From a price range between 100 and 120 USD (U.S. dollars) per barrel in 2011-2014, the crude oil price fell from mid-2014 onwards, reaching a level of 26 USD per barrel in January 2016. Here we assess the economic consequences of this strong decrease in the oil price. A retrospective analysis based on data of the past 25 years sheds light on the vulnerability of oil-producing regions to the oil price volatility. Gross domestic product (GDP) and government revenues in many Gulf countries exhibit a strong dependence on oil, while more diversified economies improve resilience to oil price shocks. The lack of a sovereign wealth fund, in combination with limited oil reserves, makes parts of Sub-Saharan Africa particularly vulnerable to sustained periods of low oil prices. Next, we estimate the macroeconomic impacts of a 60% oil price drop for all regions in the world. A numerical simulation yields a global GDP increase of roughly 1% and illustrates how the regional impact on GDP relates to oil export dependence. Finally, we reflect on the broader implications (such as migration flows) of macroeconomic responses to oil prices and look ahead to the challenge of structural change in a world committed to limiting global warming.

When modeling medium and long-term challenges we need a reference path of economic development (the so-called baseline). Because sectoral models often offer a more fundamental understanding of future developments for specific sectors, many CGE modeling teams have adopted approaches for linking their models to sectoral models to generate baselines. Linked models include agricultural sector, energy sector, biophysical and macroeconomic models. We systematically compare and discuss approaches of linking CGE models to sectoral models for the baseline calibration procedure and discuss challenges and best practices. We identify different types of linking approaches which we divide into a) one-way, and b) twoway linking. These two types of linking approaches are then analyzed with respect to the degree of consistency of the linkage, information exchanged, as well as compromises in aggregations and definitions. Based on our assessment, we discuss challenges and conclude with suggestions for best practices and research recommendations.

We define a semantics for conditionals in terms of stochastic graphs which gives a straightforward and simple method of evaluating the probabilities of conditionals. It seems to be a good and useful method in the cases already discussed in the literature, and it can easily be extended to cover more complex situations. In particular, it allows us to describe several possible interpretations of the conditional (the global and the local interpretation, and generalizations of them) and to formalize some intuitively valid but formally incorrect considerations concerning the probabilities of conditionals under these two interpretations. It also yields a powerful method of handling more complex issues (such as nested conditionals). The stochastic graph semantics provides a satisfactory answer to Lewis's arguments against the PC = CP principle, and defends important intuitions which connect the notion of probability of a conditional with the (standard) notion of conditional probability. It also illustrates the general problem of finding formal explications of philosophically important notions and applying mathematical methods in analyzing philosophical issues.The problem of estimating the probabilities of conditional sentences is interesting for at least two reasons:First, it seems that in many cases it is not clear which estimation is "The True Estimation" (as different language users give different values).Second, providing a coherent and robust method of evaluating these probabilities would give us a better understanding of many other problems connected with conditionals-e.g. the problem of counterfactuals.

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It is a well-known fact that mathematics plays a crucial role in physics; in fact, it is virtually impossible to imagine contemporary physics without it. But it is questionable whether mathematical concepts could ever play such a role in psychology or philosophy. In this paper, we set out to examine a rather unobvious example of the application of topology, in the form of the theory of persons proposed by Kurt Lewin in his Principles of Topological Psychology. Our aim is to show that this branch of mathematics can furnish a natural conceptual system for Gestalt psychology, in that it provides effective tools for describing global qualitative aspects of the latter’s object of investigation. We distinguish three possible ways in which mathematics can contribute to this: explanation, explication (construed in the spirit of Carnap) and metaphor. We hold that all three of these can be usefully characterized as throwing light on their subject matter, and argue that in each case this contrasts with the role of explanations in physics. Mathematics itself, we argue, provides something different from such explanations when applied in the field of psychology, and this is nevertheless still cognitively fruitful.