Die gegenwärtige Anschauung vom Aufbau des Weltgebäudes

The Science of Nature -     

Restrictions and options: A heuristic tool to integrate knowledge for strategies towards a sustainable development

SUMMARY

This article presents the heuristic ‘Restrictions and Options’ that was created in an interdisciplinary research team to identify restricting and supporting factors regarding sustainable ways of actions for different institutional and individual actors in the need-field of nutrition. The heuristic format is first presented on a general level. Then it is combined with two different approaches: first, with a systemic constructivist approach to organization theory, and second, with the ipsative theory of action (psychology). Its abilities are illustrated by examples from two research projects.

The heuristic tool was developed to help identify relevant factors that restrict or support sustainable development in two empirical settings and also could be used to compare the findings. Combining the two approaches remains for future research.     

A Perspective on Nonstationarity and Water Management1

Hirsch, Robert M., 2011. A Perspective on Nonstationarity and Water Management. Journal of the American Water Resources Association (JAWRA) 47(3):436‐446. DOI: 10.1111/j.1752‐1688.2011.00539.x Abstract: This essay offers some perspectives on climate‐related nonstationarity and water resources. Hydrologists must not lose sight of the many sources of nonstationarity, recognizing that many of them may be of much greater magnitude than those that may arise from climate change. It is paradoxical that statistical and deterministic approaches give us better insights about changes in mean conditions than about the tails of probability distributions, and yet the tails are very important to water management. Another paradox is that it is difficult to distinguish between long‐term hydrologic persistence and trend. Using very long hydrologic records is helpful in mitigating this problem, but does not guarantee success. Empirical approaches, using long‐term hydrologic records, should be an important part of the portfolio of research being applied to understand the hydrologic response to climate change. An example presented here shows very mixed results for trends in the size of the annual floods, with some strong clusters of positive trends and a strong cluster of negative trends. The potential for nonstationarity highlights the importance of the continuity of hydrologic records, the need for repeated analysis of the data as the time series grow, and the need for a well‐trained cadre of scientists and engineers, ready to interpret the data and use those analyses to help adjust the management of our water resources.     

The resource curse revisited and revised: A tale of paradoxes and red herrings

We critically evaluate the empirical basis for the so-called resource curse and find that, despite the topic's popularity in economics and political science research, this apparent paradox may be a red herring. The most commonly used measure of “resource abundance” can be more usefully interpreted as a proxy for “resource dependence”—endogenous to underlying structural factors. In multiple estimations that combine resource abundance and dependence, institutional, and constitutional variables, we find that (i) resource abundance, constitutions, and institutions determine resource dependence, (ii) resource dependence does not affect growth, and (iii) resource abundance positively affects growth and institutional quality.     

“Applicability of the t-Test for Detecting Trends in Water Quality Variables,” by Robert H. Montgomery and Jim C. Loftis2

Montgomery and Loftis (1987) have listed several situations for which the t-test does not accurately reproduce Type I errors, and should therefore be avoided. Characteristics common to water quality data (skewness or other non-normality, presence of outliers and less-thans) also reduce the power of the t-test, in relation to nonparametric alternatives. Thus if one is interested in reaching correct decisions when trends or differences exist, and not just when they do not, the t-test should not be considered “robust” (in the sense of being generally applicable) when its assumptions are violated. Further, t-tests assume that differences in means are relevant (the mean is a good measure of central tendency), and that data groups differ by some additive amount. When all of these assumptions are recognized, and in light of the availability of truly robust and comparatively powerful non-parametric alternatives, we believe there is little applicability of the t-test for detecting trends or differences in water quality variables.