Sean Kelty, Raiyan Abdul Baten, Adiba Mahbub Proma, Ehsan Hoque, Johan Bollen & Gourab Ghoshal
Humanities and Social Sciences Communications volume 12, Article number: 926 (2025)

Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these “superstars” foster leadership in scientific innovation? We employ a series of information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the academic output of scientists in the American Physical Society corpus with varying levels of connections to superstar scientists. The strength of connection is based on the frequency of citations to superstar papers, which is also related to the frequency of collaboration. We find that while strongly-connected scientists publish more, garner more citations, and produce moderately more diverse content, this comes at a cost of lower innovation, less disruption, and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these strongly connected scientists greatly diminishes. In contrast, authors who publish at the same rate without the benefit of collaborations with scientific superstars produce papers that are more innovative, more disruptive, and have comparable citation rates, once one controls for the transferred prestige of superstars. On balance, our results indicate that academia pays a price by focusing attention and resources on superstars.

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Sean P. Maley, Carlos Gershenson, Stuart A. Kauffman

There has been a long debate on how new levels of organization have evolved. It might seem unlikely, as cooperation must prevail over competition. One well-studied example is the emergence of autocatalytic sets, which seem to be a prerequisite for the evolution of life. Using a simple model, we investigate how varying bias toward cooperation versus antagonism shapes network dynamics, revealing that higher-order organization emerges even amid pervasive antagonistic interactions. In general, we observe that a quantitative increase in the number of elements in a system leads to a qualitative transition.
We present a random threshold-directed network model that integrates node-specific traits with dynamic edge formation and node removal, simulating arbitrary levels of cooperation and competition. In our framework, intrinsic node values determine directed links through various threshold rules. Our model generates a multi-digraph with signed edges (reflecting support/antagonism, labeled “help”/“harm”), which ultimately yields two parallel yet interdependent threshold graphs. Incorporating temporal growth and node turnover in our approach allows exploration of the evolution, adaptation, and potential collapse of communities and reveals phase transitions in both connectivity and resilience.
Our findings extend classical random threshold and Erdős-Rényi models, offering new insights into adaptive systems in biological and economic contexts, with emphasis on the application to Collective Affordance Sets. This framework should also be useful for making predictions that will be tested by ongoing experiments of microbial communities in soil.

Read the full article at: arxiv.org

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Zdzislaw Burda, Mario Kieburg

Phys. Rev. E 112, 014114

We study the dynamical aspects of the top rank statistics of particles, performing Brownian motions on a half-line, which are ranked by their distance from the origin. For this purpose, we introduce an observable Ω⁡(𝑡) which we call the overlap ratio. The average overlap ratio is equal to the probability that a particle that is on the top-𝑛 list at some time will also be on the top-𝑛 list after time 𝑡. The overlap ratio is a local observable which is concentrated at the top of the ranking and does not require the full ranking of all particles. In practice, the overlap ratio is easy to measure. We derive an analytical formula for the average overlap ratio for a system of 𝑁 particles in the stationary state that undergo independent Brownian motion on the positive real half-axis with a reflecting wall at the origin and a drift towards the wall. In particular, we show that for 𝑁→∞, the overlap ratio takes a rather simple form ⟨Ω⁡(𝑡)⟩=erfc⁡(𝑎⁢√𝑡) for 𝑛≫1 with some scaling parameter 𝑎>0. This result is a very good approximation even for moderate sizes of the top-𝑛 list such as 𝑛=10. Moreover, we observe in numerical studies that the overlap ratio exhibits universal behavior in many dynamical systems including geometric Brownian motion, Brownian motion with asymptotically linear drift, the Bouchaud-Mézard wealth distribution model, and Kesten processes. We conjecture the universality to hold for a broad class of one-dimensional stochastic processes.

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W. Barfuss, J. Flack, C.S. Gokhale, L. Hammond, C. Hilbe, E. Hughes, J.Z. Leibo, T. Lenaerts, N. Leonard, S. Levin, U. Madhushani Sehwag, A. McAvoy, J.M. Meylahn, & F.P. Santos

PNAS 122 (25) e2319948121

Cooperation at scale is critical for achieving a sustainable future for humanity. However, achieving collective, cooperative behavior—in which intelligent actors in complex environments jointly improve their well-being—remains poorly understood. Complex systems science (CSS) provides a rich understanding of collective phenomena, the evolution of cooperation, and the institutions that can sustain both. Yet, much of the theory in this area fails to fully consider individual-level complexity and environmental context—largely for the sake of tractability and because it has not been clear how to do so rigorously. These elements are well captured in multiagent reinforcement learning (MARL), which has recently put focus on cooperative (artificial) intelligence. However, typical MARL simulations can be computationally expensive and challenging to interpret. In this perspective, we propose that bridging CSS and MARL affords new directions forward. Both fields can complement each other in their goals, methods, and scope. MARL offers CSS concrete ways to formalize cognitive processes in dynamic environments. CSS offers MARL improved qualitative insight into emergent collective phenomena. We see this approach as providing the necessary foundations for a proper science of collective, cooperative intelligence. We highlight work that is already heading in this direction and discuss concrete steps for future research.

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