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In recent decades, academia has addressed a wide range of research topics in the field of ethical decision-making. Besides a great amount of research on ethical consumption, also the domain of ethical investments increasingly moves in the focus of scholars. While in this area most research focuses on whether socially or environmentally sustainable businesses outperform traditional investments financially or investigates the character traits as well as other socio-demographic factors of ethical investors, the impact of sustainable corporate conduct on the investment intentions of private investors still requires further research. Hence, we conducted two studies to shed more light on this highly relevant topic. After discussing the current state of research, in our first empirical study, we explore whether besides the traditional triad of risk, return, and liquidity, also sustainability exerts a significant impact on the willingness to invest. As hypothesized, we find that sustainability shows a clear and decisive impact in addition to the traditional factors. In a consecutive study, we investigate deeper into the sustainability-willingness to invest link. Here, our results show that improved sustainability might not pay off in terms of investment attractiveness, however and conversely, it certainly harms to conduct business in a non-sustainable manner, which cannot even be compensated by an increased return.
As a consequence of the real estate market crash after 2008, large investors invested a significant amount of wealth into single-family houses to construct a portfolio of rental dwellings, whose income is securitized in the capital. In some local housing markets, these investors own remarkable numbers of single-family houses. Furthermore, their trading activities have resulted in a new investment strategy, which exacerbates property wealth concentration and polarization. This new investment strategy and its portfolio optimization inspire curiosity about its influence on housing markets. This paper first aims to find an optimal portfolio strategy by employing an expected utility optimization from the terminal wealth, which adopts a stochastic model that includes a variety of economic states to estimate house prices. Second, it aims to analyze the effect of large investors on the housing market. The results show the investment strategies of large investors depend on the balance among economic state, maintenance cost, rental income, interest rate and investment willingness of large investors to housing and their effect depends on the state of the economy.
Dataflow process networks (DPNs) are intrinsically data-driven, i.e., node actions are not synchronized among each other and may fire whenever sufficient input operands arrived at a node. While the general model of computation (MoC) of DPNs does not impose further restrictions, many different subclasses of DPNs representing different dataflow MoCs have been considered over time. These classes mainly differ in the kinds of behaviors of the processes. A DPN may be heterogeneous in that different processes in the network belong to different classes of DPNs. A heterogeneous DPN can therefore be effectively used to model and to implement different components of a system with different kinds of processes and, therefore, different dataflow MoCs. This paper presents a model-based design based on different dataflow MoCs including their heterogeneous combinations. In particular, it covers the automatic software synthesis of systems from DPN models. The main objective is to validate, evaluate and compare the artifacts exhibited by different dataflow MoCs at the implementation level of systems under the supervision of a common design tool. Moreover, this work also offers an efficient synthesis method that targets and exploits heterogeneity in DPNs by generating implementations based on the kinds of behaviors of the processes. The proposed synthesis method provides a tool chain including different specialized code generators for specific dataflow MoCs, and a runtime system that finally maps models using a combination of different dataflow MoCs on cross-vendor target hardware.
Quantum Annealing (QA) is a metaheuristic for solving optimization problems in a time-efficient manner. Therefore, quantum mechanical effects are used to compute and evaluate many possible solutions of an optimization problem simultaneously. Recent studies have shown the potential of QA for solving such complex assignment problems within milliseconds. This also applies for the field of job shop scheduling, where the existing approaches however focus on small problem sizes. To assess the full potential of QA in this area for industry-scale problem formulations, it is necessary to consider larger problem instances and to evaluate the potentials of computing these job shop scheduling problems while finding a near-optimal solution in a time-efficient manner. Consequently, this paper presents a QA-based job shop scheduling. In particular, flexible job shop scheduling problems in various sizes are computed with QA, demonstrating the efficiency of the approach regarding scalability, solutions quality, and computing time. For the evaluation of the proposed approach, the solutions are compared in a scientific benchmark with state-of-the-art algorithms for solving flexible job shop scheduling problems. The results indicate that QA has the potential for solving flexible job shop scheduling problems in a time efficient manner. Even large problem instances can be computed within seconds, which offers the possibility for application in industry.
Es werden Ergebnisse aus einer Kontaktsimulation vorgestellt, welche die Oberflächenveränderung eines Axiallagers infolge von unerwünschtem elektrischem Stromdurchgang bei Mischreibung zeigen. Das hierzu entwickelte Modell berücksichtigt neben den Oberflächenrauheiten auch das nichtlineare Materialverhalten des Wälzlagerwerkstoffes. Im Gegensatz zu bekannten Modellierungsmethoden für ähnliche Problemstellungen, wird hier ein neuartiger Ansatz auf Basis einer gekoppelten Euler- Lagrange- Finite Element Simulation entwickelt. Das Modell liefert, mit experimentell geschädigten Oberflächen als Eingangsgröße, Erkenntnisse zum Traganteilsverhalten und weiterer mechanischer Kenngrößen infolge kombinierter mechanischer und elektrischer Belastungen.
Continuous-time regime-switching models are a very popular class of models for financial applications. In this work the so-called signal-to-noise matrix is introduced for hidden Markov models where the switching is driven by an unobservable Markov chain. Its relations to filtering, i.e. state estimation of the chain given the available observations, and portfolio optimization are investigated. A convergence result for the filter is derived: The filter converges to its invariant distribution if the eigenvalues of the signal-to-noise matrix converge to zero. This matrix is then also used to prove a mutual fund representation for regime-switching models and a corresponding market reduction which is consistent with filtering and portfolio optimization. Two canonical cases for the reduction are analyzed in more detail, the first based on the market regimes and the second depending on the eigenvalues. These considerations are presented both for observable and unobservable Markov chains. The results are illustrated by numerical simulations.
In this paper we investigate a utility maximization problem with drift uncertainty in a multivariate continuous-time Black–Scholes type financial market which may be incomplete. We impose a constraint on the admissible strategies that prevents a pure bond investment and we include uncertainty by means of ellipsoidal uncertainty sets for the drift. Our main results consist firstly in finding an explicit representation of the optimal strategy and the worst-case parameter, secondly in proving a minimax theorem that connects our robust utility maximization problem with the corresponding dual problem. Thirdly, we show that, as the degree of model uncertainty increases, the optimal strategy converges to a generalized uniform diversification strategy.
The dynamic behaviour of unsaturated sand rubber chips mixtures at various gravimetric contents is evaluated through an experimental study comprising resonant column tests in a fixed-free device. Chips were irregularly shaped with dimensions ranging from 5 to 14 mm. Three types of sand with different gradation have been considered. Relative density amounted to 0.5 for all specimens. Due to the large size of the chips, the diameter of the specimens had to be equal to 100 mm, which in turn required a re-calibration of the device assuming a frequency-dependent drive head inertia. The effects of confining stress, rubber chips content, and sand gradation on shear modulus and damping ratio are determined over wide ranges of the shear strain. At small strains, as known for sands, increasing the confining stress stiffens the mixtures. Increasing the rubber chips content reduces significantly the shear modulus and increases the damping ratio. At higher strains, increasing the confining stress or the rubber content flattens the reduction of the shear modulus with strain. Damping at high strains does not show any appreciable dependence on rubber content. Unloading–reloading sequences are used to assess shear modulus degradation and threshold strains. Finally, design equations are derived from the test results to predict the dynamic response of the composite material.
Many practical optimisation problems have conflicting objectives, which should be addressed by multi-criteria optimisation (MCO), i.e. by determining the set of best compromises, the Pareto set (PS), along with its picture in parameter space (PSPS). In previous work on low-dimensional MCO problems, we have found characteristic topological features of the PS and PSPS, which depend on the dimensionality of the parameter space M and the objective space N. E.g., M = 2 and N = 3 yields triangles with needle-like extensions. The reasons for these topological features were unknown so far. Here, we show that they are to be expected if all objective functions of the MCO satisfy two conditions: (a) they can be approximated by quadratic functions and (b) one of the eigenvalues of the Hessian matrix evaluated at the function’s minimum is small compared to the other eigenvalues. Objective functions which meet conditions (a) and (b) have a valley-like topology, for which the valley lies in the direction of the eigenvector corresponding to the lowest eigenvalue. The PSPS can be estimated by starting at the minimum of an objective function, following the valley, and combining these lines for all objective functions. The PS is obtained by evaluating the objective functions. We believe that the conditions (a) and (b) are met in many practical problems and discuss an example from molecular modelling. The improved understanding of the features of these MCO problems opens the route for designing methods for swiftly finding estimates of their PS and PSPS.
This contribution defends two claims. The first is about why thought experiments are so relevant and powerful in mathematics. Heuristics and proof are not strictly and, therefore, the relevance of thought experiments is not contained to heuristics. The main argument is based on a semiotic analysis of how mathematics works with signs. Seen in this way, formal symbols do not eliminate thought experiments (replacing them by something rigorous), but rather provide a new stage for them. The formal world resembles the empirical world in that it calls for exploration and offers surprises. This presents a major reason why thought experiments occur both in empirical sciences and in mathematics. The second claim is about a looming aporia that signals the limitation of thought experiments. This aporia arises when mathematical arguments cease to be fully accessible, thus violating a precondition for experimenting in thought. The contribution focuses on the work of Vladimir Voevodsky (1966–2017, Fields medalist in 2002) who argued that even very pure branches of mathematics cannot avoid inaccessibility of proof. Furthermore, he suggested that computer verification is a feasible path forward, but only if proof is not modeled in terms of formal logic.