Commercial Policy Variability, Bindings, and Market Access

Protection unconstrained by rules often varies substantially over time. Rules-based disciplines, such as WTO tariff bindings and bindings on market access in services, constrain this variability. We examine the theoretical effects of such constraints on the expected cost of protection and offer a formalization of the concept of “market access,” emphasizing both the first and second moments of the distribution of protection. As an illustration, we provide a stylized examination of Uruguay Round bindings on wheat.expected costs of protection, commercial policy uncertainty, market access WTO, tariff bindings.

QCD: Challenges for the Future

Despite many experimental verifications of the correctness of our basic understanding of QCD, there remain numerous open questions in strong interaction physics and we focus on the role of future colliders in addressing these questions. We discuss possible advances in the measurement of $\alpha_s$, in the study of parton distribution functions, and in the understanding of low $x$ physics at present colliders and potential new facilities. We also touch briefly on the role of spin physics in advancing our understanding of QCD.Comment: 12 pages, LATEX2e with snow2e, epsfig and 2 figures. Also available at http://penguin.phy.bnl.gov/~dawson/qcdsnow.ps . QCD working group summary at DPF/DPB Summer Study on New Directions for High Energy Physics, Snowmass, CO, June 25- July 12, 199

Overcoming the data crisis in biodiversity conservation

How can we track population trends when monitoring data are sparse? Population declines can go undetected, despite ongoing threats. For example, only one of every 200 harvested species are monitored. This gap leads to uncertainty about the seriousness of declines and hampers effective conservation. Collecting more data is important, but we can also make better use of existing information. Prior knowledge of physiology, life history, and community ecology can be used to inform population models. Additionally, in multispecies models, information can be shared among taxa based on phylogenetic, spatial, or temporal proximity. By exploiting generalities across species that share evolutionary or ecological characteristics within Bayesian hierarchical models, we can fill crucial gaps in the assessment of species’ status with unparalleled quantitative rigor

Integrated modelling of social-ecological systems for climate change adaptation

Analysis of climate change risks in support of policymakers to set effective adaptation policies requires an innovative yet rigorous approach towards integrated modelling (IM) of social-ecological systems (SES). Despite continuous advances, IM still faces various challenges that span through both unresolved methodological issues as well as data requirements. On the methodological side, significant improvements have been made for better understanding the dynamics of complex social and ecological systems, but still, the literature and proposed solutions are fragmented. This paper explores available modelling approaches suitable for long-term analysis of SES for supporting climate change adaptation (CCA). It proposes their classification into seven groups, identifies their main strengths and limitations, and lists current data sources of greatest interest. Upon that synthesis, the paper identifies directions for orienting the development of innovative IM, for improved analysis and management of socio-economic systems, thus providing better foundations for effective CCA

Propagation of aleatory and epistemic uncertainties in the model for the design of a flood protection dike

International audienceTraditionally, probability distributions are used in risk analysis to represent the uncertainty associated to random (aleatory) phenomena. The parameters (e.g., their mean, variance, ...) of these distributions are usually affected by epistemic (state-of-knowledge) uncertainty, due to limited experience and incomplete knowledge about the phenomena that the distributions represent: the uncertainty framework is then characterized by two hierarchical levels of uncertainty. Probability distributions may be used to characterize also the epistemic uncertainty affecting the parameters of the probability distributions. However, when sufficiently informative data are not available, an alternative and proper way to do this might be by means of possibilistic distributions. In this paper, we use probability distributions to represent aleatory uncertainty and possibility distributions to describe the epistemic uncertainty associated to the poorly known parameters of such probability distributions. A hybrid method is used to hierarchically propagate the two types of uncertainty. The results obtained on a risk model for the design of a flood protection dike are compared with those of a traditional, purely probabilistic, two-dimensional (or double) Monte Carlo approach. To the best of the authors' knowledge, this is the first time that a hybrid Monte Carlo and possibilistic method is tailored to propagate the uncertainties in a risk model when the uncertainty framework is characterized by two hierarchical levels. The results of the case study show that the hybrid approach produces risk estimates that are more conservative than (or at least comparable to) those obtained by the two-dimensional Monte Carlo method

Mathematics and Statistics in the Social Sciences

Over the years, mathematics and statistics have become increasingly important in the social sciences1 . A look at history quickly confirms this claim. At the beginning of the 20th century most theories in the social sciences were formulated in qualitative terms while quantitative methods did not play a substantial role in their formulation and establishment. Moreover, many practitioners considered mathematical methods to be inappropriate and simply unsuited to foster our understanding of the social domain. Notably, the famous Methodenstreit also concerned the role of mathematics in the social sciences. Here, mathematics was considered to be the method of the natural sciences from which the social sciences had to be separated during the period of maturation of these disciplines. All this changed by the end of the century. By then, mathematical, and especially statistical, methods were standardly used, and their value in the social sciences became relatively uncontested. The use of mathematical and statistical methods is now ubiquitous: Almost all social sciences rely on statistical methods to analyze data and form hypotheses, and almost all of them use (to a greater or lesser extent) a range of mathematical methods to help us understand the social world. Additional indication for the increasing importance of mathematical and statistical methods in the social sciences is the formation of new subdisciplines, and the establishment of specialized journals and societies. Indeed, subdisciplines such as Mathematical Psychology and Mathematical Sociology emerged, and corresponding journals such as The Journal of Mathematical Psychology (since 1964), The Journal of Mathematical Sociology (since 1976), Mathematical Social Sciences (since 1980) as well as the online journals Journal of Artificial Societies and Social Simulation (since 1998) and Mathematical Anthropology and Cultural Theory (since 2000) were established. What is more, societies such as the Society for Mathematical Psychology (since 1976) and the Mathematical Sociology Section of the American Sociological Association (since 1996) were founded. Similar developments can be observed in other countries. The mathematization of economics set in somewhat earlier (Vazquez 1995; Weintraub 2002). However, the use of mathematical methods in economics started booming only in the second half of the last century (Debreu 1991). Contemporary economics is dominated by the mathematical approach, although a certain style of doing economics became more and more under attack in the last decade or so. Recent developments in behavioral economics and experimental economics can also be understood as a reaction against the dominance (and limitations) of an overly mathematical approach to economics. There are similar debates in other social sciences. It is, however, important to stress that problems of one method (such as axiomatization or the use of set theory) can hardly be taken as a sign of bankruptcy of mathematical methods in the social sciences tout court. This chapter surveys mathematical and statistical methods used in the social sciences and discusses some of the philosophical questions they raise. It is divided into two parts. Sections 1 and 2 are devoted to mathematical methods, and Sections 3 to 7 to statistical methods. As several other chapters in this handbook provide detailed accounts of various mathematical methods, our remarks about the latter will be rather short and general. Statistical methods, on the other hand, will be discussed in-depth

The decision-making entrepreneur; Literature review

This study provides a literature overview of the entrepreneurial decision-making process. The literature review is used as background information for a qualitative study, which investigates, by means of case studies, the decision-making process of small business enterpreneurs in The Netherlands (Gibcus and Van Hoesel, 2003). The literature overview is the starting point of a confrontation between the literature on decision-making and the empirical findings of the latter qualitive study. Firstly, this literature review gives an introduction to general decision theory. It discusses the classical rationality, the bounded rationality and the neoclassical rationality. The place of the entrepreneur in the general decision theory is also discussed. Next, an analytic framework of the strategic decision-making in SMEs is presented. The analytic framework consists of three elements: the entrepreneur, the environment and the strategic decision process. Each of these elements is critical. Finally, some earlier empirical findings on entrepreneurial strategic decision-making are discussed.

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Precision Physics at LEP

1 - Introduction 2 - Small-Angle Bhabha Scattering and the Luminosity Measurement 3 - Z^0 Physics 4 - Fits to Precision Data 5 - Physics at LEP2 6 - ConclusionsComment: Review paper to appear in the RIVISTA DEL NUOVO CIMENTO; 160 pages, LateX, 70 eps figures include