Industrial Policies vs Public Goods under Asymmetric Information

This paper presents an analytical framework that captures the informational problems and tradeoffs that policy makers face when choosing between public goods (e.g., infrastructure) and industrial policies (e.g., firm or sector-specific subsidies). The paper first provides a discussion of the literature on industrial policies. It then presents an illustrative model, where the economy consists of a set of firms that vary by productivity and a government that can support firms through general or targeted expenditures. The paper examines the cases of full and asymmetric information on firm productivity. Working under full information, it describes the first-best allocation of government resources among firms according to their productivity. It then introduces uncertainty by restricting information regarding firm productivity to be private to the firm. The paper develops an optimal contract (which replicates the first-best) consisting of a tax-based mechanism that induces firms to reveal their true productivity. As this requires high government capacity, the paper considers other simpler policies, one of which is the provision of public goods to all firms. The paper concludes that providing public goods is likely to dominate industrial policies under most scenarios, especially when government capacity is low

Scale-free Networks from Optimal Design

A large number of complex networks, both natural and artificial, share the presence of highly heterogeneous, scale-free degree distributions. A few mechanisms for the emergence of such patterns have been suggested, optimization not being one of them. In this letter we present the first evidence for the emergence of scaling (and smallworldness) in software architecture graphs from a well-defined local optimization process. Although the rules that define the strategies involved in software engineering should lead to a tree-like structure, the final net is scale-free, perhaps reflecting the presence of conflicting constraints unavoidable in a multidimensional optimization process. The consequences for other complex networks are outlined.Comment: 6 pages, 2 figures. Submitted to Europhysics Letters. Additional material is available at http://complex.upc.es/~sergi/software.ht

Interplay between surface chemistry and performance of rutile-type catalysts for halogen production

Catalytic HBr oxidation is an integral step in the bromine-mediated functionalisation of alkanes to valuable chemicals. This study establishes the relationships between the mechanism of HBr oxidation over rutile-type oxides (RuO2, IrO2, TiO2) and their apparent catalytic performance. Comparison with the well-studied HCl oxidation revealed distinct differences in surface chemistry between HBr and HCl oxidation that impact the stability and activity of the catalysts. The kinetic fingerprints of both oxidation reactions over the three rutile-type oxides investigated are compared using temporal analysis of products, which substantiates the energy profiles derived from density functional theory. The quantitative determination of the halogen uptake under operando conditions using prompt gamma activation analysis demonstrates that RuO2 suffers from extensive subsurface bromination upon contact with hydrogen bromide, particularly at low temperature and low O2 : HBr ratios, which negatively affects the stability of the catalyst. TiO2 exhibits intrinsically low halogen coverage (30–50%) under all the conditions investigated, due to its unique defect-driven mechanism that renders it active and stable for Br2 production. On the contrary, for HCl oxidation TiO2 is inactive, and the chlorination of the highly active RuO2 is limited to the surface. Differences in the extent of surface halogenation of the materials were also confirmed by high-resolution transmission electron microscopy and explained by the DFT calculations. These insights into the molecular-level processes taking place under working conditions pave the way for the design of the next generation catalysts for bromine production

Signatures of arithmetic simplicity in metabolic network architecture

Metabolic networks perform some of the most fundamental functions in living cells, including energy transduction and building block biosynthesis. While these are the best characterized networks in living systems, understanding their evolutionary history and complex wiring constitutes one of the most fascinating open questions in biology, intimately related to the enigma of life's origin itself. Is the evolution of metabolism subject to general principles, beyond the unpredictable accumulation of multiple historical accidents? Here we search for such principles by applying to an artificial chemical universe some of the methodologies developed for the study of genome scale models of cellular metabolism. In particular, we use metabolic flux constraint-based models to exhaustively search for artificial chemistry pathways that can optimally perform an array of elementary metabolic functions. Despite the simplicity of the model employed, we find that the ensuing pathways display a surprisingly rich set of properties, including the existence of autocatalytic cycles and hierarchical modules, the appearance of universally preferable metabolites and reactions, and a logarithmic trend of pathway length as a function of input/output molecule size. Some of these properties can be derived analytically, borrowing methods previously used in cryptography. In addition, by mapping biochemical networks onto a simplified carbon atom reaction backbone, we find that several of the properties predicted by the artificial chemistry model hold for real metabolic networks. These findings suggest that optimality principles and arithmetic simplicity might lie beneath some aspects of biochemical complexity