Andrea Califano – författare

Olivier Blanchard, former chief economist of the International Monetary Fund (IMF), is author of one of the most important standard macroeconomics textbooks, which is used throughout the world. Endorsed by Blanchard himself, Anti-Blanchard Macroeconomics critically analyzes prevailing economic theory and policy in comparison with alternative approaches. This thoroughly revised edition represents a field of research that has developed through intense theoretical debates, continual empirical testing and the resultant disputes about economic policy.Emiliano Brancaccio and Andrea Califano succinctly explore the relationship between theoretical models and economic policies, providing readers with examples and empirical exercises, and showing how the conclusions of different theories can be empirically tested. This updated second edition examines the links between the issues at the core of macroeconomic debate, including economic growth, economic crises, labour market reforms, government debt sustainability, the behaviour of central banks and the stock market, among many others.Key features: Contains an analysis of the economic policies, consequences and theories surrounding the Covid-19 pandemic Examines the connection between contemporary issues in world politics, economic theory and policymaking It overcomes the typical contradiction between the opportunity to offer students a preliminary mainstream education and the need to nurture rather than crush their critical spirit It helps students to understand that economics is not a discipline that changes in a smooth, linear manner but, on the contrary, represents a dynamic field of research that develops through intense theoretical debate and continual empirical testing, and is shaped by the resultant disputes concerning economic policyIncludes the typescript of a lively and informative debate between Emiliano Brancaccio and Olivier Blanchard, together with comparative economic policy examples.

Computational biologists are striving to "reverse engineer" the underlying networks of interactions between the molecules in the cell. This volume and the conference it reports on attempt a systematic evaluation of reverse engineering methods. The DREAM project brings together a diverse group of researchers to clarify potentials and limitations of the enterprise of reverse engineering cellular networks. An important aspiration of the project is to compare the effectiveness of different methods in reverse engineering biological networks. Evaluating this requires a "gold standard" network for which at least the true topology of connections is known. Many participants, especially the computational biologists, believe that synthetic networks are good candidates for this purpose because, at least for now, only they can be described with certainty. Experimental biologists, however, worry that unless the project addresses real biological networks, it could evolve into a mathematical exercise with little impact on biology. These and other ideas are discussed.NOTE: Annals volumes are available for sale as individual books or as a journal. For information on institutional journal subscriptions, please visit www.blackwellpublishing.com/nyas.ACADEMY MEMBERS: Please contact the New York Academy of Sciences directly to place your order (www.nyas.org). Members of the New York Academy of Science receive full-text access to the Annals online and discounts on print volumes. Please visit http://www.nyas.org/MemberCenter/Join.aspx for more information about becoming a member

At a microscopic level, organisms are ruled by interacting systems of biomolecules. Historically, scientists painstakingly elucidated chains of molecular events using experiments that reveal individual interactions, although they recognized that members of different pathways frequently interact. In recent years, researchers have built richer, interconnected networks to mathematically summarize their knowledge of these interactions. This systems biology enterprise, largely stimulated by high-throughput tools like microarrays that measure mRNA levels as an indicator of gene expression, is a vital and increasingly important activity in both basic biology and in medicine. A nagging concern, however, is how accurately these networks represent the biology. For complex systems like biological networks, there are practical limits on how well even massive amounts of data can uniquely define the underlying structure and yield useful predictions of measurable events. Indeed, although its advocates call this process "reverse engineering," the topology and the detailed molecular interactions of the "inferred" networks will likely never be known with precision.This volume captures the ongoing process to assess the ability of scientists—and their computer servants—to infer networks from experimental data, by comparing their predictions to "gold-standard" networks whose structure is thought to be known.NOTE: Annals volumes are available for sale as individual books or as a journal. For information on institutional journal subscriptions, please visit www.blackwellpublishing.com/nyas.ACADEMY MEMBERS: Please contact the New York Academy of Sciences directly to place your order (www.nyas.org). Members of the New York Academy of Science receive full-text access to the Annals online and discounts on print volumes. Please visit http://www.nyas.org/MemberCenter/Join.aspx for more information about becoming a member.