66 research outputs found
Das Kapitalanleger-Musterverfahrensgesetz als Lösung zur Bewältigung von Massenverfahren
8.000 kg Papier, überquellende Geschäftsstellenzimmer, zusammenbrechende Faxgeräte und Aussichten auf Verfahrensdauern von 15 Jahren - die „Telekom-Prozesse“ vor dem Landgericht Frankfurt zeigten die Probleme auf, vor die Massenverfahren das deutsche Zivilprozessrecht stellen. Diesem versuchte der Gesetzgeber im Jahr 2005 mit dem KapMuG zu begegnen.
Auf der Grundlage einer Untersuchung der Möglichkeiten und Grenzen des überkommenen deutschen Zivilprozessrechts sowie ausgewählter ausländischer Verfahrensregelungen überprüft der Autor die Praxistauglichkeit des KapMuG. Im Ergebnis ist diese zu bejahen. Zudem wäre eine Ausdehnung des Verfahrens nach dem KapMuG auf andere Bereiche sinnvoll. Dabei sind jedoch vorhandene Schwächen des KapMuG zu beheben. Hierfür liefert der Autor konkrete Verbesserungsvorschläge. <br/
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Emergent inequality and business cycles in a simple behavioral macroeconomic model
Standard macroeconomic models assume that households are rational in the sense that they are perfect utility maximizers and explain economic dynamics in terms of shocks that drive the economy away from the steady state. Here we build on a standard macroeconomic model in which a single rational representative household makes a savings decision of how much to consume or invest. In our model, households are myopic boundedly rational heterogeneous agents embedded in a social network. From time to time each household updates its savings rate by copying the savings rate of its neighbor with the highest consumption. If the updating time is short, the economy is stuck in a poverty trap, but for longer updating times economic output approaches its optimal value, and we observe a critical transition to an economy with irregular endogenous oscillations in economic output, resembling a business cycle. In this regime households divide into two groups: poor households with low savings rates and rich households with high savings rates. Thus, inequality and economic dynamics both occur spontaneously as a consequence of imperfect household decision-making. Adding a few “rational” agents with a fixed savings rate equal to the long-term optimum allows us to match business cycle timescales. Our work here supports an alternative program of research that substitutes utility maximization for behaviorally grounded decision-making
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
We introduce the \texttt{pyunicorn} (Pythonic unified complex network and
recurrence analysis toolbox) open source software package for applying and
combining modern methods of data analysis and modeling from complex network
theory and nonlinear time series analysis. \texttt{pyunicorn} is a fully
object-oriented and easily parallelizable package written in the language
Python. It allows for the construction of functional networks such as climate
networks in climatology or functional brain networks in neuroscience
representing the structure of statistical interrelationships in large data sets
of time series and, subsequently, investigating this structure using advanced
methods of complex network theory such as measures and models for spatial
networks, networks of interacting networks, node-weighted statistics or network
surrogates. Additionally, \texttt{pyunicorn} provides insights into the
nonlinear dynamics of complex systems as recorded in uni- and multivariate time
series from a non-traditional perspective by means of recurrence quantification
analysis (RQA), recurrence networks, visibility graphs and construction of
surrogate time series. The range of possible applications of the library is
outlined, drawing on several examples mainly from the field of climatology.Comment: 28 pages, 17 figure
Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World-Earth modeling framework
Analysis of Earth system dynamics in the Anthropocene requires explicitly taking into account the increasing magnitude of processes operating in human societies, their cultures, economies and technosphere and their growing feedback entanglement with those in the physical, chemical and biological systems of the planet. However, current state-of-the-art Earth system models do not represent dynamic human societies and their feedback interactions with the biogeophysical Earth system and macroeconomic integrated assessment models typically do so only with limited scope. This paper (i) proposes design principles for constructing world-Earth models (WEMs) for Earth system analysis of the Anthropocene, i.e., models of social (world)-ecological (Earth) coevolution on up to planetary scales, and (ii) presents the copan:CORE open simulation modeling framework for developing, composing and analyzing such WEMs based on the proposed principles. The framework provides a modular structure to flexibly construct and study WEMs. These can contain biophysical (e.g., carbon cycle dynamics), socio-metabolic or economic (e.g., economic growth or energy system changes), and sociocultural processes (e.g., voting on climate policies or changing social norms) and their feedback interactions, and they are based on elementary entity types, e.g., grid cells and social systems. Thereby, copan:CORE enables the epistemic flexibility needed for contributions towards Earth system analysis of the Anthropocene given the large diversity of competing theories and methodologies used for describing socio-metabolic or economic and sociocultural processes in the Earth system by various fields and schools of thought. To illustrate the capabilities of the framework, we present an exemplary and highly stylized WEM implemented in copan:CORE that illustrates how endogenizing sociocultural processes and feedbacks such as voting on climate policies based on socially learned environmental awareness could fundamentally change macroscopic model outcomes
Earth system modeling with endogenous and dynamic human societies: the copan:CORE open World-Earth modeling framework
Analysis of Earth system dynamics in the Anthropocene requires to explicitly
take into account the increasing magnitude of processes operating in human
societies, their cultures, economies and technosphere and their growing
feedback entanglement with those in the physical, chemical and biological
systems of the planet. However, current state-of-the-art Earth System Models do
not represent dynamic human societies and their feedback interactions with the
biogeophysical Earth system and macroeconomic Integrated Assessment Models
typically do so only with limited scope. This paper (i) proposes design
principles for constructing World-Earth Models (WEM) for Earth system analysis
of the Anthropocene, i.e., models of social (World) - ecological (Earth)
co-evolution on up to planetary scales, and (ii) presents the copan:CORE open
simulation modeling framework for developing, composing and analyzing such WEMs
based on the proposed principles. The framework provides a modular structure to
flexibly construct and study WEMs. These can contain biophysical (e.g. carbon
cycle dynamics), socio-metabolic/economic (e.g. economic growth) and
socio-cultural processes (e.g. voting on climate policies or changing social
norms) and their feedback interactions, and are based on elementary entity
types, e.g., grid cells and social systems. Thereby, copan:CORE enables the
epistemic flexibility needed for contributions towards Earth system analysis of
the Anthropocene given the large diversity of competing theories and
methodologies used for describing socio-metabolic/economic and socio-cultural
processes in the Earth system by various fields and schools of thought. To
illustrate the capabilities of the framework, we present an exemplary and
highly stylized WEM implemented in copan:CORE that illustrates how endogenizing
socio-cultural processes and feedbacks could fundamentally change macroscopic
model outcomes
Node-weighted measures for complex networks with spatially embedded, sampled, or differently sized nodes
When network and graph theory are used in the study of complex systems, a
typically finite set of nodes of the network under consideration is frequently
either explicitly or implicitly considered representative of a much larger
finite or infinite region or set of objects of interest. The selection
procedure, e.g., formation of a subset or some kind of discretization or
aggregation, typically results in individual nodes of the studied network
representing quite differently sized parts of the domain of interest. This
heterogeneity may induce substantial bias and artifacts in derived network
statistics. To avoid this bias, we propose an axiomatic scheme based on the
idea of node splitting invariance to derive consistently weighted variants of
various commonly used statistical network measures. The practical relevance and
applicability of our approach is demonstrated for a number of example networks
from different fields of research, and is shown to be of fundamental importance
in particular in the study of spatially embedded functional networks derived
from time series as studied in, e.g., neuroscience and climatology.Comment: 21 pages, 13 figure
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