. The algorithms are compared extensively relating to execution time, performance of the chosen sensors, and the applicability on a practical problem. The practical problem is a model of a high-voltage power grid with the aim to measure the phase angles and the frequencies at every node. The result of the comparison is that a algorithm with a greedy approach solves the optimization problem fast and usually with a good solution. However, this algorithm is problematic because the shortsighted greedy approach cannot exclude that a worst case solution is generated. The best results in general were produced by a novel approach made in this thesis. This novel algorithm iteratively solves the relaxed optimization problem and finds near-optimal sensor subsets.