Lea Karbevska & César A. Hidalgo 
EPJ Data Science volume 14, Article number: 21 (2025)

Value chain data is crucial for navigating economic disruptions. Yet, despite its importance, we lack publicly available product-level value chain datasets, since resources such as the “World Input-Output Database”, “Inter-Country Input-Output Tables”, “EXIOBASE”, and “EORA”, lack information about products (e.g. Radio Receivers, Telephones, Electrical Capacitors, LCDs, etc.) and instead rely on aggregate industrial sectors (e.g. Electrical Equipment, Telecommunications). Here, we introduce a method that leverages ideas from machine learning and trade theory to infer product-level value chain relationships from fine-grained international trade data. We apply our method to data summarizing the exports and imports of 1200+ products and 250+ world regions (e.g. states in the U.S., prefectures in Japan, etc.) to infer value chain information implicit in their trade patterns. In short, we leverage the idea that due to global value chains, regions specialized in the export of a product will tend to specialize in the import of its inputs. We use this idea to develop a novel proportional allocation model to estimate product-level trade flows between regions and countries. This contributes a method to approximate value chain data at the product level that should be of interest to people working in logistics, trade, and sustainable development.

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Stephen Polasky, Marten Scheffer, and John M. Anderies

122 (14) e2320528122

A well-functioning society requires well-functioning institutions that ensure prosperity, fair distribution of wealth, social participation, security, and informative media. Such institutions are built on a foundation of trust. However, while trust is essential for economic success and good governance, interconnected mechanisms inherent in weakly governed market economies tend to undermine the very trust on which such success depends. These mechanisms include the intrinsic tendency for inequality to grow, media to boost perceived unfairness, and self-interest to gain rewards at the expense of others. These mechanisms, if left unchecked, allow wealth concentration to result in state capture where institutions facilitate further wealth concentration instead of the promoting the common good. As a result, people may become alienated and untrusting of fellow citizens and of institutions. Several democracies now experience such dynamics, the United States being a prime example. We discuss ways in which well-functioning democracies can design institutions to help avoid this social trap, and the much harder challenge of escaping the trap once in it. Successful cases such as the ability of Scandinavian democracies to maintain high-trust, and the US progressive era in the early 20th century provide instructive examples.

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Robert L. Axtell, J. Doyne Farmer
JOURNAL OF ECONOMIC LITERATURE VOL. 63, NO. 1, MARCH 2025 (pp. 197–287)

Agent-based modeling (ABM) is a novel computational methodology for representing the behavior of individuals in order to study social phenomena. Its use is rapidly growing in many fields. We review ABM in economics and finance and highlight how it can be used to relax conventional assumptions in standard economic models. ABM has enriched our understanding of markets, industrial organization, labor, macro, development, public policy, and environmental economics. In financial markets, substantial accomplishments include understanding clustered volatility, market impact, systemic risk, and housing markets. We present a vision for how ABMs might be used in the future to build more realistic models of the economy and review some of the hurdles that must be overcome to achieve this.

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Andrea Gabrielli, Diego Garlaschelli, Subodh P. Patil & M. Ángeles Serrano 

Nature Reviews Physics (2025)

The renormalization group (RG) is a powerful theoretical framework. It is used on systems with many degrees of freedom to transform the description of their configurations, along with the associated model parameters and coupling constants, across different levels of resolution. The RG also provides a way to identify critical points of phase transitions and study the system’s behaviour around them. In traditional physical applications, the RG largely builds on the notions of homogeneity, symmetry, geometry and locality to define metric distances, scale transformations and self-similar coarse-graining schemes. More recently, efforts have been made to extend RG concepts to complex networks. However, in such systems, explicit geometric coordinates do not necessarily exist, different nodes and subgraphs can have different statistical properties, and homogeneous lattice-like symmetries are absent — all features that make it complicated to define consistent renormalization procedures. In this Technical Review, we discuss the main approaches, important advances, and the remaining open challenges for network renormalization.

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