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In den letzten Jahrzehnten ist der Trend der Verselbstständigung in vielen Kommunen zu beobachten. Ein Großteil der öffentlichen Leistungserbringer wird mittlerweile als privatrechtliche Gesellschaften in einem wettbewerbsorientierten Umfeld geführt. Während viele Forscher Ausgliederungen in Form von nachgeordneten Behörden auf Bundesebene untersuchen und diese Reformwelle als einen faktischen Autonomisierungsprozess beschreiben, gibt es nur einige wenige Studien, die sich explizit mit den Autonomisierungstendenzen auf Kommunalebene auseinandersetzen. Daher fehlt es an empirischen Erkenntnissen zur Steuerung der kommunalen Beteiligungen.
In dieser Arbeit werden die Steuerungsarrangements deutscher Großstädte erstmals aus Sicht der Gesteuerten beleuchtet. Das Untersuchungsziel der vorliegenden Forschungsarbeit besteht darin, Flexibilisierungstendenzen in mehrheitlich kommunalen Unternehmen zu identifizieren und hierfür Erklärungsfaktoren zu identifizieren. Die Forschungsfrage lautet: Welche instrumentellen und relationalen Faktoren beeinflussen die Managementautonomie in kommunalen Mehrheitsbeteiligungen?
Dabei interessiert insbesondere die Einflussnahme der Kommunen auf verschiedene Tätigkeitsbereiche ihrer Ausgliederungen. Über diese unternehmensspezifischen Sachverhalte ist in Deutschland fast nichts und international nur sehr wenig Empirisches bekannt. Zur Beantwortung der Forschungsfrage hat der Autor auf Basis der Transaktionskosten- und der Social-Exchange-Theorie einen Analyserahmen erstellt. Die aufgestellten Hypothesen wurden mit einer großflächigen Umfrage bei 243 Unternehmen in den 39 größten deutschen Städten empirisch getestet.
Im Ergebnis zeigen sich mehrere empirische Erkenntnisse: Erstens konnten mittels Faktorenanalyse vier unabhängige Faktoren von Managementautonomie in kommunalen Unternehmen identifiziert werden: Personalautonomie, Generelles Management, Preisautonomie und Strategische Fragen. Während die Kommunen ihren Beteiligungen einen hohen Grad an Personalautonomie zugestehen, unterliegen vor allem strategische Investitionsentscheidungen wie die finanzielle Beteiligung an Tochterfirmen, große Projektvorhaben, Diversifikationsentscheidungen oder Kreditautfnahmen einem starken politischen Einfluss.
Zweitens führt eine Rechtsformänderung und die Platzierung in einem Wettbewerbsumfeld (auch bekannt als Corporatisation) vor allem zu einer größeren Flexibilisierung der Personal- und Preispolitik, wirkt sich allerdings wenig auf die weiteren Faktoren der Managementautonomie, Generelles Management und Strategische Entscheidungen, aus. Somit behalten die Kommunen ihre Möglichkeit, auf wichtige Unternehmensfragen der Beteiligung Einfluss zu nehmen, auch im Fall einer Formalprivatisierung bei.
Letztlich können zur Erklärung der Autonomiefaktoren transaktionskostenbasierte und relationale Faktoren ergänzend herangezogen werden. In den Transaktionsspezifika wirken vor allem der wahrgenommene Wettbewerb in der Branche, die Messbarkeit der Leistung, Branchenvariablen, die Anzahl der Politiker im Aufsichtsrat und die eingesetzten Steuerungsmechanismen. In den relationalen Faktoren setzen sich die Variablen gegenseitiges Vertrauen, Effektivität der Aufsichtsräte, Informationsaustausch, Rollenkonflikte, Rollenambivalenzen und Geschäftsführererfahrung im Sektor durch.
We demonstrate that a multiple delayed feedback is a powerful tool to control coherence properties of autonomous self-sustained oscillators. We derive the equation for the phase dynamics in presence of noise and delay, and analyze it analytically. In Gaussian approximation a closed set of equations for the frequency and the diffusion constant is obtained. Solutions of these equations are in good agreement with direct numerical simulations.
In the present work, we use symbolic regression for automated modeling of dynamical systems. Symbolic regression is a powerful and general method suitable for data-driven identification of mathematical expressions. In particular, the structure and parameters of those expressions are identified simultaneously.
We consider two main variants of symbolic regression: sparse regression-based and genetic programming-based symbolic regression. Both are applied to identification, prediction and control of dynamical systems.
We introduce a new methodology for the data-driven identification of nonlinear dynamics for systems undergoing abrupt changes. Building on a sparse regression algorithm derived earlier, the model after the change is defined as a minimum update with respect to a reference model of the system identified prior to the change. The technique is successfully exemplified on the chaotic Lorenz system and the van der Pol oscillator. Issues such as computational complexity, robustness against noise and requirements with respect to data volume are investigated.
We show how symbolic regression can be used for time series prediction. Again, issues such as robustness against noise and convergence rate are investigated us- ing the harmonic oscillator as a toy problem. In combination with embedding, we demonstrate the prediction of a propagating front in coupled FitzHugh-Nagumo oscillators. Additionally, we show how we can enhance numerical weather predictions to commercially forecast power production of green energy power plants.
We employ symbolic regression for synchronization control in coupled van der Pol oscillators. Different coupling topologies are investigated. We address issues such as plausibility and stability of the control laws found. The toolkit has been made open source and is used in turbulence control applications.
Genetic programming based symbolic regression is very versatile and can be adapted to many optimization problems. The heuristic-based algorithm allows for cost efficient optimization of complex tasks.
We emphasize the ability of symbolic regression to yield white-box models. In contrast to black-box models, such models are accessible and interpretable which allows the usage of established tool chains.
In eye-movement control during reading, advanced process-oriented models have been developed to reproduce behavioral data. So far, model complexity and large numbers of model parameters prevented rigorous statistical inference and modeling of interindividual differences. Here we propose a Bayesian approach to both problems for one representative computational model of sentence reading (SWIFT; Engbert et al., Psychological Review, 112, 2005, pp. 777-813). We used experimental data from 36 subjects who read the text in a normal and one of four manipulated text layouts (e.g., mirrored and scrambled letters). The SWIFT model was fitted to subjects and experimental conditions individually to investigate between- subject variability. Based on posterior distributions of model parameters, fixation probabilities and durations are reliably recovered from simulated data and reproduced for withheld empirical data, at both the experimental condition and subject levels. A subsequent statistical analysis of model parameters across reading conditions generates model-driven explanations for observable effects between conditions.
We examine a special class of dynamic pricing and advertising models for the sale of perishable goods, including marginal unit costs and inventory holding costs. The time horizon is assumed to be finite and we allow several model parameters to be dependent on time. For the stochastic version of the model, we derive closed-form expressions of the value function as well as of the optimal pricing and advertising policy in feedback form. Moreover, we show that for small unit shares, the model converges to a deterministic version of the problem, whose explicit solution is characterized by an overage and an underage case. We quantify the close relationship between the open-loop solution of the deterministic model and the expected evolution of optimally controlled stochastic sales processes. For both models, we derive sensitivity results. We find that in the case of positive holding costs, on average, optimal prices increase in time and advertising rates decrease. Furthermore, we analytically verify the excellent quality of optimal feedback policies of deterministic models applied in stochastic models. (C) 2015 Elsevier B.V. All rights reserved.
Standing waves are studied as solutions of a complex Ginsburg-Landau equation subjected to local and global time-delay feedback terms. The onset of standing waves is studied at the instability of the homogeneous periodic solution with respect to spatially periodic perturbations. The solution of this spatiotemporal wave pattern is given and is compared to the homogeneous periodic solution.
In two-dimensional reaction-diffusion systems, local curvature perturbations on traveling waves are typically damped out and vanish. However, if the inhibitor diffuses much faster than the activator, transversal instabilities can arise, leading from flat to folded, spatio-temporally modulated waves and to spreading spiral turbulence. Here, we propose a scheme to induce or inhibit these instabilities via a spatio-temporal feedback loop. In a piecewise-linear version of the FitzHugh-Nagumo model, transversal instabilities and spiral turbulence in the uncontrolled system are shown to be suppressed in the presence of control, thereby stabilizing plane wave propagation. Conversely, in numerical simulations with the modified Oregonator model for the photosensitive Belousov-Zhabotinsky reaction, which does not exhibit transversal instabilities on its own, we demonstrate the feasibility of inducing transversal instabilities and study the emerging wave patterns in a well-controlled manner.
In two-dimensional reaction-diffusion systems, local curvature perturbations on traveling waves are typically damped out and vanish. However, if the inhibitor diffuses much faster than the activator, transversal instabilities can arise, leading from flat to folded, spatio-temporally modulated waves and to spreading spiral turbulence. Here, we propose a scheme to induce or inhibit these instabilities via a spatio-temporal feedback loop. In a piecewise-linear version of the FitzHugh-Nagumo model, transversal instabilities and spiral turbulence in the uncontrolled system are shown to be suppressed in the presence of control, thereby stabilizing plane wave propagation. Conversely, in numerical simulations with the modified Oregonator model for the photosensitive Belousov-Zhabotinsky reaction, which does not exhibit transversal instabilities on its own, we demonstrate the feasibility of inducing transversal instabilities and study the emerging wave patterns in a well-controlled manner.