Criteria for recognizing and rewarding scientists primarily focus on individual contributions. This creates a conflict between what is best for scientists’ careers and what is best for science. In this article, we show how the theory of multilevel selection provides conceptual tools for modifying incentives to better align individual and collective interests. A core principle is the need to account for indirect effects by shifting the level at which selection operates from individuals to the groups in which individuals are embedded. This principle is used in several fields to improve collective outcomes, including animal husbandry, team sports, and professional organizations. Shifting the level of selection has the potential to ameliorate several problems in contemporary science, including accounting for scientists’ diverse contributions to knowledge generation, reducing individual-level competition, and promoting specialization and team science. We discuss the difficulties associated with shifting the level of selection and outline directions for future development in this domain.
Outcomes Graph: A protocol for applied science coordination - DeepScience Ventures
In this article, we explain how our Outcomes Graph works, including how the functions of scientific knowledge shape the features we've developed and how we're using it to harness the collective intelligence of venture scientists.
A Vision of Metascience
How does the culture of science change and improve? Many people have identified shortcomings in core social processes of science, such as peer review, how grants are awarded, how people are selected to become scientists, and so on. Yet despite often compelling criticisms, strong barriers inhibit widespread change in such social processes. The result is near stasis, and apathy about the prospects for improvement. People sometimes start new research institutions intended to do things differently; unfortunately such institutions are often changed more by the existing ecosystem than they change it. In this essay we sketch a vision of how the social processes of science may be rapidly improved. In this vision, metascience plays a key role: it deepens our understanding of which social processes best support discovery; that understanding can then help drive change. We introduce the notion of a metascience entrepreneur, a person seeking to achieve a scalable improvement in the social processes of science. We argue that: (1) metascience is an imaginative design practice, exploring an enormous design space for social processes; (2) that exploration aims to find new social processes which unlock latent potential for discovery; (3) decentralized change must be possible, so outsiders with superior ideas can't be blocked by established power centers; (4) ideally, change would align with what is best for science and for humanity, not merely what is fashionable, politically popular, or media-friendly; (5) the net result would be a far more structurally diverse set of environments for doing science; and (6) this would enable crucial types of work difficult or impossible within existing environments. For this vision to succeed metascience must develop and intertwine three elements: an imaginative design practice, an entrepreneurial discipline, and a research field. Overall, it is a vision in which metascience is an engine of improvement for the social processes and ultimately the culture of science.
Artificial intelligence tools expand scientists’ impact but contract science’s focus
Nature - Artificial intelligence boosts individual scientists’ output, citations and career progression, but collectively narrows research diversity and reduces collaboration, concentrating...
Research topic choice: Motivations, strategies, and consequences
Abstract. Scientists’ choices of what research topics to pursue are highly consequential and have been the subject of many studies. However, these studies are dispersed across several fields and literatures. This paper provides a review of this body of work. It first reviews theory from economics and sociology to explain how topic choice fits into scientists’ broader competitive strategy, and how the social valuation of research topics reproduces inequalities in science. Then it examines empirical literature on how research topics are chosen in practice, first looking at observational accounts derived from large-scale secondary data sources, then looking at self-reported accounts elicited by surveys and interviews of researchers. Finally, it concludes with a synthesis of the theoretical and empirical literature and identifies research gaps. Themes in these literatures include the rewards associated with certain topics, demographic differences in topic choices, and whether choices are broadly reward maximizing or driven by social context, identities, and path dependence. We also identify several research gaps, including the types and impacts of costs impeding topic entry and switching, the causal mechanisms associating topics with rewards, and the discrepancy between scientists’ self-reported topic motivations and observed behavior.
From Grants to Portfolios: Index Logic for Science
How science can learn from capital markets to coordinate diversity, measure progress, and turn discovery into a public portfolio.
Involuntary collaboration: a strategy for decentralized science
How your best co-worker, might be someone you’ll never meet
How and When to Involve Crowds in Scientific Research - The Book
This book explores how millions of people can significantly contribute to scientific research. Discover main elements, authors, and who this book is for and why.
The Peer Review Crisis Demands Radical Reform: Why Reviewers Should Become Paid Professional Referees
The peer review system, fundamental to scientific quality control, faces a significant crisis. As journal editors, we often need to send up to 35 invitations just to secure two reviewers, confronting daily the collapse of voluntary participation. This reflects a critical imbalance: while publication pressure intensifies, willingness to evaluate diminishes, creating "literature elephantiasis", i.e., an overwhelming proliferation of papers exceeding human processing capacity. Current compensation models, relying on token recognition and database access, fail to incentivize quality engagement and may encourage ethically problematic practices like excessive self-citation. The unchecked infiltration of artificial intelligence into peer review, with minimal enforcement, further undermines system integrity. We propose transforming peer reviewers into professional referees, modeled on sports officiating. This radical solution involves formal training and certification for reviewers, equipping them to assess scientific merit, methodology, and ethics comprehensively. Like sports referees supported by assistants, scientific referees would collaborate with specialists - including statisticians, methodology experts, and reference checkers - ensuring thorough evaluation while distributing workload effectively. Funding would come from publishers or research funders, recognizing peer review as an essential, compensated component of the research lifecycle. Implementation faces challenges including publisher resistance and funding allocation, which we address through phased transition strategies. This professionalization addresses current inequities where conscientious scientists shoulder disproportionate reviewing burdens while others contribute minimally. Professional reviewers would view evaluation as valued career development rather than unwelcome obligation. Critics citing independence concerns overlook the sports analogy: referees maintain impartiality through professional standards despite league compensation. Quality scientific evaluation requires dedicated expertise, adequate training, and fair remuneration. Science deserves better than a system dependent on goodwill and guilt - it needs professional referees now.
More Versus Better: Artificial Intelligence, Incentives, and the Emerging Crisis in Peer Review | Organization Science
As the AI Task Force for Organization Science, we provide an early account of artificial intelligence’s (AI) impact on both submissions and reviews at a major academic journal. Submission volume ha...
Science Communication as a Collective Intelligence Endeavor: A Manifesto and Examples for Implementation
Effective science communication is challenging when scientific messages are informed by a continually updating evidence base and must often compete against misinformation. We argue that we need a new program of science communication as collective intelligence—a collaborative approach, supported by technology. This would have four key advantages over the typical model where scientists communicate as individuals: scientific messages would be informed by (a) a wider base of aggregated knowledge, (b) contributions from a diverse scientific community, (c) participatory input from stakeholders, and (d) better responsiveness to ongoing changes in the state of knowledge.
What could a human right to participate in science be?
Abstract. At first sight, the idea of a human right to participate in science may seem absurd. Many assume science must be the preserve only of those with
Why do people trust physicists and biologists more than economists or sociologists? In a new paper out in Public Understanding of Science, @hugoreasoning.bsky.social and I argue that it has to do with perceived precision and consensus. doi.org/10.1177/09636625261471051
Sciences perceived as precise and consensual are more trusted - Jan Pfänder, Hugo Mercier, 2026
doi.org"what if engineering a scientific revolution is, at least in part, a problem of organizing humans and coordinating effort?" uniconq.substack.com/p/the-ground-truth-institute h/t @ranganaut.bsky.social
The Ground Truth Institute
uniconq.substack.com
