César A. Hidalgo, Viktor Stojkoski

Economic complexity estimates rely on eigenvectors derived from matrices of specialization to explain differences in economic growth, inequality, and sustainability. Yet, despite their widespread use, we still lack a principled theory that can deduce these eigenvectors from first principles and place them in the context of a mechanistic model. Here, we calculate these eigenvectors analytically for a model where the output of an economy in an activity increases with the probability the economy is endowed with the factors required by the activity. We show that the eigenvector known as the Economic Complexity Index or ECI is a monotonic function of the probability that an economy is endowed with a factor, and that in a multi-factor model, it is an estimate of the average endowment across all factors. We then generalize this result to other production functions and to a short-run equilibrium framework with prices, wages, and consumption. We find that our main result does not depend on the introduction of prices or wages, and that the derived wage function is consistent with the convergence of economies with a similar level of complexity. Finally, we use this model to explain the shape of networks of related activities, such as the product space and the research space. These findings solve long standing theoretical puzzles in the economic complexity literature and validate the idea that metrics of economic complexity are estimates of an economy being endowed with multiple factors.

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Sachit Mahajan, Dirk Helbing

This paper critically examines the growing use of big data algorithms and AI in science, society, and public policy. While these tools are often introduced with the goal of increasing efficiency, the results do not always lead to greater empowerment or fairness for individuals or communities. Persistent issues such as bias, measurement error, and over-reliance on prediction can undermine success and produce outcomes that are neither fair nor transparent, especially when automated decisions replace human judgment. Beyond technical limitations, the widespread use of data-driven methods also shapes the distribution of power, influences public trust, and raises questions about the health of techno-socioeconomic institutions. We argue that the pursuit of optimality cannot succeed without careful evaluation of ethical risks and societal side effects. Responsible innovation demands open standards, ongoing scrutiny, and a focus on human values alongside technical performance. Our goal is to encourage a more balanced approach to big data-one that recognizes both its potentials and its limits, and one that aims for genuine social benefits rather than just efficiency alone.

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Binglu Wang, Zhengnan Ma, Dashun Wang, Brian Uzzi

This study examines a fundamental yet overlooked function of peer review: its role in exposing reviewers to new and unexpected ideas. Leveraging a natural experiment involving over half a million peer review invitations covering both accepted and rejected manuscripts, and integrating high-scale bibliographic and editorial records for 37,279 submitting authors, we find that exposure to a manuscript’s core ideas significantly influences the future referencing behavior and knowledge of reviewer invitees who decline the review invite. Specifically, declining reviewer invitees who could view concise summaries of the manuscript’s core ideas not only increase their citations to the manuscript itself but also demonstrate expanded breadth, depth, diversity, and prominence of citations to the submitting author’s broader body of work. Overall, these results suggest peer review substantially influences the spread of scientific knowledge. Ironically, while the massive scale of peer review, entailing millions of reviews annually, often drives policy debates about its costs and burdens, our findings demonstrate that precisely because of this scale, peer review serves as a powerful yet previously unrecognized engine for idea diffusion, which is central to scientific advances and scholarly communication.

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Shuowen Li, Kexin Wang, Minglu Fang, Danqi Huang, Ali Asadipour, Haipeng Mi, Yitong Sun

We present a semantic feedback framework that enables natural language to guide the evolution of artificial life systems. Integrating a prompt-to-parameter encoder, a CMA-ES optimizer, and CLIP-based evaluation, the system allows user intent to modulate both visual outcomes and underlying behavioral rules. Implemented in an interactive ecosystem simulation, the framework supports prompt refinement, multi-agent interaction, and emergent rule synthesis. User studies show improved semantic alignment over manual tuning and demonstrate the system’s potential as a platform for participatory generative design and open-ended evolution.

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