Contributions in Merlin

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We argue that job performance appraisal is an agency problem with asymmetric transfer values: an employee is paid in proportion to the rating received from his line manager, who only partially internalizes the resultant payroll cost. This asymmetry in rating valuations is based on evidence that managers are not fully accountable for payroll expense, with the degree of unaccountability increasing in firm size. We develop a nested agency model of economic organization of a firm with unaccountable managers, which in equilibrium obtains the firm-size wage effects - the large-firm wage premium and inverse relationship between firm size and wage dispersion.

In our modern world we are facing an increasing complexity which creates higher risks as well as new interdependencies in the socioeconomic environment. Linear models tend to fail when dealing with complex real-world problems. System dynamics may deliver new tools to find sustainable solutions to these problems. However, these tools have some limitations. While the quantitative methods are sometimes too complex, the qualitative have limited capacities for detailed analysis. This is especially true with regard to the time dimension. Therefore, we developed a series of analytical tools including a Cross-Time Matrix to address this gap. Decision makers in business and politics can easily apply our tools. Based on the Cross-Time Matrix and combined with the Impact Matrix, one can determine whether a variable is suited for intervention or one can calculate how long it will take until an impulse from one variable in the system arrives at another. For our calculations we used an adapted version of the Floyd-Warshall algorithm.

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Designing component-based systems (CBS) that exhibit a good trade-off between multiple quality criteria is hard. Even after functional design, many remaining degrees of freedom of different types (e.g.\ component allocation, component selection, server configuration) in the CBS span a large, discontinuous design space. Automated approaches have been proposed to optimise CBS models, but they only consider a limited set of degrees of freedom, e.g.\ they only optimise the selection of components without considering the allocation, or vice versa. We propose a flexible and extensible formulation of the design space for optimising any CBS model for a number of quality properties and an arbitrary number of degrees of freedom. With this design space formulation, a generic quality optimisation framework that is independent of the used CBS metamodel can apply multi-objective metaheuristic optimisation such as evolutionary algorithms.

Designing software architectures that exhibit a good trade-off between multiple quality attributes is hard. Even with a given functional design, many degrees of freedom in the software architecture (e.g. component deployment or server configuration) span a large design space. In current practice, software architects try to find good solutions manually, which is time-consuming, can be error-prone and can lead to suboptimal designs.We propose an automated approach guided by architectural tactics to search the design space for good solutions. Our approach applies multi-objective evolutionary optimization to software architectures modelled with the Palladio Component Model. Software architects can then make well-informed trade-off decisions and choose the best architecture for their situation.To validate our approach, we applied it to the architecture models of two systems, a business reporting system and an industrial control system from ABB. The approach was able to find meaningful trade-offs leading to significant performance improvements or costs savings. The novel use of tactics decreased the time needed to find good solutions by up to 80\%.

This study aims to explore a control architecture that enables the control of a soft and flexible octopus-like arm for an object reaching task. Inspired by the division of functionality between the central and peripheral nervous systems of a real octopus, we discuss that the important factor of the control is not to regulate the arm muscles one by one but rather to control them globally with appropriate timing, and we propose an architecture equipped with a recurrent neural network (RNN). By setting the task environment for the reaching behavior, and training the network with an incremental learning strategy, we evaluate whether the network is then able to achieve the reaching behavior or not. As a result, we show that the RNN can successfully achieve the reaching behavior, exploiting the physical dynamics of the arm due to the timing based control.